Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
sequencelengths 1
91
| keywords
sequencelengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
Poly ( ADP-ribose ) polymerase 1 ( PARP1 ) , a nuclear protein , utilizes NAD to synthesize poly ( AD-Pribose ) ( pADPr ) , resulting in both automodification and the modification of acceptor proteins . Substantial amounts of PARP1 and pADPr ( up to 50% ) are localized to the nucleolus , a subnuclear organelle known as a region for ribosome biogenesis and maturation . At present , the functional significance of PARP1 protein inside the nucleolus remains unclear . Using PARP1 mutants , we investigated the function of PARP1 , pADPr , and PARP1-interacting proteins in the maintenance of nucleolus structure and functions . Our analysis shows that disruption of PARP1 enzymatic activity caused nucleolar disintegration and aberrant localization of nucleolar-specific proteins . Additionally , PARP1 mutants have increased accumulation of rRNA intermediates and a decrease in ribosome levels . Together , our data suggests that PARP1 enzymatic activity is required for targeting nucleolar proteins to the proximity of precursor rRNA; hence , PARP1 controls precursor rRNA processing , post-transcriptional modification , and pre-ribosome assembly . Based on these findings , we propose a model that explains how PARP1 activity impacts nucleolar functions and , consequently , ribosomal biogenesis .
The nuclear substructure , nucleolus , is a site commonly associated with translational complex assembly , and thus functions as a major regulator of cell growth [1] . The nucleolus is composed of an array of tandem repeated units of ribosomal RNA ( rRNA ) genes , some of which are transcribed , while others remain in an inactive heterochromatic state [2]–[4] . Additionally , the nucleolus contains a diverse pool of proteins , most of which are involved primarily with transcription , processing , and modification of rRNA transcripts , ribosome assembly , and transport of translational competent ribosome to the cytoplasm [1] , [5] . Actively growing yeast cells produce about 2000 ribosomes per minute , underscoring the amount of metabolic investment made by a cell during growth towards ribosome production [6] . Ample data also suggest that the regulation of rRNA synthesis and production of ribosomes can influence cancer progression [7] . However , despite the advances in nucleolar research , the sequence of molecular events that coordinates ribosomal biogenesis with cell growth , especially in highly proliferative cells , such as cancer cells , is poorly understood . PARP1 protein , utilizes NAD as a substrate to generate poly ( ADP-ribose ) ( pADPr ) for automodification and the modification of acceptor proteins , such as chromatin-associated histone proteins [8]–[12] . Glutamate residues of acceptor proteins serve as sites for poly ( ADP-ribose ) attachment [13] . Modification of proteins by PARP1 alters their localization in the cell and modifies their biological activities [14]–[17] . Since automodification disrupts the physiological activity of PARP1 , it is necessary to counteract the addition of ADPr polymers . Thus , to maintain active PARP1 protein levels , ADPr polymers are removed and subsequently metabolized by PARG [18]–[21] . PARG knockout results in the accumulation of automodified PARP1 , which is rendered incapable of re-associating with DNA or further catalyzing ADPr [20] , [22] . Drosophila nucleoli contain large quantities of PARP1 and pADPr , and display considerable amounts of PARP1 activity [23] , [24] . Whereas nucleoli structure disintegrates completely in Parp1 mutants , the ectopic expression of PARP1 cDNA restores proper assembly of nucleolar components and structure [23] . Although PARP1 does not contain any known nucleolar localization signal , it has been proposed that PARP1 localization in the nucleolus appears to depend on nucleolar activity because a large amount of PARP1 translocates from the nucleolus when ribosomal DNA ( rDNA ) transcription is inhibited [25] , [26] . Nucleolar components , such as Fibrillarin [20] , Nucleolin , and Nucleoplasmin/B23 [26] , [27] , interact and colocalize with PARP1 in the nucleus and undergo modification by pADPr [28] . In addition , a number of ribosomal proteins have been shown to interact with PARP1 protein [29] , [30] . Both the nucleolar localization and interaction with nucleolar proteins suggest that PARP1 may function in regulating some aspect of nucleolar activity . Here we evaluate the roles of PARP1 , ADPr , and nucleolar proteins that interact with PARP1 to determine the impact of PARP1 in regulating nucleolar structure and functions .
We previously reported that Drosophila PARP1 is broadly distributed on chromosome and is enriched in active chromatin [24] . In all tissues of wild-type Drosophila , nucleoli contained large quantities of PARP1 and pADPr , and display considerable amounts of PARP1 activity [23] . We determined that more than 40% of nuclear PARP1 ( Figure 1A and 1B ) and pADPr ( Figure S1 ) are localized within the nucleolus . Since PARP1 activity within the nucleolus is not clearly defined , we investigated how its enzymatic activity affects nucleolar architecture and functions . Here we show that depletion of PARP1 protein in ParpCH1 mutant results in mis-localization of nucleolar specific proteins in all Drosophila tissues analyzed ( Figure 1C and 1D ) . Although a large portion of nucleolar proteins shifted their localization to the cytoplasm , the total amount of these proteins did not change ( Figure S2 ) . This finding suggests that PARP1 may function in part to control nucleolar structural integrity by maintaining proteins within this subnuclear structure . To address this , we analyzed the localization of nucleolar-specific proteins under different PARP1 genetic backgrounds . We used Fibrillarin , a nucleolar protein involved in rRNA processing and maturation , as a marker for nucleolar integrity [31] . Consistent with our finding regarding ParpCH1 mutants [23] , disruption of PARP1 activity by expressing ParpRNAi ( Figure S3 ) , using hypomorphic mutants ParpC03256 [32] , or overproducing the antagonist of PARP1 , PARG protein , caused nucleolar fragmentation , as detected by anti-Fibrillarin antibody ( Figure 1E–1H ) . Whereas these additional nucleolus-like structures do not contain rDNA ( Figure 1I–1K ) , they cannot take part in the ordered process of ribosomal biogenesis . Our data shows that PARP1 protein is required for nucleolar structural integrity and compromising the enzymatic activity of the protein always leads to nucleolar disruption . This finding suggests that the product of PARP1 enzymatic reaction , poly ( ADP-ribose ) ( pADPr ) , may be an important component of the nucleolus and that it may serve as a matrix for nucleolar protein binding , keeping them together in proximity to precursor rRNA . To substantiate the role of pADPr in nucleolar structure and localization of nucleolar- specific proteins , we depleted Parp1 using RNAi expression ( Figure S3 ) and analyzed the resulting salivary gland for localization of three nucleolar-specific markers: Fibrillarin , AJ1 , and nucleolar GFP-exon trap marker [33] , CC01311 ( Figure 2 ) . In wild-type larvae , Fibrillarin , AJ1 , and CC01311 localized in an intact single nucleolar structure ( Figure 2A and 2B ) . However , RNAi depletion of Parp1 caused nucleolar-specific proteins to be localized completely independent from each other , unlike in wild-type tissue ( Figure 2C and 2D ) . The observation that depletion of PARP1 protein affected the localization of nucleolar proteins caused us to determine whether PARP1 enzymatic activity , and not PARP1 protein itself , is essential for nucleolar integrity . We inhibited the activity of PARP1 by culturing third-instar larvae in the presence of the NAD analogue , 3 aminobenzamide ( 3AB ) . Upon inhibition of PARP1 activity by 3AB , we observed the disintegration of nucleoli , as indicated by fragmented Fibrillarin ( Figure 2E and 2F ) . A prolonged treatment of Drosophila tissues with PARP1 inhibitor exacerbates nucleolus fragmentation but does not affect localization of PARP1 protein within these nucleolus-like fragments ( Figure 2G ) . Together , these results support the hypothesis that PARP1 enzymatic activity and pADPr are required for maintaining nucleolar structure . To further evaluate nucleoli structural integrity , we used Transmission Electron Microscopy ( TEM ) analysis to detect any nucleolar abnormalities associated with disrupting PARP1 activity . TEM revealed significant changes in nucleoli in both wild-type and Parg27 . 1 mutant nuclei ( Figure 3A and 3B ) . Wild-type nucleoli are typically located close to the middle part of nuclei and appear to be almost homogenic ( Figure 3A ) . In contrast , nucleoli of Parg27 . 1 mutants contain heavily condensed areas positioned close to nuclear lamina ( Figure 3B ) . Such structural changes may impact nucleolar functions and affect the steady state equilibrium of ribosomal rRNA processing . Therefore , we tested localization of nucleolar proteins reported to participate in rRNA processing and maturation in wild-type and Parg27 . 1 mutant nuclei using immunostaining . In addition to Fibrillarin , we evaluated colocalization using Nucleolin and dNop5 , both of which have been shown to be involved in nucleolar rRNA processing and ribosomal maturation [34] , [35] . Additionally , we used Casein Kinase II α ( CKIIα as a nucleolar marker , since previous reports have shown that it participates in rDNA transcription by phosphorylating components of RNA polymerase I [36] . Our results from this analysis shows that , in wild-type nucleoli , all tested nucleolar proteins demonstrate similar homogenic staining and co-localization with PARP1 protein ( Figure 3C , only Fibrillarin protein is included ) . In contrast , nucleolar proteins in Parg27 . 1 nuclei accumulate in two distinct locations . This suggests that , their exclusive localization within the nucleolus requires the activity of a functional PARP1 protein . Previously , we demonstrated that in Parg27 . 1 mutants , most of PARP1 protein is automodified , and , together with all other poly ( ADP-ribosyl ) ated proteins and pADPr binding proteins , it is either targeted to Cajal Bodies ( CBs ) or arrested inside abnormal condensed nucleolar substructures [20] , [32] . Based on this observation , we proposed that pADPr modification would lead to co-localization with automodified PARP1 in CBs . One group of proteins , Fibrillarin and Nucleolin , co-localized with PARP1 inside condensed blocks of the nucleolus and in CBs ( Figure 3D and 3E ) . However , another set of proteins , including nucleolar markers CC01311 , dNop5 and CKIIα were preferentially antagonistic to PARP1 localization and did not accumulate in CBs ( Figure 3F and 3G; Figure S4 ) . These observations suggested a direct interaction of the first group of proteins with automodified PARP1 . This interaction likely affects the location of these proteins in the nucleolus and thus may impact nucleolar activity . In contrast , CC01311 , dNop5 and CKIIα do not interact with automodified PARP1 . As a result , their positioning in the nucleolus is determined by PARP1-independent mechanisms . The existence of a selected group of nucleolar proteins requiring PARP1 enzymatic activity for their localization suggested to us that PARP1 may function in the nucleolus by binding these proteins through pADPr attachment . To further test our hypothesis that a specific sub-group of nucleolar proteins preferentially interacts with pADPr , we performed co-immunoprecipitation experiments ( Co-IP ) using anti-pADPr antibody . Indeed , we found that Fibrillarin , AJ1 , Nucleolin , and Nucleophosmin co-precipitate with pADPr in wild-type animals and Parg27 . 1 mutants ( Figure 4A and 4B ) , while other nucleolar proteins , including CC01311 , dNop5 and CKIIα , do not show interaction with pADPr in wild-type nuclei ( Figure 4A ) . Interestingly , a slight interaction of CC01311 protein with pADPr was detected in Parg27 . 1 mutant ( Figure 4B ) , although this weak interaction is likely indirect and may be mediated by other nucleolar components . This result suggests that by binding a specific set of proteins to pADPr , PARP1 may determine the order of steps that occur during the process of ribosome biogenesis . Therefore , we further tested if mutating PARP1 or PARG affects any specific steps involved in ribosome production . Resident nucleolar proteins , such as Fibrillarin , have critical roles in rRNA processing , modification , and maturation [31] , [37]–[39] . Our observation of fragmented localization of nucleolar-specific proteins , some of which are critical for rRNA maturation , prompted us to investigate the effect of inhibiting PARP1 activity on rRNA processing and maturation . The transcription of rDNA produces a precursor molecule which is subjected to multiple rounds of exonucleolytic and endonucleolytic cleavages by resident nucleolar protein complexes to generate mature 18S , 5 . 8S , and 28S rRNA final products [40] , [41] . We reasoned that the displacement of nucleolar proteins involved in rRNA processing will have a disruptive effect on the generation of mature rRNA products . To evaluate our hypothesis , we isolated RNA from both wild-type and mutant larvae and analyzed precursor rRNA intermediates using Northern blot . Surprisingly , we found no quantifiable difference between mature rRNA amounts in wild-type , Parg27 . 1 , and ParpC03256 mutants ( Figure 5 , 18S and 28S ) . Instead , a significant increase in rRNA intermediates was detected in Parg27 . 1 and ParpC03256 mutants ( Figure 5 , Figure S5 ) . When taken together with the observations reported above , this suggest that displacement of key components of ribosomal biogenesis , which occurs in Parg27 . 1 and ParpC03256 mutants , results in rRNA processing delays and over-accumulation of immature intermediates . In contrast , the initial 47S rRNA precursor product and the intermediate 36S product were effectively processed in Parg27 . 1 and ParpC03256 heterozygotes , and accumulation of these two precursor products was observed in larvae lacking PARP1 or PARG function ( Figure 5 , red asterisks ) . The last finding indicates that mutating pADPr pathway does not affect the rate of rDNA transcription , but blocks specific steps of precursor rRNA maturation and accumulation of immature ribosomes . To test this hypothesis , we then proceeded to compare ribosomal content of wild-type , Parp1 , and Parg mutants . Our observation of rRNA intermediates processing defects in Parg27 . 1 and ParpC03256 larvae caused us to investigate if any changes occur in the assembly of ribosomal subunit in these mutants . To assemble competent translational machinery , mature rRNA products form complexes with accessory proteins to form the small ( 40S ) and large ( 60S ) subunit particles [1] , [5] . These particles are released into the nucleoplasm for further maturation and then exported into the cytoplasm where they become part of the translational machinery [1] . The activity and effectiveness of the translational machinery often can be monitored by presence of multiribosomal complexes on mRNAs , called polysomes [42] . To examine the role of poly ( ADP-ribosyl ) ation in ribosomal production , we first compared the concentration of ribosomal particles in wild-type and Parp null mutant second-instar larvae using TEM analysis . We dissected larval midintestine from the same developmental stages of control and mutant animals , prepared ultra-thin sections from the same areas of midintestines ( Figure 1C and 1D ) , and subjected these samples to EM analysis . Concentration of ribosomal particles was quantified using at least 5 sections for each sample . Although no difference in ribosome concentration was detected ( compare Figure 6A and 6C and Figure 6B and 6D ) , we found a significant difference in total volume of cytoplasm between Parp null mutants and the wild-type . Cells of midintestine walls in Parp null mutant were , on average , twice as small as compared to those of the wild-type ( Figure 6A and 6B , Figure S6 ) such phenotypes are typical of mutations within ribosome biogenesis pathways [6] , [7] . We next compared ribosomal-polysomal profiles of wild-type and mutants within the pADPr turnover pathway using sucrose density gradient separation [6] . ParpCH1 mutants arrest early in development and show a phenotype similar to small second-instar larvae which limited their use in sucrose density analysis . To analyze the effect of disrupting PARP1 activity on ribosomal assembly , we used Parg27 . 1 and ParpC03256 mutants that survive up to late pupae . Both mutants demonstrated an absence of polysomes and abnormal quantities of mature ribosomal subunits 40S , 60S , and mono-ribosomes 80S ( Figure 6E and 6F ) . Moreover , both mutants show a marked decline in mRNA quantity within polysomal fractions ( Figure 6G ) , suggesting problems with mRNAs translation . Taken together with the accumulation of uncleaved rRNA intermediates ( Figure 5B ) , these last findings suggest the presence of a significant number of aberrant misfolded and unprocessed ribosomes in the cytoplasm of Parg27 . 1 and ParpC03256 cells .
Although substantial amount of PARP1 localizes in the nucleolus , prior to our study , very little was known about the function of this important protein in the nucleolus of Drosophila . We demonstrate that PARP1 activity is essential for the maintenance of Drosophila nucleolar structure and function , particularly for ribosome biogenesis . A number of nucleolar factors including Fibrillarin , AJ1 , and CC01311 that co-localize in wild-type nucleolus , were observed to localize completely independent from one another when PARP1 function was disrupted . This suggests that the product of PARP1 enzymatic reaction , pADPr , may serve as a matrix for binding these nucleolar proteins and keeping them together in proximity to precursor rRNA . Our experiments with mutated PARP1 antagonist , PARG , identified a selected group of nucleolar proteins , including Fibrillarin , AJ1 , Nucleolin , and Nucleophosmin , which were targeted to a specific location inside the nucleolus by PARP1 enzymatic reaction , apparently by binding of these proteins through attachment to pADPr matrix . Interestingly , although we observed a dramatic accumulation of 47S and 36S rRNA transcripts in the absence of a functional PARP1 activity , the level of 18S product was similar in both PARP1 wild-type and mutants . The accumulation of 47S and 36S rRNA transcripts can be attributed to either the upregulation of transcriptional activity in PARP1 mutants or defect in rRNA processing machinery . However , based on the dislocation of nucleolar proteins required for rRNA processing in PARP1 mutants , we believe that this accumulation is likely caused by the absence of a functional rRNA processing complex in PARP1 mutants . Furthermore , inhibiting PARP1 activity also lead to a significant reduction in the levels of ribosomes , suggesting that PARP1 activity is required for ribosome biogenesis . Taken together our findings suggest that by binding a specific set of nucleolar factors to pADPr , PARP1 likely determines the order of steps that occur during the process of ribosome biogenesis in the nucleolus . The nucleolus is a site where the protein synthesizing machinery , the translational complex , is assembled . By virtue of this property , the nucleolus functions as a major regulator of cell growth in normal and cancer cells [43] . In addition to the proteins that make up the translational complex , the nucleolus also contains an array of proteins that function in cell cycle regulation , cell growth , and cell death induction upon exposure to DNA damaging agents [44] , [45] . Findings reported here indicate that PARP1 activity is critical for nucleolar integrity and function . Recently published work by Guerrero and Maggert , support our findings that PARP1 activity is essential for the maintenance of nucleolar structure [46] . This results together with our data [23] highlights a role for PARP1 in nucleolar structure and maintenance . The research reported here extends beyond these analyses by examining PARP1 activity on the colocalization of nucleolar proteins , rRNA processing , and ribosome biogenesis . Since nucleolar function is essential during growth , this study suggests that PARP1 activity may play a central role in coordinating cell growth at the metabolic level . Here , we report our exciting observations that a novel PARP1 activity controls localization of critical components of ribosomal biogenesis within the nucleoli and therefore PARP1 is a critical regulator of ribosome production . Transcription of ribosomal DNA in nucleoli is performed specifically by the polymerase I machinery [47] . Although this transcriptional apparatus is very different from Pol II , the presence of poly ( ADP-ribose ) in nucleoli suggests that transcriptional start by Pol I involves PARP1 activation as it occurs with Pol II-dependent transcription [24] , [48] , [49] . By summarizing our data , we could propose that upon activation of rRNA synthesis , simultaneous activation of PARP1 leads to synthesis of an equal amount of pADPr , which “attracts” proteins required for rRNA processing , modification , and loading of an initial set of ribosomal proteins . PARP1 then coordinates the steps of ribosomal maturation and protects immature ribosomes from interacting with other groups of proteins that should be loaded last ( Figure 7 , Figures S7 and S8 ) . To produce poly ( ADP-ribose ) and regulate production of ribosomes , PARP1 utilizes a pool of NAD which is linked to energy status of the cells . Therefore , our proposed model provides a new insight into the connection between the status of metabolism of an organism and translation and cell growth . Specifically , any event leading to a decrease of NAD level in a cell should slow down all PARP1 dependent processes in ribosome biogenesis and , therefore , change the rate of translational apparatus assembly . While our results establish a direct connection between PARP1 and ribosome biogenesis , our findings do not exclude the possibility that PARP1 accumulation has other additional functions inside the nucleolus . One such function could involve protecting genomic stability of tandemly organized clusters of ribosomal genes . The presence of tandem arrays creates a possibility of unequal crossover , as a consequence of partial loosening of rDNA , which could be crucial for viability [50] . One of the first functions proposed for PARP1 protein upon its discovery was its involvement in DNA repair [51] , [52] . Therefore , PARP1 may be a specific protector of rDNA that guards it against genetic instability by creating barrier between rDNA and enzymes involved in homology repair . Alternatively , the presence of negatively charged pADPr may create a microenvironment which blocks homologue recombination within tandem arrays and therefore protects these arrays from unequal crossover .
Flies were cultured on standard cornmeal-molasses-agar media at 22°C , unless otherwise indicated . The fly stocks were generated by the standard genetic methods or obtained from the Bloomington Drosophila Stock Center and the Exelixis Collection at the Harvard Medical School , except as indicated . Genetic markers are described in Flybase [53] . CC01311 GFP-trap stock was obtained from the A . Spradling Lab [33] . The ParpC03265 strains were generated in a single pBac-element mutagenesis screen [54] . Parg27 . 1 [19] and ParpCH1 [23] mutants were previously described . pP{w1 , UAS::PARG-EGFP} , called UAS:: PARG-EGFP , has also been previously described [21] . pP{w1 , UAS::PARP1-DsRed} , called UAS::PARP1-DsRed , was described [23] . The following GAL4 driver strains were used: arm::GAL4 ( Bloomington stock no . 1560 ) , da::GAL4 ( gift of A . Veraksa ) , and 69B-GAL4 [55] . Balancer chromosomes carrying Kr::GFP , i . e . , TM3 , Sb , P{w+ , Kr-GFP} and FM7i , P{w1 , Kr-GFP} [56] , were used to identify heterozygous and homozygous ParpCH1 , ParpC03265 and Parg27 . 1 . To construct the anti-Parp RNAi transgene we cloned an 1839-bp fragment of Parp-e cDNA ( from GM10715 clone ) in direct and inverted orientation within the pUASt vector . As a spacer between inverted repeats we used a 720-bp fragment of EGFP sequence ( Figure S3 ) . Transformation was as described [57] , with modifications [58] . Protein extracts were separated on a 4–12% gel ( Invitrogen ) , transferred onto a nitrocellulose membrane and detected using Amersham/GE Healthcare ( #RPN2106 ) kit , according to manufacturer's instructions . The following primary antibodies were used: anti-Fibrillarin ( rabbit , 1∶4000 , gift form Dr . J . Gall ) , anti-Nucleolin ( rabbit , 1∶4000 ) , nucleolar AJ1 antibody ( rabbit , 1∶1000 , gift form Dr . J . Gall ) , anti-Nucleophosmin ( rabbit , 1∶1000 ) , anti-dNop5 ( rabbit , 1∶2000 , gift form Dr . G . Vorbruggen ) , anti-GFP ( rabbit , Torrey Pines Biolabs , #TP401 , 1∶1000 ) , anti-CKIIα ( rabbit , 1∶150 , Stressgen , # KAP-ST010 ) , anti-DLG ( rabbit , 1∶5000 , gift from Dr , F . Roegiers ) and anti-RPS6 ( mouse mAb , 1∶1000 , Cell Signalling #2317 ) . Electron microscopy was performed essentially as described [20] . Immunoprecipitation experiments were performed as described [20] , with little modifications . Briefly , 30 ul of Protein-G Sepharose 4B were added to the protein lysates and incubated overnight at 4°C with rotation . Beads were washed 4 times for 5 min in 1 ml of lysis buffer . Bound proteins were eluted using 60 ul of 1× Laemli with heating at 95°C for 5 min . The following antibodies were used for immunoprecipitation: anti-pADPr ( Mouse mAb , H10 1∶20 , Tulip , #1020 ) . Total RNA was isolated using Trizol reagent ( Gibco BRL ) , precipitated twice with 3 M LiCl , treated with Amplification Grade Dnase I ( Gibco BRL ) . The following primers were used to produce ITS probe detecting intermediates of pre-rRNA: ITSf ( 5′-ataacaaaatgattccatgg-3′ ) and ITSr ( 5′-aaaaatacaccattttactgg-3′ ) ; for 18S rRNA probe: 18Sf ( 5′-aaaagtgaaaccgcaaaagg-3′ ) and 18Sr ( 5′-taatgatccttccccgcagg-3′ ) . For Northern blot analysis , at least of 2 . 5 ug of total nuclear RNA from third instar larvae was used per lane . A Tubulin probe was used as a loading control . Analysis of ribosomes by sucrose density gradient centrifugation was carried out as described [6] , with modifications . Briefly , 3rd instar larvae were picked , washed 2× in distilled water , followed by 20 min incubation at room temperature in 200 ul of Buffer A ( 20 mM HEPES [pH 7 . 5] , 10 mM KCl , 2 . 5 mM MgCl2 , 1 mM EGTA , 100 ug/ml cycloheximide , 1 mM DTT ) . 450 ul of Buffer A was then added , and larvae were then lysed on ice and centrifuged for 5 min at 10 , 000 rpm at 4°C . The absorbance of the supernatant was measured at 260 nm and 400 ug of each sample was carefully loaded onto a 10 . 5 ml 10–55% sucrose gradient in Buffer A without cycloheximide and DTT , centrifuged for 8 hrs at 27 , 000 rpm in a SW41 rotor . 1 ml fractions were collected using a Foxy Jr gradient collector ( ISCO ) with a UV detection system for recording profile . 25% of selected fractions were TCA-precipitated and analyzed by Western blot for the presence of S6 ribosomal protein ( Cell Signaling ) . 380 ul of the remaining samples was used for RNA isolation . 30 ul of 10% SDS/Tris ( pH 7 . 5 ) and 1 ml of combined phenol∶Chloroform∶Isoamyl Alcohol solution ( invitrogen cat # 15593-031 ) was added to each sample and heated at 65°C for 2 min . Samples were centrifuged and the supernatant transferred to a new tube . Extraction was repeated again using the phenol∶Chloroform∶Isoamyl Alcohol solution , followed by transfer of the supernatant . 1/10 the volume of 3 M sodium acetate and 2× the volume of cold ethanol was added to each sample followed by incubation at −20°C for 20 min . Samples were centrifuged at 4°C for 4 min and the resulting pellet dissolved in appropriate volume of RNase free water to be used for qPCR analysis . 10 ug of RNA was used for cDNA synthesis using the high capacity cDNA Reverse Transcription Kit ( applied biosystems cat # 4368814 ) . The following primer sequences: Tubulin: Forward ( ccttcgtccactggtacgtt ) , Reverse ( ggcgtgacgcttagtactcc ) ; GAPDH: Forward ( cgacaagttcgtgaagctga ) , Reverse ( attctaccgcgccctaatct ) ; Act5C: Forward ( gtgcccatctacgagggtta ) , Reverse ( agggcaacatagcacagctt ) were utilized with Power SYBER Green master mix ( cat # 4367659 ) for PCR using the Applied Biosystems StepOnePlus detection system with the following cycling conditions: 95°C for 10 min , followed by 40 ( 2-step ) cycles ( 95°C , 15 s; 60°C , 60 s ) . rDNA probes were generated by digesting full length Drosophila rDNA gene ( 18S , 5 . 8S , and 28S ) containing intergenic sequences with the following enzymes: HaeIII , AluI , MspI , RsaI , and MseI ( New England Biolabs ) . Labeled probes were generated using DIG DNA Labeling Kit ( Roche; Cat # 11 175 033 910 ) essentially as outlined by manufacturer . FISH protocol was carried out as described [59] , with modifications . Salivary glands were dissected from 3rd instar larvae and fixed in for 10 min at room temperature ( RT ) in 4% formaldehyde in buffer A ( 15 mM PIPES , 80 mM KCl , 20 mM NaCl , 2 mM EDTA , 0 . 5 mM EGTA , 0 . 5 mM spermidine , 0 . 15 mM spermine , 1 mM DTT ) prewarmed to 37°C . After fixing , salivary glands were washed three times ( 5 min each ) in 2× SSCT ( SSC in 0 . 1% Tween ) followed by successive incubation at RT for 10 min in 20% formamide in 2× SSCT , 40% formamide in 2× SSCT , 50% formamide in 2× SSCT . Salivary glands were again incubated in fresh 50% formamide in 2× SSCT for 30 min at 37°C . The solution was aspirated and salivary glands were incubated with DNA probes diluted in 40 ul of hybridization solution ( 50% formamide , 3× SSCT , 20% dextran sulfate , 0 . 25% Tween ) . Probes and salivary glands were denatured ( 91°C for 2 min ) together in a thermal cycler . Hybridization was carried out at 37°C for 24 hrs . Following hybridization , salivary glands were washed three times for 20 min each in 50% formamide in 2× SSCT at 37°C , and a wash in 25% formamide in 2× SSCT at RT . Salivary glands were washed again three times in 2× SSCT and 1× PBS/Tween at RT for 5 min each . Following washes , immunostaining protocol for Fibrillarin ( outlined below ) was carried out , starting by blocking with 10% BSA solution . The DIG labeled probes and Fibrillarin were detected by staining with rhodamine-conjugated anti-digoxigenin F ( ab ) fragments ( Boehringer Mannheim ) and Goat Anti-Mouse Alexa 488 diluted in 1× PBS/Tween at RT for 2 hrs . Salivary glands were washed with 1× PBS/Tween at RT twice , 20 min each time followed by incubation with DNA binding dye , Drag5 , for 1 hr at RT . Salivary glands were dissected out in Grace's medium brought to room temperature . Salivary glands were then moved directly into fixative solution of 2% formaldehyde in PBS containing 1% Triton X-100 ( PBT ) ( in 1 . 5 mL Eppendorf tube ) and rotated at room temperature for 30 min . After washing twice for 5 min each in PBT , blocking solution of PBT containing 10% bovine serum albumin ( 10% BSA ) was applied to salivary glands and rotated at room temperature for 1 hr . After blocking in 10% BSA solution , salivary glands were washed in PBT containing 1% bovine serum albumin ( 1% BSA ) for 5 min . Primary antibodies were then applied to salivary glands . Mouse Anti-Nop1p Fibrillarin ( Corning ) at a dilution of 1∶200 , Rabbit Anti-Nucleolin ( Abcam ) at a dilution of 1∶200 , Rabbit Anti-Fibrillarin ( Abcam ) at a dilution of 1∶500 , Rabbit Anti-dNop5 ( 1∶400 ) , Rabbit nucleolar AJ1 antibody ( 1∶400 ) and Rabbit Anti-GFP ( 1∶500 ) were applied to respective samples . Salivary glands were incubated in primary antibody overnight at 4 degrees on a rotator . After that , samples were washed in 1% BSA solution three times for 10 min each . Salivary glands were incubated with appropriate secondary antibody at room temperature on rotator for 2 hrs , and Goat Anti-Mouse Alexa 488 , Goat Anti-Rabbit Alexa 568 , Goat Anti-Rabbit Alexa 488 and Alexa 633 ( from Molecular Probes ) at a dilution of 1∶400 were applied . Next , samples were washed twice in PBT buffer for 5 min and then subjected to chromatin staining using Draq5 ( Biostatus ) at a dilution 1∶500 in PBT buffer for 1 hr at room temperature on rotator or Oligreen ( Invitrogen ) at a dilution of 1∶10 , 000 in PBT buffer solution for 10 min at room temperature . Salivary glands stained with Oligreen were then washed twice for 5 min in PBT buffer solution and fixed to microscope slide . Images were obtained using the Leica ( DM-IRB ) Confocal System . | Ribosome assembly happens primarily in the subnuclear organelle nucleolus . In the nucleolus , ribosomes are assembled into a multmeric complex , composed of rRNA and ribosomal proteins . Although a lot is known about ribosomes and how they function , very little is known about the mechanism that facilitates the assembly of these multimeric protein complexes in the nucleolus . Here , we provide evidence that a nuclear protein , PARP1 , primarily known for its DNA damage repair and transcriptional activities , also plays a critical role in the assembly of ribosomes . Using the Drosophila model system , we show that PARP1 localization within the nucleolus impacts such nucleolar activities as rRNA processing and ribosome biogenesis . We show that , when PARP1 activity is disrupted , nucleolar proteins that normally co-localize under wild-type conditions disperse into the nucleoplasm and do not show any co-localization . We also show that some nucleolar proteins , essential for rRNA processing , also interact with pADPr , which keeps these proteins close to precursor rRNA . When PARP1 activity was disrupted , we observed precursors rRNA accumulation and a concomitant decrease in the levels of ribosomes . Together , our data suggest a novel activity for PARP1 and highlight a potential mechanism associated with ribosome biogenesis in the nucleolus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"biology",
"genetics",
"and",
"genomics",
"gene",
"function"
] | 2012 | Poly(ADP-Ribose) Polymerase 1 (PARP-1) Regulates Ribosomal Biogenesis in Drosophila Nucleoli |
Clustered copy number variants ( CNVs ) as detected by chromosomal microarray analysis ( CMA ) are often reported as germline chromothripsis . However , such cases might need further investigations by massive parallel whole genome sequencing ( WGS ) in order to accurately define the underlying complex rearrangement , predict the occurrence mechanisms and identify additional complexities . Here , we utilized WGS to delineate the rearrangement structure of 21 clustered CNV carriers first investigated by CMA and identified a total of 83 breakpoint junctions ( BPJs ) . The rearrangements were further sub-classified depending on the patterns observed: I ) Cases with only deletions ( n = 8 ) often had additional structural rearrangements , such as insertions and inversions typical to chromothripsis; II ) cases with only duplications ( n = 7 ) or III ) combinations of deletions and duplications ( n = 6 ) demonstrated mostly interspersed duplications and BPJs enriched with microhomology . In two cases the rearrangement mutational signatures indicated both a breakage-fusion-bridge cycle process and haltered formation of a ring chromosome . Finally , we observed two cases with Alu- and LINE-mediated rearrangements as well as two unrelated individuals with seemingly identical clustered CNVs on 2p25 . 3 , possibly a rare European founder rearrangement . In conclusion , through detailed characterization of the derivative chromosomes we show that multiple mechanisms are likely involved in the formation of clustered CNVs and add further evidence for chromoanagenesis mechanisms in both “simple” and highly complex chromosomal rearrangements . Finally , WGS characterization adds positional information , important for a correct clinical interpretation and deciphering mechanisms involved in the formation of these rearrangements .
Structural variants ( SVs ) contribute to genomic diversity in human [1] and include copy number variants ( CNVs ) ( deletions , duplications ) , as well as copy number neutral ( balanced ) variants ( inversions and translocations ) , and more complex rearrangements , resulting from chromothripsis and/or chromoanasynthesis [2 , 3] . Complex SVs ( complex chromosomal rearrangements , CCRs ) often result in congenital and developmental abnormalities , as well as in cancer development , although carriers with unaffected phenotypes have also been reported [4] . A rare phenomenon regularly observed in clinical genetic diagnostic laboratories is multiple CNVs co-localizing on the same chromosome . Even though a chromosomal microarray ( CMA ) may identify such rearrangements , further characterization with whole genome sequencing ( WGS ) may be useful . A previous WGS study of two closely located duplications revealed additional copy-neutral complex genomic rearrangements associated with paired-duplications , such as inverted fragments , duplications with a nested deletion and other complexities , which were cryptic to CMA [5] . Proposed mechanisms that could explain the formation of multiple CNVs on the same chromosome include chromothripsis and chromoanasynthesis [6 , 7] while the term chromoanagenesis , a form of chromosome rebirth , describe the two phenomena independent of the underlying mechanism [8] . Chromothripsis is a chromosome shattering phenomenon , where part of or an entire chromosome , or few chromosomes , are fragmented into multiple pieces and reassembled in a random order and orientation resulting in complex genomic rearrangements [9] . During this process , some of the generated fragments can be lost resulting in heterozygous deletions . One of the distinctive features of chromothripsis is that the rearrangement breakpoints ( BPs ) are localized to relatively small genomic regions , usually spanning a few Mb . The causes of such clustered fragmentations are still unclear , however some studies suggested that chromothripsis could be generated through the physical isolation of chromosomes within micronuclei , where the “trapped” lagging chromosome ( s ) undergo defective DNA replication and repair , resulting in chromosome pulverization [10 , 11] . Others hypothesized that the clustered DNA double-strand breaks ( DSBs ) during chromothripsis could be initiated by ionizing radiation [9 , 12] , breakage-fusion-bridge cycle associated with telomere attrition [9 , 13] , aborted apoptosis [14] , as well as endogenous endonucleases [15] . The highly characteristic breakpoint-junction ( BPJ ) sequences in the derivative chromosomes point to non-homologous end-joining ( NHEJ ) [16] or microhomology-mediated end-joining ( MMEJ ) [17] as being likely underlying repair mechanisms for rejoining of the shattered DNA fragments [9 , 18 , 19] . Although non-allelic homologous recombination ( NAHR ) was excluded as a chromothripsis repair mechanism [20] , our recent report showed that homologous Alu elements may also mediate germline chromothripsis [15] . Chromothripsis was deciphered by the help of whole genome next generation sequencing technologies ( WGS ) in microscopic complex chromosomal rearrangements involving three or more BPs [18 , 19 , 21 , 22] , as well as in microscopically balanced reciprocal translocations [23 , 24] . Chromoanasynthesis [25] , was described by high resolution chromosome microarray analysis ( CMA ) and refers to clustered copy number changes , including deletions , duplications , and triplications , that are flanked by regions of normal dosage state . Small templated insertions and microhomologies found at most BPJs pinpointed that chromoanasynthesis likely involves replication failures , such as fork stalling and template switching ( FoSTeS ) [26] and/or microhomology-mediated break-induced replication ( MMBIR ) [27] . Another rare but distinct underlying mechanism of formation is atypical chromoanasynthesis that seems to only involve single chromosomes and exclusively generate duplications [28] , either clustering on one chromosome arm or scattered throughout the entire chromosome . It has also been shown that clustered duplications confined to a single chromosome may not only be integrated into the chromosome-of-origin in tandem , but could be integrated at multiple positions in the derivative chromosome and have non-templated insertions at the BPJs , indicating a different mutational mechanism , such as alternative NHEJ mediated by the DNA polymerase Polθ [28] . Finally , evidence suggests that both chromothripsis and replicative errors are not only responsible for highly complex rearrangements involving several chromosomes or a large number of chromosomal segments . Even simpler rearrangements involving a small number of chromosomal segments on a single chromosome could have formed through shattering of a chromosome or replicative errors [21] . To delineate the chromosomes and analyze the plausible underlying mechanisms of formation of multiple CNVs on a single chromosome , we characterized 21 germline complex rearrangements initially detected by CMA . The rearrangements involved only duplications , only deletions or both deletions and duplications . Underlying mechanisms of rearrangement formation were inferred from the BPJ architecture as well as the overall connective picture .
Based on the CNV type , all rearrangements were classified into deletions-only group ( n = 8 ) , duplications-only group ( n = 7 ) and deletions-and-duplications group ( n = 6 ) ( S1 Fig ) . Examples from each group are presented in Fig 1 . The average number of BPJs per case was 4 ( range = 2–14 ) . The rearrangements in the duplications-only group contained the fewest BPJs per case ( average = 3 , range = 2–5 ) and consisted mostly of DUP-DIP-DUP rearrangements ( Table 1 ) . The rearrangements in the deletions-only group contained slightly more junctions ( average = 4 , range = 2–7 ) . The rearrangements belonging to the deletions-and-duplications group showed the highest degree of complexity with more BPJs per case ( average = 6 , range = 2–14 ) . In total , WGS revealed additional duplicated or deleted fragments not detected by CMA in 16 out of 21 cases ( 76% ) ( Table 3 ) . In most of the cases , the obtained BPJs allowed us to resolve the exact nature of rearranged chromosomes . For one case ( P5513_206 ) from the duplications-only group , there was no conclusive order for the duplicated fragments , hence three possibilities are shown in Fig 2 . In one highly complex case ( P1426_301 ) the full connective picture of rearranged chromosomes could not be established ( Fig 3 ) . In four cases where CMA suggested two clustered duplications separated by a diploid fragment ( P4855_511 , P2109_150 , P06 and P74 ) , WGS revealed a nested deletion within the duplicated segment ( S2 Fig ) . Notably , all these four rearrangements were maternally inherited indicating that the duplication and the deletion are located in cis . In addition , WGS allowed detection of copy-neutral segments ( inversions and insertions ) ; and in total , 37 inversions were detected within the clustered CNVs ( Table 3 ) . The deletions-only group contains a large number of inverted fragments similar to the deletions-and-duplications group , while the duplications-only group contains only four duplicated fragments with inverted orientation in three cases ( P209_151 , P4855_512 and P5513_206 ) ( Table 3 ) . Several OMIM morbid genes were identified in clustered CNVs detected by CMA ( S3 Table ) . A CNV was assessed as pathogenic or likely pathogenic in 11 cases , as benign in one case , and in the remaining cases as variants of unknown significance ( Table 1 ) . The pathogenicity classification was based on the American College of Medical Genetics and Genomics ( ACMG ) guidelines [29] and included the segregation analysis , amount of OMIM morbid genes or specific disease-related genes , size of the CNVs and/or if the CNVs had been reported previously in patients with similar phenotype . None of the CNVs disrupted an OMIM morbid gene but all CNVs that were classified as likely pathogenic or pathogenic was based on gene dosage sensitivity mechanisms . In four cases ( P2046_133 , P5513_206 , P5513_116 and P1426_301 ) WGS enabled detection of further OMIM morbid genes , which could not be revealed by CMA ( S3 Table ) . Thirteen of the 21 rearrangements consisted of 36 duplicated fragments ( Table 1 ) : 17 of these fragments belong to the duplications-only group ( 7 individuals ) and 19 fragments belong to the deletions-and-duplications group ( 6 individuals ) . In all cases , the WGS data analysis could detect whether the duplications were tandem ( 3 fragments ) or interspersed ( 33 fragments ) . Notably , the majority of the duplications were interspersed ( 92% ) . There was a single tandem duplication in the duplications-only group ( P4855_512 ) and two tandem duplications in the deletions-and-duplications group ( P5371_204 and P2109_176 ) ( Fig 1B ) . All interspersed duplications were intrachromosomal and 46% of the duplicated fragments were inverted , indicating random orientation of the duplicates . The duplicates of the interspersed duplications clustered tightly: 79% of the duplicates were inserted next to another duplicate . P5513_206 represents such a rearrangement that consists of five interspersed duplications , all inserted in a clustered but seemingly random manner in the same region ( Fig 2 ) . Of the 83 total BPJs , 63 ( 19 cases ) were resolved to single nucleotide resolution ( Table 2 ) . SplitVision analyses suggested the following features for the BPJs: novel single nucleotide variants ( SNVs ) within 1 kb of the BPJ ( absent in gnomAD and SweFreq ) , microhomology , short insertions and repeat elements . Most of the rearrangements contained at least one of these features ( S2 Table , Table 2 ) . In total , 30 BPJs ( 48% ) contained microhomology stretches ranging from 2 to 32 nucleotides ( median = 2 ) ( S2 Table , S5 Fig , S6 Fig ) . Even though repeat elements were enriched in BPJs , fusions of similar repeats were only observed in 11 BPJs ( 13% ) . The longest stretch of microhomology was 32 nucleotides ( P2109_123 ) and involved homologous Alu associated BPs ( Fig 4A ) . Similarly , all the 11 BPs in P2109_176 contained LINE elements resulting in fusion LINEs at the BPJs ( Fig 4B ) . The most complex case , P1426_301 , contained deletions , duplications , and inversions and harbored 25 BPs ( 14 BPJs ) where 16 ( 64% ) were located within repeat regions ( Fig 3 , S6 Fig ) . In two cases ( P4855_512 and P5371_204 ) , two BPJs harbored novel SNVs within 1 kb of BPJs localized to non-coding regions . Lastly , 10 blunt BPJs were identified in 5 cases ( P2046_133 , P81 , P00 , P4855_511 , P06 ) ( Table 2 , S2 Table , S6 Fig ) . P2046_133 , P81 and P00 belong to the deletions-only group , and P4855_511 and P06 belong to the duplications-only group . No blunt BPJs were found in the deletions-and-duplications group ( Table 2 ) . Comprehensive analysis of the BPJ characteristics surrounding the BPJs in all cases and comparisons between the groups are presented in S5 Fig and S6 Fig . Molecular signatures at the BPJs further enabled the reconstruction of underlying mutational mechanisms . For example , blunt joints , absent or short microhomology ( 1–4 bp ) and small insertions or deletions at the BPJs are characteristic of DNA DSB repair through direct ligation by NHEJ . In the clustered CNVs studied here , we observed that most of the BPJs involved in the deletions-only group showed such signatures ( Table 2 , S2 Table ) pinpointing involvement of NHEJ . Alternatively , DNA DSBs can also be repaired by alternative NHEJ ( alt-NHEJ ) mechanisms , such as MMEJ which is a more error prone repair pathway highly dependent on microhomology [17] . MMEJ may result in deletions of the DNA regions flanking the original BP , and longer stretches of both templated ( sequences found within 100 nucleotides upstream or downstream of the junction ) and non-templated ( seemingly random nucleotides ) insertions at the BPJs . One of the characterized BPJs in P2109_188 has very typical signatures of MMEJ: a 14bp non-templated insertion followed by a 26 bp templated insertion ( chr21:45466217–45466242 , ( - ) strand ) , followed by another 12 bp non-templated insertion , plus 3 bp and 4bp microhomologies at the 5’- and the 3’-sides of the BPJ ( S3 Fig ) . Short stretches of microhomologies ( 2–3 bp ) were also found at other BPJs in the deletions-only group ( i . e . P00 , P2046_133 , P2109_190 , P2109_302 ) . It is important to note that these features are also overlapping with features consistent with alt-NHEJ mediated by PARP1 , CTIP , MRE11 , DNA ligase I/III and polymerase θ ( Polθ ) [28 , 30 , 31] , which is associated with short single-strand overhangs after a DSB . This typically leads to inserts of 5–25 bp before ligation and hence leads to short stretches of microhomology seen in the BPJ [31] , similar to what is seen in MMEJ . In addition , canonical NHEJ and alt-NHEJ can operate simultaneously in the same cell [32] , and this possibility needs to be taken into consideration as well . Overall , microhomologies were mostly prevalent at the BPJs of the complex rearrangements containing duplications ( 54% and 59% for duplications-only group and deletions-and-duplications group , respectively ) ( Table 2 , S5 Fig ) . A model of replication-based mechanisms , for example multiple template switching , could better explain the formation of these complex rearrangements ( Fig 3B , Fig 4 ) . Such mechanisms are commonly associated with similar features as MMEJ , as well as de novo single nucleotide variants around the BPJs [33] . Seemingly identical rearrangements on 2p25 . 3 were identified in individuals P4855_511 ( from Sweden ) and P06 ( from Denmark ) , belonging to the duplications-only group based on CMA results . However , these two cases were later redefined as having duplication with a “nested” deletion inside the duplicated fragment . An identical blunt BPJ without microhomology ( the BPJ of the nested deletion ) was detected in both P4855_511 and P06 . The duplication junction was resolved at nucleotide level only in P4855_511 and a 3bp microhomology ( TGC ) was detected at the BPJ through split reads in the deep paired-end data . However , for case P06 no split-read was present for the BPJ showing the duplication in the shallow mate-pair WGS data . Several attempts were made to amplify the BPJ using breakpoint PCR and Sanger sequencing without success due to GC-rich sequences in the area . Hence , we could only compare the junction sequences of one junction , which were identical , including a SNV ( rs4971462 ) in cis upstream of the junction ( S4 Fig ) . This may suggest that the 2p25 . 3 could be a rare founder variant in Europe . However , using the WGS data from P4855_511 and the Affymetrix Cytoscan HD SNP array data from P06 , we analyzed 100 common SNVs surrounding the rearrangement and found that the haplotypes for these variants varied in a way that would be expected for two unrelated individuals . Hence , it was not possible to assess whether the rearrangement in these two individuals have occurred through separate events or in a common ancestor . No evidence suggest that the region is a hotspot for CNV formation , no common repeat structure was present in the BPJs and we also assessed the junction sequence from the common BPJ ( S4 Fig ) in the Predict a Secondary Structure Web Server ( https://rna . urmc . rochester . edu/RNAstructureWeb/Servers/Predict1/Predict1 . html ) and no significant structure was seen . Remaining rearrangements were all unique . Finally , the junction architecture may indicate that the nested deletion occurred via non-replicative mechanisms ( e . g . NHEJ ) , which require no microhomology . Although the tandem duplication might occur during replication process , we hypothesize that they occurred within a single cell cycle , as the duplication is co-segregated with deletion in both families . We and others have previously shown that the sequence homology between Alu elements ( average 71% ) may facilitate unequal crossover between genomic segments and generate Alu-Alu mediated CNVs , inversions , translocations and chromothripsis [15 , 34 , 35] . In the current cohort , DEL-INV-DEL rearrangements on 17p13 . 3 are associated with fusion Alu–Alu elements at both junctions ( P2109_123 ) , suggesting an Alu-Alu mediated mechanism in this complex rearrangement . Sequence identity between the AluSx_AluSx1 and AluSq2_AluSq2 pairs are 73 . 3% and 78 . 6% , respectively . Notably , both AluSx_AluSx1 and AluSq2_AluSq2 pairs are in opposite orientation on the reference genome , which resulted in inversion of the fragment C ( Fig 4A ) . As the sequence identity of involved Alu pairs is < 90% , it might not be sufficient for homologous recombination , while MMEJ or FoSTeS/MMBIR could potentially generate Alu-Alu mediated rearrangements here as previously suggested by other studies [34–36] . Indeed , 17p13 . 3 region is known to be Alu rich and consequently many Alu-Alu mediated CNVs and complex genomic rearrangements associated with multiple disorders have been reported [35] . Similarly , in P2109_176 involving a combination of deletions , duplications and other copy-neutral rearrangements on chromosome 2 , we observed LINE elements at all 11 BPs , indicating underlying LINE-mediated mechanisms ( Fig 4B ) . Here , we found 3–5 bp microhomologies at most of the BPJs , indicating replication based FoSTeS/MMBIR mechanisms likely being involved in this case . Finally , 14 out of 25 BPs in the most complex case ( P1426_301 ) containing deletions , duplications , and inversions are located within repeat regions of different classes likely providing microhomology for multiple template switching ( Fig 3 ) .
In the current study we present 21 individuals with two or more clustered non-recurrent CNVs confined to a single chromosome including both chromosomal arms ( two cases ) or to a single chromosomal arm ( 19 cases ) . WGS enabled us to decipher the true nature of the rearrangements including detection of copy neutral variants within or flanking the rearrangements . The individuals had a wide range of clinical symptoms , including congenital malformations and neurodevelopmental disorders . Dosage of the genes located within the deleted and/or duplicated fragments and/or the disruption of genes located in the BPJs could be responsible for the clinical manifestations . In the current cohort , the more exact resolution of WGS as compared to CMA resulted in a reduction of the number of morbid OMIM genes affected in three cases ( 14% ) and in an increase in one individual ( 5% ) . However , this information did not influence the overall assessment of the clinical relevance . WGS analysis revealed additional complexities such as inversions and interspersed duplicates in most cases , findings that are in line with previous findings in a cohort of autism spectrum disorder where 84 . 4% of large complex SVs involved inversions [3] . In addition , we detected that most of the interspersed duplications were inserted next to another in a seemingly random manner , similar to the few cases reported before [28] . For ultra-complex chromosomal rearrangements such as the ones seen in P1426_301 and P00 , the large number of genomic pieces with breakpoints often located in repetitive regions complicates the mapping of the final structure of the derivative chromosome ( s ) . Third-generation sequencing including Pacific Biosciences SMRT long-read sequencing platform or Nanopore MinION sequencing has showed promising results [37 , 38] for bridging repetitive sequences and hence overcoming one of the largest limitations with short-read sequencing . The current study is limited by the fact that we did not try any of these technologies , which would be the next step needed to completely solve the structure of the derivative chromosomes in this case ( P1426_301 ) . Long-read sequencing might also add information in case P5513_206 that is presented here with three possible rearrangements of the duplicated fragments . By mapping all the BPs and resolving the links between the generated fragments , we observed several hallmarks of germline chromothripsis and chromoanasynthesis [4 , 25 , 39] . First , all the BPs associated with the complex rearrangements were clustered and confined to a single chromosome . Second , the rearranged fragments within the derivative chromosomes had random order and orientation . Third , the copy-number states detected in deletions-only group oscillated between one and two , typical to chromothripsis , while the rearrangements including duplications were mostly resembling chromoanasynthesis . Fourth , signatures of NHEJ and MMEJ pathways were mostly detected at the BPJs of the complex rearrangements included in the deletions-only group , which is compatible with the previous reports describing BPJs associated with chromothripsis [9 , 18 , 19 , 32] . Even though both chromothripsis and chromoanasynthesis are generally of paternal origin [6 , 40] , the current de novo chromosomal rearrangements occurred on the maternal and paternal chromosomes to the same extent . Of the seven de novo cases where we had parental samples , three had characteristics of chromoanasynthesis and replicative errors and two of those arose on the maternal chromosome . This is in contrast to the expectation that replicative error-mediated chromosomal aberrations would be biased towards spermatogenic origin . In addition , among the four cases with characteristics of chromothripsis , two were of paternal origin and two of maternal origin . Finally , we confirmed that Alu- or LINE- mediated mechanisms may also underlie chromothripsis formation . Most of the reported germline chromothripsis cases are nearly dosage-neutral , possibly due to embryonic selection against loss of dosage-sensitive genes . However , there are few reports of heavy imbalances detected by CMA , suggesting chromothripsis event [41–45] . Such cases need further investigations by paired-end or mate-pair sequencing in order to decipher the balanced rearrangements involved as well as to understand the underlying mechanisms . Our approach of applying high-resolution sequencing in such cases with clustered deletions , confirmed that additional copy-neutral SVs may coexist . Combined picture of such complex rearrangements resembled catastrophic phenomenon of chromosome “shattering” , where some of the fragments may be lost ( deleted ) , while retained fragments would be resembled by repair machinery with random order and orientation . The fact that clustered duplications and combinations of deletions and duplications typical to chromoanasynthesis revealed both non-tandem and inverted nature of most duplicates , enriched with microhomologies at the BPJs , further supports the notion that replication based mechanisms , may explain the complex nature of these derivative chromosomes . In summary , we suggest that seven cases in the current study ( P2109_190 , P72 , P2109_302 , P2109_123 , P2109_188 , P81 and P00 ) represents chromothripsis , ten cases ( P06 , P4855_511 , P2109_150 , P2109_151 , P74 , P4855_512 , P5513_206 , P2109_162 , P5513_116 , P5371_204 ) are chromoanasynthesis events and four cases ( P2109_185 , P2109_176 , P2046_133 and P1426_301 ) have ambiguous mutational signatures . All four ambiguous cases showed large non-templated insertions in the BPJ ( typical to Polθ-driven atypical chromoanagenesis or retrotransposition-mediated chromothripsis ) , but three cases harbored both duplications and deletions ( typical to chromoanasynthesis ) and one case contained only deletions ( typical to chromothripsis ) . Of the seven chromothripsis cases , one case was Alu-Alu mediated ( P2109_123 ) and one was likely mediated by replicative errors and the DSBs were joined through alt-NHEJ ( P2109_188 ) , while remaining cases showed more consistent signatures of canonical NHEJ or MMBIR . Among the cases involving duplications or both duplications and deletions , most BPJs showed signatures of replicative errors with microhomology in the breakpoints , some possibly caused by repeat elements , except in three cases from the deletions and duplications-group ( P2109_185 , P2109_176 , P1426_301 ) with non-templated insertions ranging in 8–52 bp in size and short microhomology ( 2–6 nt ) in the BPJs . These features are not fully consistent with replicative joining mechanisms such as FoSTeS/MMBIR , but it is possible that these cases are mediated by replicative errors , and that Polθ is involved in the stitching of the chromosomes , hence two operating repair machineries in the same cell . In two of the cases in our cohort ( P5513_116 and P2109_185 ) the clustered CNVs were detected on both arms of the chromosomes involved ( chromosome X and 5 , respectively ) . Notably , these two cases show similar patterns , where a terminal duplication of one chromosomal arm is inserted in the place of terminal deletion of the other chromosomal arm with an inverted orientation . A breakage-fusion-bridge cycle process could explain parts of this kind of rearrangement . Briefly , the process starts when a chromosome loses its telomere and after replication the two sister chromatids will fuse into a dicentric chromosome [46] . Then , during anaphase the two centromeres will be pulled towards opposite nuclei , resulting in the breakage of the dicentric chromosome . Random breakage may cause large inverted duplications . After the breakage there will be new chromosome ends lacking telomeres resulting in a new cycle of breakage-fusion-bridge , the cycles will stop once the chromosome end acquires a telomere . This mechanism has previously been suggested to explain some cases of chromothripsis formation [9 , 13 , 47] . Here , with telomeric regions of both chromosome arms being involved , it is likely that the breakage-fusion-bridge cycle has been accompanied by a formation-attempt of a ring chromosome . However , chromosome analysis and FISH had previously shown that no ring chromosome was formed in either of these cases . In addition , as mentioned previously , case P2109_185 showed characteristics of Polθ involvement in the stitching with large non-templated insertions in the BPJs . In conclusion , the BP characterization of the derivative chromosomes showed that multiple mechanisms are likely involved in the formation of clustered CNVs , including replication independent canonical NHEJ and alt-NHEJ , replication-dependent MMBIR/FoSTeS and breakage-fusion-bridge cycle , as well as Alu- and LINE-mediated pathways . WGS characterization adds positional information important for a correct interpretation of complex CNVs and for determining their clinical significance; and deciphers the mechanisms involved in formation of these rearrangements .
The local ethical board in Stockholm , Sweden approved the study ( approval number KS 2012/222-31/3 ) . This ethics permit allows us to use clinical samples for analysis of scientific importance as part of clinical development . Included subjects were part of clinical cohorts investigated at the respective centers and the current study reports de-identified results that cannot be traced to a specific individual . All subjects have given oral consent to be part of these clinical investigations . The subjects included in this study ( n = 21 ) were initially referred to the Department of Clinical Genetics at the Karolinska University Hospital ( n = 13 ) , Kennedy Center ( n = 5 ) , Sahlgrenska University Hospital ( n = 2 ) or Linköping University Hospital ( n = 1 ) . All subjects were part of clinical cohorts investigated at respective centers with CMA due to congenital developmental disorders , intellectual disability or autism . Karyotypes and phenotypes are provided in Table 1 . Genomic DNA was prepared from whole blood using standard procedures . CMA was carried out using either SNP ( single nucleotide polymorphism ) or oligonucleotide microarrays . Fluorescent in situ hybridization ( FISH ) analysis or quantitative PCR ( qPCR ) with Power SYBR Green reagents ( Applied Biosystems , Carlsbad , CA , USA ) was employed to verify the structural variants . FISH- , qPCR- , or array comparative genomic hybridization ( aCGH ) analysis was used to investigate parental inheritance when possible . In 13 cases ( P2046_133 , P2109_123 , P2109_150 , P2109_151 , P2109_162 , P2109_188 , P2109_190 , P2109_302 , P4855_511 , P4855_512 , P2109_176 , P1426_301 , P2109_185 ) , the CMA was performed with an 180K custom oligonucleotide microarray with whole genome coverage and a median resolution of approximately 18 kb ( Oxford Gene Technology ( OGT ) , Oxfordshire , UK ) . Experiments were performed at the Department of Clinical Genetics at Karolinska University Hospital , Stockholm , Sweden , according to the manufacturer’s protocol . Slides were scanned using an Agilent Microarray Scanner ( Agilent Technologies , Santa Clara , CA , USA ) . Raw data were normalized using Feature Extraction Software ( Agilent Technologies , Santa Clara , CA , USA ) , and log2 ratios were calculated by dividing the normalized intensity in the sample by the mean intensity across the reference sample . The log2 ratios were plotted and segmented by circular binary segmentation in the CytoSure Interpret software ( OGT , Oxfordshire , UK ) . Oligonucleotide probe positions were annotated to the human genome assembly GRCh37 ( Hg19 ) . Aberrations were called using a cut-off of three probes and a log2 ratio of 0 . 65 and 0 . 35 for deletions and duplications , respectively . For eight cases ( P72 , P81 , P06 , P74 , P5513_206 , P5513_116 , P5371_204 , P00 ) the CMA was performed using an Affymetrix CytoScan HD array and data were analyzed with ChAS software ( Affymetrix , Santa Clara , CA , USA ) using the following filtering criteria: deletions > 5 kb ( a minimum of 5 markers ) and duplications >10 kb ( a minimum of 10 markers ) . Patients’ CNV data were reported to ClinVar ( P2046_133 , P2109_123 , P2109_150 , P2109_151 , P2109_162 , P2109_188 , P2109_190 , P2109_302 , P4855_511 , P4855_512 , P2109_176 , P1426_301 , P2109_185 , P5513_206 , P5513_116 , P5371_204 ) or to DECIPHER ( P72 , P81 , P06 , P74 , P00 ) . Mate-pair libraries were prepared using Nextera mate-pair kit following the manufacturers’ instructions ( Illumina , San Diego , CA , USA ) . The subjects were investigated with the gel-free protocol where 1 μg of genomic DNA was fragmented using an enzymatic method generating fragments in the range of 2–15 kb . The final library was subjected to 2x100 bases paired-end sequencing on an Illumina HiSeq2500 sequencing platform . The PCR-free paired-end Illumina WGS data was produced at the National Genomics Infrastructure ( NGI ) , Stockholm , Sweden . The WGS data was generated using the Illumina Hiseq Xten platform , which produced an average coverage of 30X per sample . The average insert size of the WGS libraries was 350 bp , and each read length was 2x150 bp . The WGS data was aligned to GRCh37 ( Hg19 ) using BWA-mem ( version 0 . 7 . 15-r1140 ) [48] , and duplicates were marked using Picard tools ( http://broadinstitute . github . io/picard/ ) . Structural variant calling was performed using FindSV ( https://github . com/J35P312/FindSV ) , which combines CNVnator [49] and TIDDIT [50] . The variant call format ( vcf ) files of these two callers were merged and annotated using VEP [51] and filtered against an internal frequency database consisting of 350 individuals . The exact position of the BPs was pinpointed using split reads ( S2 Table; cases P2046_133 , P2109_123 , P2109_150 , P2109_151 , P2109_162 , P2109_188 , P2109_190 , P2109_302 , P4855_511 , P4855_512 , P2109_176 , P5513_116 , P5371_204 , P1426_301 , P2109_185 ) or Sanger sequencing ( cases P00 , P06 and P81; Primers and PCR conditions will be provided upon request ) . The WGS data and Sanger reads were analyzed for junction features such as microhomology , insertions , single nucleotide variants ( SNVs ) , and repeat elements using blat ( https://genome . ucsc . edu/cgi-bin/hgBlat ? command=start ) and an in-house developed analysis tool dubbed SplitVision ( https://github . com/J35P312/SplitVision ) ( S1 Appendix ) . In short , SplitVision searches for split reads bridging each BPJ . A consensus sequence of these reads are generated through multiple sequence alignment using ClustalW [52 , 53] and assembly using a greedy algorithm; maximizing the length and support of each consensus sequence . The consensus sequences are then mapped to the reference genome using BWA . The exact BPs as well as any microhomology and/or insertions at the BPJs are found based on the orientation , position and cigar string of the primary and supplementary alignments of the consensus sequences . Additionally , SplitVision searches for repeat elements and SNVs close to the BPJs ( <1 kb ) . Repeat elements are found using the USCS repeat masker [54] and SNVs are called using SAMtools [55] . Lastly , the SNVs were filtered based on the SweFreq ( SweGen Variant Frequency Dataset ) [56] and gnomAD ( http://gnomad . broadinstitute . org ) . The allele frequency threshold was set to 0 , removing any previously reported SNVs , and SNVs located in regions not covered by the SweGen dataset . The quality of the remaining SNVs was assessed using the Integrative Genomics Viewer ( IGV ) tool [57] . 10X Genomics Chromium WGS was performed on sample P00 at NGI , Stockholm , Sweden . Libraries were prepared using the 10X Chromium controller and sequenced on an Illumina Hiseq Xten platform . Data was analyzed using two separate pipelines developed by 10X Genomics: the default Long Ranger pipeline ( https://support . 10xgenomics . com/genome-exome/software/downloads/latest ) and a custom de novo assembly pipeline based on the Supernova de novo assembler ( https://support . 10xgenomics . com/de-novo-assembly/software/downloads/latest ) . The custom de novo assembler pipelines included mapping of raw Supernova contigs with the bwa mem intra-contig mode , as well as extraction of split contigs using a python script ( https://github . com/J35P312/Assemblatron ) . The bam files of all the sequenced samples indicating SVs are deposited in European Nucleotide Archive ( ENA ) , ( S4 Table ) . Patients’ CNV data are reported to ClinVar ( P2046_133 , P2109_123 , P2109_150 , P2109_151 , P2109_162 , P2109_188 , P2109_190 , P2109_302 , P4855_511 , P4855_512 , P2109_176 , P1426_301 , P2109_185 , P5513_206 , P5513_116 , P5371_204 ) or to DECIPHER ( P72 , P81 , P06 , P74 , P00 ) . The details of in-house developed analysis tool dubbed SplitVision is provided in S1 Appendix ( https://github . com/J35P312/SplitVision ) . | Clustered copy number variants ( CNVs ) as detected by chromosomal microarray are often reported as germline chromoanagenesis . However , such cases might need further investigation by whole genome sequencing ( WGS ) to accurately resolve the complexity of the structural rearrangement and predict underlying mutational mechanisms . Here , we used WGS to characterize 83 breakpoint-junctions ( BPJs ) from 21 clustered CNVs , and outlined the rearrangement connectivity pictures . Cases with only deletions often had additional structural rearrangements , such as insertions and inversions , which could be a result of multiple double-strand DNA breaks followed by non-homologous repair , typical to chromothripsis . In contrast , cases with only duplications or combinations of deletions and duplications , demonstrated mostly interspersed duplications and BPJs enriched with microhomology , consistent with serial template switching during DNA replication ( chromoanasynthesis ) . Only two rearrangements were repeat mediated . In aggregate , our results suggest that multiple CNVs clustered on a single chromosome may arise through either chromothripsis or chromoanasynthesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"sequencing",
"techniques",
"microhomology-mediated",
"end",
"joining",
"genome",
"sequencing",
"non-homologous",
"end",
"joining",
"genome",
"analysis",
"bioassays",
"and",
"physiological",
"analysis",
"dna",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"genomic",
"libraries",
"genome",
"complexity",
"chromosomal",
"duplications",
"chromosome",
"biology",
"chromosomal",
"aberrations",
"molecular",
"biology",
"microarrays",
"biochemistry",
"chromosomal",
"deletions",
"cell",
"biology",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"repair",
"genomics",
"computational",
"biology"
] | 2018 | Replicative and non-replicative mechanisms in the formation of clustered CNVs are indicated by whole genome characterization |
Cryptococcus neoformans is a ubiquitous human fungal pathogen . This pathogen can undergo morphotype transition between the yeast and the filamentous form and such morphological transition has been implicated in virulence for decades . Morphotype transition is typically observed during mating , which is governed by pheromone signaling . Paradoxically , components specific to the pheromone signaling pathways play no or minimal direct roles in virulence . Thus , the link between morphotype transition and virulence and the underlying molecular mechanism remain elusive . Here , we demonstrate that filamentation can occur independent of pheromone signaling and mating , and both mating-dependent and mating-independent morphotype transition require the transcription factor Znf2 . High expression of Znf2 is necessary and sufficient to initiate and maintain sex-independent filamentous growth under host-relevant conditions in vitro and during infection . Importantly , ZNF2 overexpression abolishes fungal virulence in murine models of cryptococcosis . Thus , Znf2 bridges the sex-independent morphotype transition and fungal pathogenicity . The impacts of Znf2 on morphological switch and pathogenicity are at least partly mediated through its effects on cell adhesion property . Cfl1 , a Znf2 downstream factor , regulates morphogenesis , cell adhesion , biofilm formation , and virulence . Cfl1 is the first adhesin discovered in the phylum Basidiomycota of the Kingdom Fungi . Together with previous findings in other eukaryotic pathogens , our findings support a convergent evolution of plasticity in morphology and its impact on cell adhesion as a critical adaptive trait for pathogenesis .
Adaptation to the host environment by many eukaryotic pathogens is often companied by transition in cellular morphology [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] . The ubiquitous fungal pathogen Cryptococcus neoformans causes more than half a million deaths each year [10] . It can grow in the yeast form as well as the filamentous form . Earlier pre-genetic studies indicate an inverse relationship between filamentation and virulence [11] , [12] , [13] , [14] , [15] , [16] , [17] . These studies also point to the potential of filament-specific antigens as vaccines against Cryptococcus infections [18] , [19] , [20] . Because Cryptococcus typically grows in the yeast form and the morphological transition from the yeast form to the filamentous form appears to be coupled with mating , signaling pathways that lead to bisexual mating ( a-α mating ) and unisexual mating ( mostly α-α mating ) have been intensively investigated [21] , [22] , [23] , [24] . The roles of these signaling components in fungal pathogenicity are also scrutinized in animal models . However , accumulating evidence indicates that key signaling components that specifically lead to mating , such as those in the pheromone sensing pathway , have no or minimal direct effect on virulence [25] , [26] , [27] , [28] . Furthermore , conditions relevant to host physiology ( e . g . aqueous environment , high temperatures , and high levels of CO2 ) are mating-suppressive , suggesting sex-independent mechanisms in orchestrating morphotype and virulence in Cryptococcus [29] . Therefore , the existence and the nature of the link between morphological transition and virulence in Cryptococcus remain enigmatic .
Although Cryptococcus morphological transition from the yeast form to the filamentous form is historically associated with mating , the observations that filamentation can be achieved in strains in the absence of key pheromone signaling components or meiotic genes [30] , [31] , [32] , [33] , lead us to hypothesize that pheromone signaling pathways are not essential or sufficient for filamentation per se , but they are critical in stimulating filamentation in response to mating cues . To test this hypothesis , we decided to examine the effect of constitutive activation of the pheromone signaling circuit on morphogenesis under mating-inducing and mating-suppressing conditions . It is known that the expression of genes in the pheromone signaling pathway , such as those encoding the pheromone Mf1α , the pheromone receptor Ste3α , the pheromone transporter Ste6 , and the key pheromone response regulator Mat2 ( Figure 1A ) , is low under mating-suppressing conditions but is dramatically higher during a-α bisexual mating ( Figure 1B and data not shown ) [30] , [32] . We found that the expression level of these pheromone signaling genes in wildtype α strain H99 alone was low when cells were cultured on either mating-inducing condition ( V8 agar ) or mating-suppressing conditions ( YPD agar and serum ) ( Figure 1B , C , D , E and F ) . This is consistent with the well-noted poor ability of the H99 strain to undergo unisexual mating . In fact , filamentation has never been observed when H99 was cultured alone under mating-inducing conditions ( Figure 2A ) [34] . We chose this strain to study the link between morphogenesis and virulence because H99 is one of the most virulent clinical strains tested in various animal models and it is also widely used as a reference strain in Cryptococcus research . When we placed the MAT2 gene under the control of the constitutively active promoter of GPD1 ( glycerol-3-phosphate dehydrogenase 1 ) and introduced this construct to H99 , the transcript level of MAT2 was dramatically increased under mating-inducing as well as mating-suppressing conditions ( Figure 1C ) . As expected for a key regulator of the pheromone signaling , overexpression of MAT2 led to high expression of MF1α , STE3α , and STE6 under both mating-inducing and mating-suppressing conditions ( Figure 1D , E and F ) . This result indicates that constitutively overexpression of MAT2 is sufficient to induce pheromone signaling circuit independent of mating cues . We next tested the effect of activation of pheromone signaling on filamentation under different conditions . The PGPD1-MAT2 conferred filamentation to H99 during unisexual mating ( α cells alone ) and it significantly enhanced filamentation during bisexual mating ( a-α coculture ) under the mating-inducing condition ( Figure 2A ) . However , under mating-suppressing conditions , overexpression of MAT2 failed to stimulate filamentation in the α alone culture or in the a-α coculture ( Figure 2A ) , and this was not due to insufficient activation of pheromone signaling . Effective activation of pheromone signaling in the PGPD1-MAT2 strain is supported by both the high expression levels of genes involved in pheromone signaling ( Figure 1D , 1E , and 1F ) and the formation of shmoo-like cells under both mating-inducing and mating-suppressing conditions ( Figure 2B ) . Shmoo-like cells are typically observed when cells respond to mating signals prior to cell fusion . These observations indicate that activation of pheromone signaling alone is not sufficient to initiate filamentation under mating-suppressive conditions , including conditions relevant to host physiology . Thus mating signaling is unable to coordinate the yeast-filament morphological transition and virulence during infections . We previously showed that the deletion of ZNF2 , which encodes a zinc-finger transcription factor , locked cells in the yeast form during mating without impairing pheromone signaling [28] . This suggests that Znf2 is not essential for mating signal relay; rather , it is crucial for filamentation . Although Znf2 functions downstream of Mat2 during mating [28] and its gene expression was significantly induced by MAT2 overexpression under the mating-inducing condition ( Figure 2C and 2D ) , activation of the pheromone signaling pathway was unable to induce ZNF2 expression in the absence of mating stimuli . This was evidenced by the low expression level of ZNF2 in the PGPD1-MAT2 strain under mating-suppressing conditions ( Figure 2C and 2D ) . The ability of Cryptococcus to undergo filamentation correlates with the expression level of ZNF2 , but not that of MAT2 ( Figure 1C , Figure 2A , 2C and 2D ) . Thus , Znf2 could be a master regulator that dictates Cryptococcus morphotype irrespective of environmental stimuli or mating type . To test this hypothesis , we constructed the PGPD1-ZNF2 strains . Indeed , the PGPD1-ZNF2 triggered filamentation in Cryptococcus strains of either mating types a or α in both serotype A and serotype D backgrounds under all tested conditions , including those that are inducing or suppressive to mating ( Figure 3A and Figure S1 ) . In contrast to the PGPD1-MAT2 strain , filaments produced by the PGPD1-ZNF2 strain under mating-inducing condition maintained their filamentous morphology after being transferred to mating-suppressive conditions ( Figure S2 ) . However , it is notable that the PGPD1-ZNF2 strain produces more robust hyphae under mating-inducing condition , suggesting that other factors induced under mating-inducing condition could further activate Znf2 . The PGPD1-ZNF2 also conferred filamentation to mutants that harbor deletions in the key mating components under various conditions tested ( Mfα1-3 , Mat2 , or Ste12 functioning in a branching pathway in pheromone signaling ) ( Figure 3B ) . To confirm that filamentation conferred by Znf2 activation is not due to some cryptic restoration of mating ability , we measured the efficiency of cell fusion of the wildtype , the mat2Δ mutant , and the mat2Δ+PGPD1-ZNF2 strain during bisexual a-α mating . Indeed , overexpression of ZNF2 did not rescue the cell fusion defects of the mat2Δ mutant ( Figure 3C ) . Consistently , gene ontology analyses of our previous transcription data indicate that Znf2 , unlike Mat2 , does not regulate genes involved in the cell fusion event critical for mating ( Figure S3 ) [28] . Taken together , the results indicate that filamentation can be independent of mating and Znf2 is one key determinant of this sex-independent morphogenesis . To verify the correlation of ZNF2 expression and Cryptococcus morphology , we constructed the ZNF2 gene driven by two inducible promoters: the galactose-inducible GAL10 promoter ( data not shown ) [35] or the copper transporter CTR4 promoter ( Figure 3D ) ( copper deprivation–on; copper repletion–off ) [36] . Transformation of the PGAL10-ZNF2 or the PCTR4-2-ZNF2 construct into wildtype either the serotype D reference strain JEC21 or the serotype A reference strain H99 conferred filamentous growth under promoter-inducing conditions . These strains grew as yeasts under promoter-repressive conditions ( Figure 3D and data not shown ) . Increasing the concentration of the copper chelator BCS ( inducer ) increased the frequency of filamentation in the PCTR4-2-ZNF2 strain ( Figure 3D ) , indicating that the expression level of ZNF2 dictates Cryptococcus cellular morphology . To examine the effect of ZNF2 on the dynamic morphological transition , we incubated the PCTR4-2 -ZNF2 strain in H99 background in liquid YPD medium containing 200 µM BCS ( inducer ) and examined cell morphology over time . Morphological transition from the yeast form to the filamentous form completed by 60 hours ( Figure 4 ) . At this time , the hyphae were transferred to YPD medium containing copper sulfate ( inhibitor ) . Cryptococcus cells then switched from the filamentous form to the yeast form over time ( Figure 4 ) . The control of bi-directional morphological transition by Znf2 is also observed when cells were cultured in serum ( data not shown ) , indicating that this control is independent of environmental cues . These results demonstrate that ( i ) the expression level of ZNF2 determines Cryptococcus cell morphology: high expression level of ZNF2 drives the cells to the filamentous form and low expression level of ZNF2 renders cells unicellular yeast; ( ii ) Znf2 is necessary and sufficient to initiate morphological transition; ( iii ) High Znf2 activity is required to maintain cells in the filamentous morphotype . The relationship between morphotype and pathogenicity is typically defined through studying morphological mutants that are otherwise isogenic to the wildtype strains and are able to maintain given morphotype under host relevant conditions , even though mutants with such extreme phenotypes are unlikely to be encountered clinically due to natural selections in the host [1] , [2] , [3] , [4] . For Cryptococcus , host physiological environment ( e . g . high body temperature , aqueous environment , and high levels of CO2 ) is extremely inhibitory to mating . Consistently , constitutively activated mating signaling induced filamentation under mating-inducing conditions , such filaments could not be maintained when transferred to in vitro conditions that mimicked host physiological environment ( Figure S2 ) . In contrast , the PGPD1-ZNF2 strain can readily initiate and maintain filamentous growth under such host-relevant conditions ( Figure S2 ) . Thus ZNF2 overexpression strains could serve as a model to investigate the relationship between morphotype and pathogenicity . We tested the virulence of the wildtype H99 and the PGPD1-ZNF2 strain in the murine inhalation model of cryptococcosis . The PGPD1-ZNF2 strain exhibited heterogeneity in cell morphology and a mixture of cell types is always present in this strain . To obtain accurate inoculation and to avoid potential problems caused by differences in cell types at initial infection , only cells in the yeast form were used for animal inoculation . Remarkably , the PGPD1-ZNF2 strain was completely avirulent ( Figure 5A ) . By day 60 post infection ( DPI 60 ) when the study was terminated , the PGPD1-ZNF2 cells were either completely cleared from animal lungs or existed in very low numbers ( 1000 fold lower than the original inocula ) . We further examined the fungal burden in the lungs and the brain of animals infected with H99 , the znf2Δ mutant , and the PGPD1-ZNF2 strain at DPI 10 before any animal succumbed to cryptococcosis . Consistent with the animal survival rates , the lung fungal burden in animals infected with the znf2Δ mutant and the PGPD1-ZNF2 strain was 236% and 0 . 6% respectively compared to those infected with the wildtype ( Figure 5B ) . The brain fungal burden showed a similar trend with larger variations due to individual differences in the timing of dissemination in this inhalation model ( Figure S4 ) , and no fungal cells were recovered from the brains of animals infected by the PGPD1-ZNF2 strain . To examine the effects of Znf2 on fungal morphology in vivo , we infected animals intranasally with H99 , the znf2Δ mutant , and the PGPD1-ZNF2 strain and performed histological examination of lung tissues at DPI 1 , 7 , and 12 . Remarkably , even though only yeast cells from the PGPD1-ZNF2 strain were used in the original inoculation into animals , lungs infected by the PGPD1-ZNF2 strain contained Cryptococcus cells of mixed morphology: yeast , pseudohyphae , and hyphae in all the time points examined ( Figure 5C and Figure S5 ) . This is consistent with the morphological heterogeneity of the PGPD1-ZNF2 strain in vitro ( Figure S2 ) . In comparison , only yeast cells were observed in the wildtype H99 or the znf2Δ mutant infected animals ( Figure 5C and Figure S5 ) . This histological examination indicates that activation of Znf2 can drive filamentation in vivo . Tolerance of host temperatures is a pre-requisite of fungal virulence . In some fungal pathogens , morphological changes are often a response to temperature and some morphological defective mutants lose the ability to cause diseases in mammalian hosts due to growth inhibition by high temperatures in vivo . To determine if alteration of virulence potential in the znf2 mutants are simply due to altered sensitivity to high temperature , we compared the growth of the wildtype H99 , the znf2Δ mutant , and the PCTR4-2-ZNF2 strain at 30°C and 37°C on a variety of media via the spot assay . No apparent growth defects were observed in the znf2Δ mutant or the ZNF2 overexpression strain when compared to the wildtype under the conditions tested ( Figure S6 ) . Furthermore , the observation that the ZNF2 overexpression strain was capable of amplification during early stages of infection based on the fungal burden time course experiment ( Figure S7 ) also suggests that factors other than growth inhibition by high temperature are mainly responsible for the effects of Znf2 on virulence . As morphological changes reflect changes in cell surface properties , we predict that Znf2 controls cell surface constitutes . One property likely regulated by Znf2 is cell adhesion , as supported by the following observations . First , increasing the ZNF2 expression led to increasingly wrinkled colony morphology and flocculation ( Figure 3D , and Figure 6A , B and C ) . Both phenotypes are likely caused by increased expression of flocculins ( adhesins or adhesion proteins ) , as previously shown in bacteria and in yeasts [37] , [38] . Second , aerial hyphae of the ZNF2 overexpression strains formed on solid media also tended to attach to each other , forming bundles ( Figure 6D ) , as observed in flocculated strains of the filamentous fungus Ashbya gossypii [39] . Third , deletion of ZNF2 impairs agar invasion whereas overexpression of ZNF2 remarkably promoted invasive growth ( Figure 6E ) , and invasive growth reflects cell-substrate adhesion . The results suggest that Znf2 plays a pivotal role in morphogenesis-associated cell flocculation in Cryptococcus . Given that Cryptococcus strains with increased flocculation are reduced in virulence [40] , [41] , this transcription factor likely impacts pathogenicity at least partly through its effects on cell adhesion . Ontology analysis of our previous transcriptional profiling data [42] revealed that of those genes that are differentially expressed in the znf2Δ mutants , 23% encode secretory proteins based on WoLF PSORT prediction ( http://wolfpsort . org/ ) ( Figure 7A ) . We selected 9 such genes and examined their transcript level in a ZNF2 overexpression strain incubated in serum at 37°C in 5% CO2 by quantitative realtime PCR . All genes tested were also differentially expressed in the ZNF2 overexpression strain ( Figure 7B ) . We overexpressed these 9 genes using the constitutively active GPD1 promoter and examined if their overexpression could recapitulate some of the phenotypes caused by the ZNF2 overexpression ( Figure 7C ) . Interestingly , strains with overexpression of CNAG_00795 ( designated as CFL1: Cell FLocculin 1 ) formed extremely wrinkled colonies , like ZNF2 overexpression strains ( Figure 6A ) . Interestingly , the expression of CFL1 was also most dramatically induced by the ZNF2 overexpression ( Figure 7B ) . Because acapsular Cryptococcus mutants also form wrinkled colony , we examined capsule production in the CFL1 overexpression strain and cfl1Δ mutants . No apparent defect in capsule production was detected based on microscopic examination ( data not shown ) . To confirm that cell adhesion is indeed caused by increased CFL1 expression , we then constructed PCTR4-2-CFL1 strains . These strains grew as yeast cells in liquid cultures . A sharp increase in cell aggregation was observed when PCTR4-2-CFL1 cells were cultured under promoter-inducing conditions , a reminiscence of some of the phenotypes of the PCTR4-2-ZNF2 strains ( Figure 7D ) . To further confirm that CFL1 is regulated by Znf2 , we engineered a reporter strain where ZNF2 expression is inducible by galactose and the fluorescent Cfl1 is driven by its native promoter . We grew the reporter strain under mating-suppressing conditions to avoid complication due to potential activation of mating signaling . Under such conditions , the colony formed by the reporter strain became fluorescent and wrinkled when the ZNF2 expression was induced in the presence of galactose ( Figure 8A and B ) , while the colony was non-fluorescent and smooth when the ZNF2 expression was inhibited in the presence of glucose ( Figure 8A and B ) . Thus the expression of the fluorescent Cfl1 is driven by Znf2 . Taken together , Znf2 triggers morphological switch as well as flocculation ( cell adhesion ) , and its downstream factor Cfl1 regulates cell adhesion . We examined the sub-localization of Cfl1 using a strain harboring the mCherry fused Cfl1 protein driven by its native promoter . Because Cfl1 is induced during mating and controlled by key components of mating signaling ( Figure S8A and B ) , we examined microscopically the expression of CFL1-m-cherry during mating . Cfl1 was rarely detected in yeast cells ( Figure 8C ) , but it was highly expressed in hyphae during both unisexual mating and bisexual mating ( Figure 8D ) . The fluorescent Cfl1 delineated the periphery of hyphal cells , consistent with the function of adhesins on the cell surface and the prediction that Cfl1 is a secretory protein based on the presence of an N-terminal signal peptide for secretion . Secretion is required for Cfl1's function as an adhesin . This is supported by the observation that overexpression of the fluorescent Cfl1 that lacks the N-terminal signal peptide [Cfl1 ( sigPΔ ) -mCherry] failed to confer wrinkled colony morphology or cell aggregation to Cryptococcus ( Figure S9 and Figure 8E ) . This is not due to a failure of producing the mutant allele protein , as abundant Cfl1 ( sigPΔ ) -mCherry protein was produced by the cells ( Figure 8E ) . However , no fluorescence was detected from the culture supernatant ( Figure 8F ) , indicating defects in secretion . A few other fungal adhesins are also reported to be associated with cell surface as well as being released into surrounding environment [43] , [44] . Such property may facilitate their roles in mediating both cell-cell adhesion and cell-substrate adhesion , and it may also help circumvent the blockage by other extracellular components . Consistent with its role as an adhesin , Cfl1 regulates a broad spectrum of cell adhesion-related biological processes , including complex colony morphology [45] , [46] and formation of different biofilms ( Figure S10 ) . Remarkably , deletion of CFL1 dramatically reduced hyphal production during either bisexual or unisexual mating while overexpression of CFL1 enhanced the hyphal formation ( Figure 9A and B ) . Thus , both the expression pattern of CFL1 and the observed effects of CFL1 deletion or overexpression on hyphal development indicate the importance of this adhesin in hyphal morphogenesis . Like the ZNF2 overexpression strain , hyphae formed by the PGPD1-CFL1 strain on YPD medium ( mating-suppressive ) tended to attach to each other , forming bundles ( Figure 6C and Figure 9A ) . Previous studies implicate an inverse association between flocculation and virulence in Cryptococcus [40] , [41] . Consistently , we found that overexpression of CFL1 resulted in attenuation in virulence , indicating that Cfl1-mediated cell adhesion negatively modulates virulence ( Figure 9C ) . Consistently , organ fungal burdens were maintained at low level in the PGPD1-CFL1 and PGPD1-ZNF2 infected animals at DPI 7 , whereas the wildtype H99 strain proliferated significantly ( Figure 9D ) . Unlike the PGPD1-ZNF2 strain , the PGPD1-CFL1 strain was not completely avirulent and the PGPD1-CFL1 strain proliferated significantly when examined at DPI 12 ( Figure S11 ) . This is surprising but not unexpected as the impact of ZNF2 overexpression is likely the combinational effect of additional adhesion proteins and morphogenesis factors . As noted for znf2 mutations , deletion or overexpression of CFL1 did not cause any apparent change in growth compared to wildtype when cultured at 37°C with 5% CO2 ( Figure S12A and B ) . Cells aggregated when CFL1 was overexpressed at both 30°C and 37°C as expected .
C . neoformans is the major fungal pathogen from the phylum Basidiomycota in the Kingdom Fungi . Its morphological differentiation is typically heterogeneous and stochastic , and has been historically associated with mating . Pheromone signaling is the master regulation system in fungal mating , and it is required for early mating events such as cell recognition , mating projection formation , and initiation of cell contact and cell fusion [47] , [48] , [49] . However , increasing evidence implies that filamentation in Cryptococcus is a plastic process that is not limited to mating or the production of recombinant progeny: Filamentation is occasionally observed under mating-suppressing conditions , even in some attenuated strains isolated from infected host tissues [50] , [51] , [52] , [53]; Filamentation can occur in the absence of some key components of pheromone signaling or meiosis machinery [30] , [32] , [33] , [54] . Thus , filamentation could be used in behaviors unrelated with mating , such as foraging nutrients or defending predation . Such sex-independent cellular differentiation likely involves signaling pathways in response to cues other than the mating signal . Here we show that sex-independent morphogenesis is linked with virulence in this fungus . We further demonstrate that the transcription factor Znf2 plays a pivotal role in cryptococcal morphological transition , and it is necessary and sufficient to drive filamentation irrespective of environmental cues , mating types , or pheromone signaling . Znf2 not only controls morphogenesis in vivo , but also the ability of this fungus to cause diseases . Thus Znf2 provides the key link between morphogenesis and virulence in Cryptococcus . The exact mechanism by which Znf2 controls morphogenesis and links Cryptococcus pathogenicity is of great interest . Previous and this current in vitro studies indicate that Znf2 does not affect typical Cryptococcus virulence traits ( e . g . melanization , capsule production , growth at high temperatures , growth in minimal media , and resistance to salt or H2O2 [42] , [55] ) . Although the PGPD1-ZNF2 strain is avirulent , this strain was capable of propagation during the first two weeks of infection ( Figure S7 ) . This is in contrast with other avirulent strains such as cna1 or capsule mutants , which are less fit under various stress conditions and are rapidly cleared by the host [56] , [57] . These lines of evidence point to new traits regulated by Znf2 that influence pathogenicity . Our observation that genes encoding secretory proteins are enriched within the regulon of Znf2 emphasizes the importance of changes in cell surface during morphogenesis . Given that Cryptococcus strains with increased flocculation have been noted to be reduced in virulence [40] , [41] , Znf2 likely impacts pathogenicity at least partly through its effects on cell adhesion ( flocculation ) . Cell adhesion mediated by microbial pathogens usually involves a repertoire of extracellular adhesion proteins . One of Znf2's downstream factors , Cfl1 , is a prominent adhesion protein which orchestrates filamentation , cell adhesion , and virulence . To our knowledge , Cfl1 is the first Cryptococcus adhesin discovered . Interestingly , Cfl1 does not resemble any known adhesins characterized in ascomycetous fungi in terms of primary sequences and functional domains based on Pfam prediction ( http://pfam . sanger . ac . uk/ ) . There are four other homologues of CFL1 in the genome of Cryptococcus and in some other species in the phylum of Basidiomycota ( Figure S13 ) , in which no adhesin has been identified so far . This suggests that Cfl1 and its homologues represent a novel adhesion family specific to Basidiomycota . Unlike Znf2 , overexpression of CFL1 attenuates but does not abolish Cryptococcus virulence in the murine model of cryptococcosis . This is not unexpected as studies show that microbes are typically endowed with multiple adhesins . The master regulator Znf2 likely controls additional adhesins and other morphogenesis factors , and it is the orchestrated effects of its downstream targets that give rise to its overall impact on morphogenesis and virulence . Further characterization of Cfl1 , other adhesins , and morphogens downstream of Znf2 can help parse out the effects of cell morphotype and other cell properties ( e . g . changes in cell surface proteins like adhesins ) on Cryptococcus virulence . Such investigation may lay a foundation for future endeavors to develop vaccines or alternative therapies against cryptococcosis .
This study was performed according to the guidelines of NIH and Texas A&M University Institutional Animal Care and Use Committee ( IACUC ) . The animal models and procedures used have been approved by the Institutional Animal Care and Use Committee ( IACUC ) at Texas A&M University ( protocol number: 2011-22 ) . Strains used in this study are listed in Table S1 . For mating assays , parental strains ( a and α ) with equal number of cells were cocultured together on V8 medium in the dark at 22°C , and mating was examined microscopically for formation of mating hyphae and spores [58] . For cell fusion assays , the coculture of marked parental strains were removed after 48 hours of incubation on V8 medium , washed , and plated on selective media to select fusion products at 37°C as described previously [28] , [33] , [59] . For self-filamentation assays , cells were patched on V8 medium alone and hypha formation was examined microscopically . Phenotypical assays in vitro were performed as previously described [59] . The serotype A strain H99 is highly virulent and has been widely used in pathogenesis studies . Thus strains generated in this genetic background were used in the animal experiments and many of the in vitro characterization experiments . However , because wildtype H99 has not been observed to undergo unisexual mating and its bisexual mating is rather weak compared to the well-characterized but less virulent serotype D strains such as JEC21 and XL280 , strains generated in these genetic backgrounds were used in some of the morphogenesis and mating assays . Plasmids and primers used in this study are listed in Table S2 and S3 . For gene deletion , overlap PCR products with an appropriate selection marker connected with the 5′ and 3′ flanking regions of gene of interests were introduced into Cryptococcus strains by biolistic transformation and transformants with homologous replacement were selected as described previously [60] . For overexpression , genes were amplified by PCR and the amplified fragments were digested and inserted into pXL1 after the GPD1 promoter [61] . The PGPD1 of the resulting plasmids was replaced with either the PCTR4-2 or the PGAL10 to generate the copper or the galactose inducible system . The PCTR4-2 and the PGAL10 were amplified from the plasmid pNAT/CTR4-2 and H99 genomic DNA respectively [35] , [62] . Because Cfl1 contains a predicted secretory signal peptide at its N-terminus , the mCherry [63] was fused to the C-terminus . The fragment including CFL1 coding region and 1 kb upstream sequences ( NCfl1 ) was pieced together with the mCherry by an overlap PCR . The resulting products were introduced into plasmid pXL1 to generate pXL1-NCfl1-mCherryA ( for the serotype A H99 allele ) and pXL1-NCfl1-mCherryD ( for the serotype D JEC21 allele ) . The CFL1-mCherry without the CFL1 promoter was amplified and introduced into pXL1 to produce the plasmid pXL1-Cfl1-mCherry . The PGPD1 in pXL1-Cfl1-mCherry was replaced with the PCTR4-2 to generate plasmid pXC-Cfl1-mCherry . To construct overexpression of the fluorescent Cfl1 that lacks the N-terminal signal peptide [Cfl1 ( sigPΔ ) -mCherry] , primers primers Linlab948 and Linlab864 were used to generate CFL1 ( sigPΔ ) -mCherry allele and pXC-Cfl1-mCherry was used as the template . The resulting PCR product was introduced into pXC to produce pXC- Cfl1 ( sigPΔ ) -mCherry . Plasmids were linearized before introduced into relevant Cryptococcus strains . To examine the sub-cellular localization of Cfl1::mCherry , strains were grown on V8 agar medium at 22°C for 72 hrs before examined with a BX50 ( Olympus ) microscope . Total RNA was purified using the purelink RNA purification kit ( Invitrogen ) and was used as the template for the first strand cDNA synthesis using the Superscript III cDNA synthesis kit ( Invitrogen ) . Relative expression level of selected genes was measured by real time PCR using power SYBR qPCR premix reagents ( Invitrogen ) in a Realplex system ( Eppendorf ) . Primer efficiency was determined by serially diluting the cDNA and monitoring DNA amplification by real-time PCR . Primers for qPCR used in this study are listed in Table S3 . Gene-expression levels were normalized using the endogenous control gene TEF1 . The relative transcript levels were determined using the comparative CT method as described previously [64] . RNA was separated on agarose gels blotted to nylon membrane . Redi-Prime II kit ( Amersham ) was used to generate probes . The C . neoformans actin gene transcript served as a control . mRNA purification was performed using the PolyATtract mRNA Isolation System III ( Fisher ) according to the manufacture's instruction . The cells were cultured in 96-well microtiter plates under a variety of growth conditions . The air-liquid interface biofilm was only observed in CFL1 overexpression strains . The strains were grown in YPD liquid medium for 8 days . Crystal violet method was used for the quantitative assessment of the ability of Cryptococcus strains to form biofilm as previously described [65] . Animals were infected essentially as previously described [59] , [66] . Groups of 6- to 8-week-old female A/J mice ( Jackson Labs ) were infected intranasally with 1×105 Cryptococcus cells in 50 µl PBS . For the PGPD1-ZNF2 strain , the culture of cells with mixed morphotype was centrifuged briefly at a low speed to allow the enrichment of yeast cells on the top . The top culture was then centrifuged again and only yeast cells were collected for infection . Ten mice per group were used for survival studies , and four or five were used for organ fungal burden studies and histological examinations . For organ fungal burden studies , fungal CFUs from lungs , kidneys , spleen , and the brains of sacrificed mice at each time point were measured as described previously [59] , [67] . Dunnett's two-tailed t test was used to test statistical differences ( P≤0 . 05 ) . For histological examinations , organs from the sacrificed animals were fixed in 10% formalin , embedded in paraffin , sectioned at 5 µm in thickness , and stained with hematoxylin and eosin ( H&E ) and Gomori methenamine silver ( GMS ) as previously described [56] , [68] . For mortality studies , the infected animals were monitored until all mice were sacrificed due to sickness or up to DPI 60 when the experiment was terminated . If the experiment was terminated , surviving animals were examined for the presence of Cryptococcus cells . Statistical significance ( P≤0 . 05 ) of the survival data between different groups was assessed by the Mantel-Cox log-rank test [69] . C . neoformans var . grubii ( H99 ) : ZNF2 ( CNAG_03366 ) ; MAT2 ( CNAG_06203 ) ; STE3α ( CNAG_06808 ) ; STE6 ( CNAG_03600 ) ; MF1α ( CNAG_07407 ) , CFL1 ( CNAG_00795 ) and other secretory protein encoding genes controlled by Znf2 ( CNAG_00596 , CNAG_00925 , CNAG_01211 , CNAG_05778 , CNAG_07422 , CNAG_06239 , CNAG_06411 , CNAG_05729 ) , KEL1 ( CNAG_01149 ) , CDC10 ( CNAG_01373 ) , CDC12 ( CNAG_01740 ) , cnCDC11 ( CNAG_02196 ) , cnMUC1 ( CNAG_03234 ) , cnCDC24 ( CNAG_04243 ) , cnCDC3 ( CNAG_05925 ) . C . neoformans var . neoformans ( JEC21 ) : ZNF2 ( CNG02160 ) , MAT2 ( CNM02020 ) , STE12α ( CND05810 ) , CFL1 ( CNA07720 ) . ( Gene ID numbers were obtained from either NCBI Entrez or the Cryptococcus genome website at the Broad Institute http://www . broadinstitute . org/annotation/genome/cryptococcus_neoformans/MultiHome . html ) | Although morphogenesis and virulence are commonly associated in many eukaryotic pathogens , the nature of such association is often unknown . For example , Cryptococcus neoformans , a fungal pathogen that causes cryptococcal meningitis , typically undergoes morphological transition between the yeast and the filamentous form during mating . However , molecules that are critical for mating do not directly impact fungal virulence . Thus , the nature of the long observed association between morphotype and virulence in this microbe remains elusive despite decades of effort . Here we demonstrate that constitutively activated pheromone signaling is insufficient to drive morphological transition under mating-suppressing conditions , including those relevant to host physiology . Rather , we demonstrate that sex-independent morphological switching is driven by the transcription factor Znf2 and this regulator controls the ability of this fungus to cause disease . Znf2 governs Cryptococcus morphotype and virulence potential at least partly through its effects on cell surface proteins . One novel adhesin Cfl1functions downstream of Znf2 and it orchestrates morphological switch , cell adhesion , biofilm formation , and pathogenicity . Thus , cell adhesion at least partly underlies the link between morphological transition and pathogenicity in C . neoformans . Our findings provide a platform for further elucidation of the impact of morphotype on virulence in this ubiquitous pathogen . The discovery of Cfl1 and other novel adhesins in Cryptococcus could lay a foundation for the development of vaccines or alternative therapies to combat the fatal diseases caused by this fungus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbiology",
"cell",
"differentiation",
"developmental",
"biology",
"morphogenesis",
"mycology",
"cell",
"adhesion",
"microbial",
"pathogens",
"biology",
"pathogenesis",
"cell",
"biology",
"sexual",
"differentiation",
"genetics",
"molecular",
"cell",
"biology",
"gene",
"networks",
"genetics",
"and",
"genomics"
] | 2012 | The Link between Morphotype Transition and Virulence in Cryptococcus neoformans |
High epilepsy prevalence and incidence were observed in onchocerciasis-endemic villages in the Democratic Republic of Congo ( DRC ) . We investigated the clinical characteristics of onchocerciasis-associated epilepsy ( OAE ) , and the relationship between seizure severity and microfilarial density . In October 2017 , ivermectin-naive persons with epilepsy ( PWE ) were recruited from onchocerciasis-endemic areas in the Logo health zone in the DRC . Additional PWE were enrolled in the Aketi health zone , where ivermectin had been distributed annually for 14 years . Past medical history , clinical characteristics and skin snips for Onchocerca volvulus detection were obtained from participants . Bivariate and multivariable analyses were used to investigate associations with microfilarial density . Of the 420 PWE in the Logo health zone , 392 were skin snipped ( 36 . 5% positive ) . Generalized motor seizures were most frequent ( 392 PWE , 93 . 3% ) , and nodding seizures were reported in 32 ( 7 . 6% ) participants . Twelve PWE ( 3 . 1% ) presented Nakalanga features . Sixty-three ( 44 . 1% ) skin snip-positive PWE had a family history of epilepsy , compared to only 82 ( 32 . 9% ) skin snip-negative PWE ( p = 0 . 027 ) . Eighty-one onchocerciasis-infected PWE were recruited in the Aketi health zone . Positive correlations between seizure frequency and microfilarial density were observed in Logo ( Spearman-rho = 0 . 175; p<0 . 001 ) and Aketi ( Spearman-rho = 0 . 249; p = 0 . 029 ) . In the multivariable model adjusted for age , gender , and previous treatment , high seizure frequency was associated with increasing microfilarial density in Aketi ( p = 0 . 025 ) but not in Logo ( p = 0 . 148 ) . In onchocerciasis-endemic regions in the DRC , a wide spectrum of seizures was observed . The occurrence of Nodding seizures and Nakalanga features , as well as an association between seizure severity and O . volvulus microfilarial density suggest a high OAE prevalence in the study villages . ClinicalTrials . gov NCT03052998 .
As early as the 1930s , onchocerciasis was already suspected to cause seizures [1] . A meta-analysis has reported a 0 . 4% increase in epilepsy prevalence , for every 10% increase in onchocerciasis prevalence [2] . Today , there is increasing evidence that onchocerciasis is a risk factor for epilepsy [3–6] and that proper onchocerciasis elimination strategies can reduce the incidence of onchocerciasis-associated epilepsy ( OAE ) [7] . However , the physiopathology explaining how Onchocerca volvulus ( the parasite responsible for the clinical manifestations of onchocerciasis ) may cause seizures remains unclear . Recent studies in the Democratic Republic of Congo ( DRC ) have revealed a high epilepsy prevalence in hyper-endemic onchocerciasis foci , particularly where control measures are sub-optimal and transmission is ongoing [8–11] . Although specific phenotypic features of OAE such as nodding seizures ( repeated , involuntary forward bobbing of the head with reduced consciousness ) and Nakalanga syndrome ( growth retardation , dysmorphic features and cognitive decline ) have already been reported in the DRC [7 , 9] , the full clinical spectrum of OAE in the DRC remains unknown . In a bid to further elucidate the association between epilepsy and onchocerciasis , a randomized clinical trial evaluating the effect of ivermectin on the frequency of seizures in persons with epilepsy ( PWE ) living in the Logo health zone was initiated in October 2017 [12] ( Trial Registration Number NCT03052998; available at: www . clinicaltrials . gov ) . During the recruitment phase of this trial , all consenting PWE were examined and skin snipped to assess eligibility criteria . This paper describes the clinical features observed in ivermectin-naïve PWE encountered during the trial . Additional data to investigate the relationship between seizures and infection with O . volvulus were obtained from the Aketi health zone , another hyper-endemic onchocerciasis focus in the DRC with high epilepsy prevalence [10] .
We carried out a cross-sectional , descriptive study of PWE in the Democratic Republic of Congo . The study was conducted in two health zones in the DRC , namely Logo ( in the Ituri province ) and Aketi ( in the Bas-Uélé province ) . In the Logo health zone , five onchocerciasis-endemic health areas where community-directed treatment with ivermectin ( CDTI ) had never been implemented were selected: Draju , Kanga , Tedheja , Ulyeko and Wala ( Fig 1 ) . In the Aketi health zone , the study sites had already benefited from 14 years of CDTI and included Wela , Makoko , and Aketi rural town . The ecology and setting was similar in all study sites; these were essentially rural communities , with several fast-flowing rivers providing suitable breeding grounds for the blackflies ( Simulium spp ) , vectors of O . volvulus . The main economic activity of the residents was farming .
A total of 420 PWE in the Logo health zone were enrolled in the study ( age range: 1–72 years ) . Skin snip data was available for 392 ( 93 . 3% ) participants; of these , 143 ( 36 . 5% ) had detectable MF ( Table 1 ) . The mean MF density was 23 . 2 MF/skin snip , with median: 0 ( IQR: 0–9 . 6 MF/skin snip ) . Epilepsy duration ranged from 0–53 years , with a median of 7 years ( IQR: 3–14 ) . In 51 ( 12 . 3% ) participants , the duration of epilepsy was ≤1 year ( new cases of epilepsy ) . The median age for epilepsy onset was 11 years , with 308 ( 73 . 3% ) PWE experiencing the first epileptic seizure between 3–18 years ( Fig 2 ) . Generalized motor seizures were reported in 392 ( 93 . 3% ) PWE , and included 388 ( 92 . 1% ) with generalized tonic-clonic seizures , 2 ( 0 . 5% ) generalized myoclonic seizures , 2 ( 0 . 5% ) generalized atonic seizures ( “drop attacks” ) , and 1 ( 0 . 2% ) generalized tonic seizures . Nodding seizures were reported in 32 ( 7 . 6% ) participants . One hundred and sixty-five ( 39 . 3% ) PWE experienced more than one seizure type . Table 2 summarizes the clinical presentations of participants in the Logo health zone , stratified by skin snip status; the denominators may vary for the different parameters because of missing data . Among the 284 PWE ( 67 . 6% ) who met the OAE diagnostic criteria , 110/275 ( 40 . 0% ) and 99/150 ( 39 . 8% ) were positive for skin snips and Ov16 rapid tests , respectively . Only 258 of these OAE participants had complete data for both Ov16 and skin snip results , and 147 ( 57 . 0% ) of them were positive for at least one onchocerciasis test . The monthly seizure frequency among PWE who met the OAE criteria ( 2 . 0 , IQR: 1 . 0–4 . 0 ) was higher than for non-OAE PWE ( 1 . 5 , IQR: 1 . 0–2 . 0 ) ; p = 0 . 007 . Moreover , a higher mean MF density was observed among the PWE who fulfilled the OAE criteria ( 25 . 3 MF/skin snip ) compared to other participants ( 18 . 4 MF/skin snip ) ; p = 0 . 021 . Nodding seizures were reported in 32 ( 7 . 6% ) PWE . When compared with PWE without a history of nodding seizures , PWE with nodding seizures were younger ( median ages: 16 . 0 years ( IQR: 13 . 0–19 . 0 ) vs 20 . 0 years ( IQR: 14 . 2–29 . 0 ) ; p = 0 . 01 ) , had a higher seizure frequency ( 3 . 0 seizures/month ( IQR: 2 . 0–16 . 2 ) vs 2 . 0 seizures/month ( IQR: 1 . 0–3 . 0 ) ; p<0 . 001 ) , were more often cognitively impaired ( 71 . 9% vs 31 . 2%; p<0 . 001 ) , and had a higher prevalence of delayed secondary sexual development ( 11 . 1% vs 2 . 5%; p = 0 . 01 ) . Age at seizure onset was not significantly different among participants who reported nodding seizures ( age at onset: 9 . 5 years; IQR: 6 . 0–12 . 0 ) compared to those who did not ( 11 . 0 years; IQR: 7 . 0–17 . 0 ) ; p = 0 . 09 . Twelve PWE presented with Nakalanga features ( Table 3 ) ; in all those for whom the age at epilepsy onset was known , the first seizures appeared between 3 and 12 years . Two thirds ( 8/12 ) of PWE with Nakalanga features were positive for at least one onchocerciasis test . Table 4 summarizes the past history of PWE in the Logo health zone . Overall , 136 probable neurological events were reported prior to epilepsy onset , of which 62 ( 45 . 6% of the events ) were seizures with fever . Of the 288 PWE who reported ever taking anti-epileptic drugs ( AED ) , the molecules used included: phenytoin ( 91 PWE , 31 . 6% ) , phenobarbital ( 13 PWE , 4 . 5% ) and carbamazepine ( 1 PWE , 0 . 3% ) . The remaining participants could not recall the name of the AED used . Participants with a family history of epilepsy had more positive skin snips ( 44 . 1% vs 32 . 9%; p = 0 . 027 ) and higher mean MF densities ( 31 . 7 MF/skin snip vs 18 . 2 MF/skin snip; p = 0 . 007 ) when compared with PWE without a relevant family history . Different seizure triggers were identified , including food , cold weather , and storms ( Fig 3 ) . Eight of the nine PWE ( 88 . 9% ) who reported food as a trigger were experiencing nodding seizures . Correlation analysis showed a positive relationship between seizure frequency and MF density among PWE in the Logo health zone: Spearman rho: 0 . 175; p<0 . 001 ( Fig 4A ) . The multivariable analysis did not show an association between MF density and seizure frequency ( Table 5 ) . Eighty-one onchocerciasis infected PWE ( 50 . 6% males ) were recruited in the Aketi health zone; median age: 17 years ( IQR: 15–20 ) . There was one PWE ( 1 . 2% ) who experienced nodding seizures in Aketi . The mean MF density was 47 . 0 MF/skin snip with median 10 . 5 ( IQR: 3 . 5–53 . 0 ) , significantly lower than the MF density of skin snip-positive PWE in Logo ( p = 0 . 014 ) . PWE in Aketi had fewer seizures ( 1 . 0 per month , IQR: 1 . 0–2 . 0 ) compared to onchocerciasis-infected PWE in Logo ( p<0 . 001 ) . CDTI coverage among the participants in the year prior to the study was 50/81 ( 61 . 7% ) , and 55 PWE ( 67 . 9% ) reported previous AED use . Correlation analysis showed a positive relationship between seizure frequency and MF density ( Spearman rho: 0 . 249 , p = 0 . 029; Fig 4B ) . After adjusting for age , sex , previous AED and ivermectin use , the seizure frequency of participants was still significantly associated with MF density; p = 0 . 025 ( Table 5 ) .
Our study has several limitations . Laboratory and imaging investigations to exclude other possible causes of epilepsy such as neurocysticercosis were not performed . However , previous studies had suggested that Taenia solium infection is not prevalent in the Logo Health zone [4] nor in the Bas-Uélé province [28] . In addition , the high proportion of PWE meeting the OAE criteria makes it unlikely for another infectious pathology to be the main reason behind the high epilepsy prevalence . Another limitation is the fact that seizure information and past history of participants were obtained by interviewing family members , and could be subject to recall bias . Absence seizures and some focal seizures which are more subtle may have been under-reported as a consequence . Moreover , cognitive function was not assessed using a validated series of tests . In conclusion , PWE in onchocerciasis-endemic villages in the Logo Health zone presented with wide clinical spectrum including generalized seizures , nodding seizures , Nakalanga features and other OAE characteristics . MF density was significantly and positively associated with seizure frequency in Aketi . It is expedient that onchocerciasis control measures be strengthened to prevent new OAE cases , while providing comprehensive care to confirmed PWE using appropriate AED and cognitive rehabilitation services . The possible added value of anti-filarial drugs in the treatment of OAE including nodding syndrome is currently being investigated [12 , 29] . | Several epidemiological surveys suggest that onchocerciasis ( a disease resulting from an infection with the parasite Onchocerca volvulus ) is a cause of epilepsy . We conducted a study to describe the clinical characteristics of persons with epilepsy ( PWE ) living in onchocerciasis-endemic villages in the Democratic Republic of Congo . In some study sites , the frequency of seizures increased with increasing number of O . volvulus microfilariae detected in the skin snips of participants . A wide spectrum of seizures was observed , including generalized tonic-clonic seizures , absence seizures , and focal seizures . Growth retardation and household clustering of PWE were common . Specific clinical presentations such as nodding seizures and Nakalanga features were encountered . These results suggest a high prevalence of onchocerciasis-associated epilepsy ( OAE ) in the study villages . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"invertebrates",
"cognitive",
"neurology",
"medicine",
"and",
"health",
"sciences",
"growth",
"restriction",
"onchocerca",
"volvulus",
"integumentary",
"system",
"helminths",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"onchocerca",
"neuroscience",
"pediatrics",
"epileptic",
"seizures",
"cognitive",
"neuroscience",
"child",
"growth",
"neglected",
"tropical",
"diseases",
"onchocerciasis",
"tonic-clonic",
"seizures",
"epilepsy",
"hair",
"cognitive",
"impairment",
"tonic",
"seizures",
"helminth",
"infections",
"eukaryota",
"anatomy",
"child",
"development",
"neurology",
"nematoda",
"biology",
"and",
"life",
"sciences",
"cognitive",
"science",
"organisms"
] | 2019 | Onchocerciasis-associated epilepsy in the Democratic Republic of Congo: Clinical description and relationship with microfilarial density |
Communication between neoplastic cells and cells of their microenvironment is critical to cancer progression . To investigate the role of cytoneme-mediated signaling as a mechanism for distributing growth factor signaling proteins between tumor and tumor-associated cells , we analyzed EGFR and RET Drosophila tumor models and tested several genetic loss-of-function conditions that impair cytoneme-mediated signaling . Neuroglian , capricious , Irk2 , SCAR , and diaphanous are genes that cytonemes require during normal development . Neuroglian and Capricious are cell adhesion proteins , Irk2 is a potassium channel , and SCAR and Diaphanous are actin-binding proteins , and the only process to which they are known to contribute jointly is cytoneme-mediated signaling . We observed that diminished function of any one of these genes suppressed tumor growth and increased organism survival . We also noted that EGFR-expressing tumor discs have abnormally extensive tracheation ( respiratory tubes ) and ectopically express Branchless ( Bnl , a FGF ) and FGFR . Bnl is a known inducer of tracheation that signals by a cytoneme-mediated process in other contexts , and we determined that exogenous over-expression of dominant negative FGFR suppressed and tumor growth . Our results are consistent with the idea that cytonemes move signaling proteins between tumor and stromal cells and that cytoneme-mediated signaling is required for tumor growth and malignancy .
Human tumors include transformed tumor cells , blood vessels , immune response cells , and stromal cells that together with the extracellular matrix ( ECM ) constitute a “tumor microenvironment” [1] . The tumor microenvironment is essential for oncogenesis , cell survival , tumor progression , invasion and metastasis [2 , 3] , and its stromal cells produce key drivers of tumorigenesis . Known drivers are growth factors ( e . g . HGF , FGF , EGF , IGF-1 , TGF-β and Wnts ) , cytokines ( e . g . IL-6 , SDF-1 ) and pro-angiogenic factors ( e . g . VEGF ) . It is not known if these proteins function as autocrine , juxtacrine , or paracrine signals , nor is it known how they might move into or within the tumor microenvironment . Studies of tumor models in Drosophila exploit the experimental attributes of the fly that provide uniquely powerful ways to investigate tumorigenesis [4] . We tested two models for the roles of cytonemes . Cytonemes are specialized , actin-based filopodia that extend between cells that produce and secrete signaling proteins and cells that receive them . The signaling proteins move along cytonemes and exchange at transient synapses that form where cytonemes contact target cells . These synapses are similar to neuronal synapses in constitution , structure and function [5–7] , and are necessary for paracrine FGF/Bnl , BMP/Dpp , Hedgehog , Wnt/Wingless ( Wnt/Wg ) , and Notch signaling during normal development of Drosophila epithelial tissues [5 , 7–9] . EGFR activating mutations are drivers of several types of human cancers [10] . However , elevated EGFR expression of wild type EGFR is not sufficient for tumorigenesis , and additional genetic changes are necessary , such as over-expression of Perlecan , a heparan sulfate proteoglycan ( HSPG ) component of the ECM [11] . In Drosophila , ectopic over-expression of Perlecan and EGFR in epithelial cells of the wing imaginal disc drives tumorigenesis [12] . Growth and metastasis of the epithelial cells require crosstalk with closely associated mesenchymal myoblasts , which also proliferate abnormally when Perlecan and EGFR are over-expressed in epithelial neighbors . The crosstalk includes BMP/Dpp signaling from the epithelial cells to the mesenchymal myoblasts [12] . The RET gene is the primary oncogenic driver for MEN2 ( multiple endocrine neoplasia type 2 ) syndrome . MEN2 is characterized by several types of neoplastic transformations , including an aggressive thyroid cancer called medullary thyroid carcinoma ( MTC ) . A fly model that overexpresses RETMEN2 phenocopies aspects of the aberrant signaling in MEN2-related tumors , such as activation of the SRC signal transduction pathway , which promotes migration and metastasis of tumorigenic cells . The relevance of the fly model has been established by screens for small molecule suppressors of Drosophila tumors driven by RETMEN2 over-expression . Several compounds that were identified are more effective than the drugs that are currently used for patients [13 , 14] . In the work presented here , we examined the role of cytoneme-mediated signaling in the EGFR-Pcn and RETMEN2 models . Genetic inhibition of cytonemes by downregulation of five genes that were shown previously to be essential in cytoneme-mediated signaling , reduced tumor growth , and we describe genetic conditions that suppress lethality by as much as 60% in the EGFR-Pcn tumor and by as much as 30% in the RETMEN2 tumor . Our results are consistent with the possibility that cytoneme-mediated signaling is necessary for tumor growth and that interfering with cytoneme-mediated tumor-stromal cell signaling might be a therapy for tumor suppression .
Most of the wing imaginal disc is a columnar epithelium that will generate the wing and cuticle of the dorsal thorax of the adult fly . The disc also includes myoblasts that grow and spread over much of the dorsal basal surface of the columnar epithelium; these mesenchymal cells will generate the flight muscles of the adult . Tracheal branches ( respiratory tubes ) also adjoin the basal surface of the columnar epithelium , and one branch , the transverse connective , sprouts a bud ( the air sac primordium ( ASP ) , Fig 1A ) that initiates growth during the third instar period . The ASP is dependent on Dpp and Bnl signaling proteins produced by the wing disc [7] . The myoblasts relay Wg and Notch signaling between the disc and ASP [9] . Cytonemes mediate and are essential for the Dpp , Bnl , Wg , and Notch signaling [15] . To investigate whether cytonemes are also essential in tumorigenesis , we tested a cancer model that requires tumor-stroma interactions in which neoplastic transformation is driven by interactions between the wing disc epithelial cells and myoblasts [12] . Overexpression of wild type EGFR and Perlecan ( pcn , a secreted heparan sulfate proteoglycan ) in the columnar epithelium drives proliferation of the genetically modified epithelial cells , as well as their genetically wild type myoblast neighbors . Tumorigenesis depends on Dpp signaling from the epithelial cells to the myoblasts . We first investigated if cytonemes are present in EGFR-Pcn overexpressing tumor cells . We induced the EGFR-Pcn tumor model ( with ap-Gal4 , an epithelial cell-specific driver ) together with CD8:GFP , a membrane-tethered GFP protein ( Fig 1C and 1D ) , and independently expressed membrane-tethered mCherry in the myoblasts ( with 1151-lexA , lexO-mCherry-CAAX; a myoblast-specific driver ) . In this system , the epithelial cell membranes are marked with GFP fluorescence and the myoblast membranes are marked with mCherry fluorescence . We observed that , as previously reported [12] , the EGFR-Pcn tumor induces overgrowth and proliferation , producing multilayered masses of disorganized disc epithelial cells and myoblasts ( [12] , Fig 1D ) . Higher magnification imaging detected both epithelial cell cytonemes and myoblast cytonemes . Some of the cytonemes appear to extend between the tumor and mesenchymal populations ( Fig 1E–1F’ ) . These results show that tumor cells and tumor-associated cells extend cytoneme-like structures and are consistent with the possibility that cytonemes may facilitate signaling between these cell populations . To monitor the tracheal branches that are associated with the tumorous discs , we induced the EGFR-Pcn tumor and labelled the trachea with membrane tethered GFP ( with LHG lexO-CD2:GFP , a tracheal-specific driver [16] ) . In the EGFR-Pcn tumor discs , the associated trachea were more extensive and branched than normal ( Fig 1G and 1H ) . Their overgrowth was presumably a response to the disc tumor . In normal development , Dpp produced by wing disc cells at the anterior/posterior compartment border is transported by cytonemes to target cells in both the wing disc and ASP , and cytoneme deficits caused by Capricious ( Caps ) , Neuroglian ( Nrg ) , or Diaphanous ( Dia ) loss-of-function lead to developmental defects [7] . In the EGFR-Pcn tumor model , Dpp signals from the genetically altered epithelial cells to drive myoblast expansion [12] . Dpp expression is upregulated in the epithelial cells ( Fig 2A and 2A’ ) and pMAD , the phosphorylated form of the Dpp signal transducer MAD , is enriched in the myoblasts ( Fig 2A” and 2A’” ) . This indication of Dpp signal transduction in the myoblasts is consistent with previous results showing that Dpp signaling in this stromal compartment is required for tumor growth [12] . To investigate how Dpp moves in the EGFR-Pcn model , we used CRISPR mutagenesis to tag the endogenous Dpp protein with mCherry . mCherry was inserted at codon 465 as described in [17] . Homozygous Dpp:mCherry flies that lack a wild type dpp gene are viable and develop as wild type , indicating that the Dpp:mCherry chimera has normal function . We induced the EGFR-Pcn tumor in Dpp:mCherry flies , and labeled the EGFR-Pcn tumor cells with CD8:GFP . Dpp:mCherry fluorescence was present in the cytonemes of the epithelial tumor cells ( Fig 2B , 2B’ and 2B” ) , consistent with the possibility that Dpp signaling is mediated by cytonemes in the EGFR-Pcn tumor model . To assess the role of cytonemes in tumorigenesis , we examined discs in which cytonemes are impaired . Downregulation of Nrg , Caps , SCAR , or dia decreases the number and length of cytonemes , and decreases signaling in tracheal cells , myoblasts and wing disc cells [7 , 9 , 18] . Nrg and Caps are cell adhesion proteins and SCAR and Dia are actin-binding proteins . Although severe loss-of-function conditions for Nrg , caps , SCAR or dia are lethal , the partial loss-of-function conditions we used and previously characterized do not perturb cell polarity , cell viability , or cell cycle during normal development [7 , 9 , 19 , 20] . Previous studies of the wing disc and associated tracheal cells and myoblasts identified cytonemes that either “send” signaling proteins from producing cells or “receive” signaling proteins from target cells , and reported cytonemes that link disc cells to each other or to tracheal cells or myoblasts [9 , 18 , 20–22] . Available genetic tools can be used to impair cytoneme function but they do not distinguish among these types of cytonemes . For the tumor discs with diminished Nrg , Caps , SCAR , or Dia , we compared disc morphology , Dpp signaling ( monitored by anti-pMad antibody staining ) , and myoblast distribution ( monitored by anti-Cut antibody staining , a marker of myoblasts [23] ) in three types of wing discs: control non-tumor discs , EGFR-Pcn tumor discs and EGFR-Pcn tumor discs that also expressed CapsDN or RNAi constructs targeting Nrg , Dia , or SCAR . These genotypes were generated from two crosses . In the first , EGFR-Pcn tumor discs were generated from a cross between ap-Gal4 , UAS-psqRNAi/CyO;UAS-EGFR , tub-Gal80ts and UAS-CD8:GFP that produces equal numbers of animals with the tumor genotype and non-tumor controls that have the CyO balancer and lack ap-Gal4 , UAS-psqRNAi . The animals were incubated to the 2nd instar stage at low temperature ( 18°C ) to permit repression of the transgenes by Gal80ts and were incubated at non-permissive temperature ( 29°C ) thereafter ( Fig 1C ) . The CyO control animals develop to late 3rd instar within one day and eclose in approximately four days as curly wing adults . All remaining animals developed tumors and were developmentally-delayed , and were analyzed after seven days of culture at 29°C . The second cross mated ap-Gal4 , UAS-psqRNAi/CyO;UAS-EGFR , tub-Gal80ts to flies with the respective “tester chromosome” carrying UAS-CapsDN or UAS-RNAi , and were incubated with the same regimen involving removal of CyO balancer adults . The remaining larvae had tumor phenotypes to varied degrees . Tumor discs were misshapen and approximately 6 . 3 times larger than control discs , their number of Cut-expressing cells increased by four times , and their anti-pMad staining was not patterned normally ( Fig 3A and 3B ) . In contrast , discs with tumor cells that expressed NrgRNAi in addition to EGFR and Pcn were morphologically less distorted , only 1 . 8 times larger than controls , and the number and distribution of Cut-expressing cells was close to normal ( Fig 3C and 3C’ ) . In these animals , expression of EGFR , Pcn , and NrgRNAi is driven by ap-Gal4 continuously after the second instar , but the noxious effects of NrgRNAi suppress the tumor phenotype induced by EGFR and Pcn overexpression and are tolerated by the disc cells in which NrgRNAi is expressed . The implication is that tumor cells are more sensitive to the consequences of Nrg downregulation than are normal cells . Hyper-sensitivity to sub-lethal levels of toxic conditions is a common hallmark of tumor cells . Expression of CAPSDN , SCARRNAi , or diaRNAi in the epithelial cells of the EGFR-Pcn model also reduced tumor growth , pMAD expression and number of Cut-expressing cells ( Fig 3D–3F’ ) . Expression of diaRNAi also suppressed excessive tracheation in the tumor discs ( Fig 3S ) . To test whether the suppressive , ameliorative effects might be additive , we expressed NrgRNAi and diaRNAi simultaneously in EGFR-Pcn tumor cells . We did not observe that the degree of tumor suppression changed relative to expression of either NrgRNAi or diaRNAi alone ( Fig 3G ) . We also tested the roles of three genes that are essential for planar cell polarity [24]: the dachsous ( ds ) and fat ( ft ) genes that encode cadherin family proteins , and four-jointed ( fj ) that encodes a transmembrane kinase . Expression of dsRNAi , ftRNAi or fjRNAi does not perturb cytoneme-mediated signaling between wing disc and tracheal cells [19] , and expression of these RNAi lines in the tumor cells had no apparent effect on tumorigenesis ( S2 Fig ) . Fig 3H summarizes the growth suppression we observed in the genetic conditions we tested . The presence of cytonemes in both the tumor columnar epithelial and mesenchymal myoblast cells , and the essential role of the myoblasts for tumor progression raises the possibility that myoblast cytonemes might also play an essential role in tumorigenesis . To investigate the role of myoblast cytonemes , we expressed diaRNAi ( with 1151-lexA lexO-diaRNAi ) in the myoblasts of discs that overexpress EGFR and Pcn in the columnar epithelial cells . The morphology , Dpp signaling pattern and myoblast growth characteristic of the EGFR-Pcn tumors were suppressed ( Fig 3I and 3I’ ) . This result is consistent with the idea that the myoblasts signal to the epithelial tumor cells [12] , and that this signaling is mediated by cytonemes . We also analyzed the apical-basal organization of the disc cells by monitoring the distribution of Discs large ( Dlg ) , which associates with the septate junction and localizes to the apical compartment of the columnar epithelial cells . Sagittal optical sections of discs stained with anti-Dlg antibody revealed that the specific apical distribution of Dlg characteristic of wild type cells is disorganized in EGFR-Pcn tumor discs ( Fig 3J and 3J’ ) . Expression of diaRNAi in the tumor cells restored the Dlg distribution to normal ( Fig 3J” ) . This demonstrates that expression of diaRNAi suppresses a critical feature of tumor cells and that downregulation of Dia is compatible with normal cellular morphology and behavior . Although EGFR and Pcn expression in the EGFR-Pcn model ( driven by ap-Gal4 ) is restricted to the dorsal compartment of the wing disc , the tumors grow extensively and metastasize ( Fig 4A ) . The tumorous condition is 100% lethal; animals with these tumors do not mature beyond the larval stage [12] . However , the conditions of Nrg , Caps , SCAR , or dia downregulation that suppress tumor growth also suppressed lethality: the number of EGFR-Pcn tumor-bearing larvae that pupated and that reached the pharate adult stage increased , and for the animals that expressed diaRNAi , approximately 60% survived to adult stage ( Fig 4B ) . These surviving adults were fertile , and wing blade morphological defects were the only visible phenotype ( Fig 4C and 4D ) . Given that cytoneme-mediated signaling is reduced by downregulation of Nrg , Caps , SCAR , or Dia , these results are consistent with the possibility that cytoneme-mediated signaling is necessary for tumor growth and that interfering with signaling either between tumor cells or between tumor and stromal cells suppresses many if not all aspects of tumorigenesis . The disc-associated ASP branch of the tracheal system is dependent on and sensitive to signals produced by the disc [25 , 26] , and Bnl signaling from the disc to the ASP is cytoneme-mediated and cytoneme-dependent [7 , 21] . Because tracheal branches grow excessively in the EGFR-Pcn tumor model ( Fig 1H ) , we investigated if Bnl signaling is upregulated in tumor discs . Bnl is normally produced by a small , discrete group of disc cells ( Fig 1A ) . Disc cells do not express Btl , but tracheal cells express Btl and not Bnl [26] . To monitor Bnl signaling in the EGFR-Pcn tumor model , we examined a Bnl reporter that expresses mCherry:CAAX in Bnl-expressing cells [27] . The number and location of Bnl-expressing cells increased in tumor discs ( Fig 5A and 5B ) . We also examined fluorescence of Btl:mCherry ( with a CRISPR-generated knock-in [21] ) . Whereas Btl:mCherry fluorescence was not detected in the epithelial cells of normal wing discs ( Fig 5C and 5C’ ) , Btl:mCherry fluorescence was present in many epithelial cells of the tumor ( Fig 5D and 5D’ ) . These results are consistent with the possibility that the tumor induces ectopic expression of Btl and that ectopic activation of the Bnl signaling pathway might correlate with excessive growth of the tracheal branches in this tumor . To investigate the role of Bnl signaling in EGFR-Pcn tumorigenesis , we overexpressed a dominant negative FGFR mutant in the tumor cells to block Bnl signaling ( UAS-BtlDN ) . We monitored wing discs for morphology , Cut expression , and pMAD in EGFR-Pcn tumor discs , and EGFR-Pcn tumor discs that also express BtlDN . Experimental crosses were carried out with the regimen described previously and produced either tumor larvae or suppressed tumor larvae; both crosses generate control ( CyO balancer ) and tumor-containing animals in a Mendelian ratio of 1:1 . In the cross with UAS-BtlDN , 50 . 6% ( 76/151 ) of the larvae pupated and eclosed within 4 days as curly wing adults . The presence of the balancer chromosome indicates that the genotype of these flies lacked ap-Gal4 , as expected of control , non-tumor animals . The remaining larvae do not develop beyond the pupal stage , consistent with their having the tumor genotype . Larvae analyzed after 7 days of Gal4 expression at the non-permissive were compared to tumor discs of the same age ( Fig 1C ) . In the EGFR-Pcn tumor discs that also express BtlDN , characteristics of tumor morphology , size , pattern of Dpp signaling , and distribution of myoblasts were suppressed ( Fig 5E and 5F ) . To confirm the identity genotype of the suppressed tumor discs , RNA isolated from wild type , EGFR-Pcn tumor , and BtlDN-expressing tumor discs was quantified by QPCR . This analysis confirmed the overexpression of EGFR in both tumor and suppressed tumor discs ( Fig 5G ) . We also examined EGF and Bnl signal transduction in tumor and suppressed tumor discs by staining with anti-dpERK antibody . The presence of dpERK was observed in control , tumor , and BtlDN -over-expressing control and tumor discs , and whereas the pattern of dpERK in the tumor discs was expanded and disordered in the tumor discs , the patterns and levels in the suppressed discs was close to normal ( Fig 5H–5K ) . These findings are consistent with the idea that tracheogenesis is necessary for tumor growth and with a previous report that describes comparable findings in studies of a lethal giant larvae Drosophila tumor model [28] . In this tumor , ectopic tracheal sprouting is associated with hypoxic responses and tracheal differential of wing disc tumor cells , a process that may be analogous to “sprouting angiogenesis” and vascular co-option in mammalian tumors [29] . We investigated the role of cytonemes in the Drosophila RET-MEN2 tumor model developed by the Cagan lab [14] . This model mimics the mis-regulation of signaling pathways that have been implicated in MEN2-related tumors . Overexpression of RETMEN2 in a discrete set of wing disc epithelial cells ( with ptc-Gal4 ) resulted in a >4X increase in the number of ptc-expressing cells and a 7X increase in the portion of the disc that consists of ptc-expressing cells ( Fig 6A and 6B ) [14] . Approximately one-half of the animals survive to the pupal stage , but none survive to adult . We tested whether expression of Irk2DN ( an inwardly-rectifying potassium channel required for cyteneme-mediated signaling [5] ) , diaRNAi , or SCARRNAi in the RET-mutant cells affects tumor growth and survival . We observed that excessive growth of the ptc-expressing cells was suppressed by more than 2X in all three genotypes ( Fig 6C–6F ) . Approximately two-thirds of the animals developed to the pupal stage , and survival to adult also increased ( Fig 6G ) . These flies have normal morphology , and with the exception of small wing vein abnormalities , the wings are indistinguishable from wild type ( Fig 6H ) . These results are consistent with a general role for cytonemes in tumorigenesis and tumor progression .
The tumor microenvironment is a niche that responds to signaling proteins produced by tumor cells and supplies growth factors that support tumor growth and metastasis [30] . Much ongoing work seeks inhibitors of tumorigenesis that target the signaling molecules and growth factors , their signal transduction pathways , and the stromal cells of the microenvironment [31 , 32] . Two previous studies reported cellular extensions of human tumor cells in ex vivo co-cultures with non-metastatic cells and in vivo , and have been implicated these structures in material transfer between tumor and non-tumor cells [33 , 34] . In this work , we also investigated the mechanism that transfers signaling molecules and growth factors between tumor cells and stromal cells in vivo , and report the first evidence for their essential role in tumorigenesis . Previous work established that during Drosophila development , paracrine signaling by the signaling proteins/growth factors Dpp , Bnl , Wg , Notch and Hedgehog , is mediated by cytonemes [35–37] . Cytonemes are specialized filopodia that extend between signal producing and signal receiving cells , making synaptic contacts where the signaling proteins transfer from producing to receiving cells . To extend this work to tumorigenesis , we applied the strategies and tools we developed for previous studies to ask if cytonemes are present in the tumor microenvironment , and if genetic conditions that inhibit cytoneme function and cytoneme-dependent signaling in normal development also inhibit tumorigenesis . In a EFGR-Pcn tumor model , we found that cytonemes extend from both Drosophila tumor and stromal cells ( Fig 1 ) . This is consistent with previous studies that reported increased signaling between tumor and stromal cells in this model [12] , and with the presence of cytonemes in many other contexts of paracrine signaling [7 , 18 , 20 , 38–41] . We confirmed that Dpp is expressed by the tumor cells ( Fig 2; [12] ) , and found that ectopic Bnl signaling also has an essential role in this tumor ( Fig 5 ) . These results imply functional connections between the EGF , Dpp , and Bnl signaling pathways in this tumor , and although we did not identify regulatory interactions between the pathways , our results show that ectopic activation of the Bnl pathway is essential to tumorigenesis . We also found conditions that impair cytonemes and rescue flies of lethal tumors in both EGFR-Pcn and RET models . We selected five genes from among the more than twenty that are known to be essential for cytoneme-mediated signaling [5 , 7 , 18 , 19] . nrg , caps , Irk2 , SCAR , and dia are recessive lethal genes whose functions can be partially reduced in genetic mosaics without affecting viability , cell shape , or the cell cycle , but are necessary for cytoneme function . Downregulating any one of these genes improved viability in the tumor models . dia downregulation is the most effective inhibitor of cytoneme-mediated signaling in other contexts [7 , 19 , 20] , and it is the most effective in both tumor models . The cures that downregulation effected suggest that cytoneme-mediated signaling , which might be a general mechanism for tumorigenesis in a variety of cancers , might also be a potential target for therapy . The high degree of evolutionary conservation of Drosophila and human proteins makes Drosophila a clinically relevant platform for understanding mechanisms human disease , and Drosophila tumor models have successfully identified new therapeutic candidates for colorectal , lung and thyroid and stem-cells derived cancers [42–44] . Our work provides proof principle for tumor suppression by interfering with cytoneme-mediated signaling .
Flies were reared on standard cornmeal and agar medium at 29°C , unless otherwise stated . ap-Gal4 UAS-psqRNAi/CyO; UAS-EGFR tub-Gal80ts from S . Cohen [12] , UAS-RETMEN2 from R . Cagan [14] , Btl:mCherry and Bnl-lexA , from S . Roy [21 , 27] , lexO-diaRNAi from H . Huang , UAS-CapsDN [45] ( deletion mutant lacking the intracellular domain ) , UAS-BtlDN from B . Shilo [46] ( dominant negative construct lacking a functional cytoplasmic tyrosine-kinase domain ) , Irk2DN from E . Bates [47] ( a subunit predicted to block the channel ) . btl-LHG , lexO-CD2:GFP , a tracheal-specific driver [16]; ptc-Gal4 enhancer is an enhancer trap line that mimics ptc expression [48] , lexO-mCherry:CAAX from K . Basler; lines from Bloomington Stock Center: 15B03-lexA ( #52486 ) , UAS-CD8:GFP ( #5137 ) , UAS-diaRNAi ( #28541 and #35479 ) , UAS-NrgRNAi ( #37496 ) , UAS-dsRNAi ( #32964 ) , UAS-ftRNAi ( #34970 ) , UAS-fjRNAi ( #34323 ) ; and UAS-SCARRNAi ( #21908 ) from Vienna Drosophila Research Center Stock Center . The Dpp:mCherry transgene has mCherry inserted C-terminal to Dpp amino acid 465 [17] , with Leu-Val linkers inserted before and after a mCherry coding sequence deleted of its stop codon . The transgene was generated by CRISPR mutagenesis as follows: Left homology arm fragment contains overlapping sequence with PBS-SK vector and mCherry . The mCherry fragment contains overlapping sequence with the left homology arm and right homology arm . The right homology arm fragment contains overlapping sequence with mCherry and PBS-SK vector . The three fragments were stitched together and cloned into PBS-SK vector using Gibson Assembly ( NEB ) . The resulting vector is designated as Dpp:Cherry donor vector . Left arm homology sequence was amplified from wild-type genomic DNA using: L-arm-fwd: cggtatcgataagcttgatcaccttgccgcacaaatacatatac L-arm-rev: CCTCGCCCTTGCTCACCATCTCCAGGCCACCGCCCTCTCCGGCAGACACGTCCCGA The mCherry tag was amplified using: mCherry-fwd:TGTCTGCCGGAGAGGGCGGTGGCCTGGAGATGGTGAGCAAGGGCGAGGAGGATAAC Cherry-rev:CGCTTGTTCCGGCCGCCCTTCTCTAACTTGTACAGCTCGTCCATGCCGC The right arm homology sequence was amplified from wild-type genomic DNA using: R-arm-fwd:GGACGAGCTGTACAAGTTAGAGAAGGGCGGCCGGAACAAGCGGCAGCCGA R-arm-rev:ccgggctgcaggaattcgatGTCATTATTCGGTTATGCTCTCGCTAG pCFD-3 gRNA vector gRNA sequence: CGCTCCATTCGGGACGTGTCTGG The gRNA sequence without the PAM was cloned into pCFD-3 vector obtained from Addgene . pCFD-3 gRNA vector and Dpp:mCherry donor vector were co-injected into Cas9 expressing flies ( nanos-Cas9 ) by Rainbow Transgenics . The resulting CRISPR-generated flies were screened and verified by sequencing . The Dpp:mCherry homozygous fly is viable and has normal morphology . The distribution of Cherry fluorescence in the wing disc is consistent with images in [17 , 49] , and the gradient of Cherry fluorescence in the columnar epithelial cells of the disc is intracellular ( S1 Fig ) . EGFR-Pcn tumors were induced as described by Herranz et al , [12] by overexpression of EGFR and down-regulation of pipsqueak ( psq ) , which leads to increased levels of Pcn . Female flies from the stock ap-Gal4 , UAS-psqRNAi/CyO;UAS-EGFR , tub-Gal80ts were crossed to males of the corresponding genotypes at 18°C , and were cultured at 18°C to maintain Gal80 repression of Gal4 and allow normal development . After 5 days larvae were transferred to 29°C to induce Gal4 expression and tumor growth . 4 days after the temperature shift CyO/+ flies eclosed and were removed from the vial . Tumor growth was induced for 7 days , unless otherwise indicated , whereupon larvae were dissected for live imaging or immunostaining , or were maintained at 29°C for survival studies . To control for possible effects on Gal4 expression , all tested genotypes had three UAS transgenes–either UAS-EGFR , UAS-psqRNAi and UAS-CD8:GFP for tumor flies , or UAS-EGFR , UAS-psqRNAi and additional RNAi for comparisons . Experimental and control crosses were carried out in parallel . Female flies from the RETMEN2 stock [14] were crossed at room temperature to either ptc-Gal4 , 2xUAS-CD8:GFP males or either with UAS-diaRNAi , or SCARRNAi or Irk2DN males . For analysis of discs , embryos from one day collections were transferred to 29°C and cultured to third instar stage . For survival comparisons , animals were cultured at 25°C . Wing discs with trachea attached were dissected in cold phosphate-buffered saline ( PBS ) , placed on a coverslip and mounted upside-down on a coverslip on a depression slide as described [9] . Samples were imaged with a Leica TCS SPE confocal or an Olympus FV3000 inverted confocal laser scanning microscope . Wing discs were dissected in cold PBS and fixed in 4% formaldehyde for 20 minutes . After extensive washing , the samples were permeablized with PBST ( PBS + 0 . 3% TritonX-100 ) , blocked for 1h with PBST+3%BSA blocking buffer , and incubated with primary antibodies previously diluted in blocking buffer overnight at 4°C . The following primary antibodies were used: α-pMad ( Abcam ) , α-Discs large ( Dlg ) , α-Cut and α-β-galactosidase ( Developmental Studies Hybridoma Bank ) . Secondary antibodies were conjugated to Alexa Fluor 405 , 488 , 555 , or 647 . Samples were mounted in Vectashield and imaged with a Leica TCS SPE confocal or an Olympus FV3000 inverted confocal laser scanning microscope . All measurements and quantifications of wing discs were done in z-section stacks of confocal images using Fiji software from 15–20 discs for each genotype . Total wing disc area and Cut-expressing cells in the EGFR-Pcn tumor or GFP-expressing cells in the RET tumor , were quantified by measuring the mean intensity of fluorescence relative to the total area of the wing disc . Data was normalized to control . Statistical significance values were calculated with Student’s t test . Total RNA was extracted from 5 wing discs of either wild type , EGFR-Pcn tumor or EGFR-Pcn tumor + BtlDN larvae using the RNeasy Micro Kit ( Quiagen ) . Larvae corresponding from 3 genotypes were under the same temperature conditions ( 5 days of tumor induction at 29°C ) . Reverse transcription was carried out using the Applied Biosystem High Capacity RNA-to-cDNA . qPCR reactions were performed with a BioRad C1000 Touch Thermal Cycler and SYBR Green ( Bioline ) . qPCR results were analyzed according to the comparative threshold cycle ( Ct ) method , where the amount of target , normalized to an endogenous actin reference and relative to an experimental control , is given by 2–ΔΔCt . Ct represents the PCR cycle number at which the amount of target reaches a fixed threshold . The ΔCt value is determined by subtracting the reference Ct value ( rp49 ) from the target Ct value . ΔCt was calculated by subtracting the ΔCt experimental control value . | The growth of many types of tumors depend on productive interactions with stromal , non-tumor neighbors , and although there is evidence that tumor and stromal cells exchange signaling proteins and growth factors that they produce , the mechanism by which these proteins move between the signaling cells has not been investigated and is not known . Our previous work has shown that normal cells make transient chemical synapses at sites where specialized filopodia called cytonemes contact signaling partners , and in this work we explore the possibility that tumors use the same mechanism to communicate with stromal cells . We show that cytoneme-mediated signaling is essential for growth of Drosophila tumors that model human EGFR over-expression and RET-driven disease . Remarkably , inhibition of cytonemes cures flies of lethal tumors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"paracrine",
"signaling",
"animals",
"epithelial",
"cells",
"endocrine",
"physiology",
"oncology",
"animal",
"models",
"developmental",
"biology",
"drosophila",
"melanogaster",
"model",
"organisms",
"stem",
"cells",
"connective",
"tissue",
"cells",
"experimental",
"organism",
"systems",
"drosophila",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"animal",
"studies",
"stromal",
"cells",
"endocrinology",
"life",
"cycles",
"connective",
"tissue",
"biological",
"tissue",
"dpp",
"signaling",
"cascade",
"myoblasts",
"insects",
"arthropoda",
"carcinogenesis",
"signal",
"transduction",
"eukaryota",
"cell",
"biology",
"anatomy",
"physiology",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"cell",
"signaling",
"larvae",
"organisms",
"signaling",
"cascades"
] | 2019 | Cytoneme-mediated signaling essential for tumorigenesis |
Dynamic network biomarkers ( DNB ) can identify the critical state or tipping point of a disease , thereby predicting rather than diagnosing the disease . However , it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual , which are generally not available , and thus limit the applicability of DNB . In this study , we developed a novel method , i . e . , single-sample DNB ( sDNB ) , to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient , thus opening a new way to predict diseases in a personalized way . In contrast to the information of differential expressions used in traditional biomarkers to “diagnose disease” , sDNB is based on the information of differential associations , thereby having the ability to “predict disease” or “diagnose near-future disease” . Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples . We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer , thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level .
Biomarkers , which are indicators of physiological states for living things , are commonly used to examine organ functions or disease states in biology or medicine . Generally , complex disease progression can be divided into three states , i . e . , normal , pre-disease and disease ( Fig 1A ) [1 , 2] , where the pre-disease state is the critical state or tipping point from the normal to disease state and is also the limit of the normal state just before the critical transition to the disease state . The pre-disease state is usually considered to be reversible to a normal state if appropriately treated [1 , 2] , in contrast to disease states such as cancer and diabetes that are generally difficult to return to the normal state . Thus , the pre-disease state is a crucial state during the disease progression . However , it is hard to be identified by traditional biomarkers due to its similarities to the normal state in phenotypes and expressions , i . e . , there are generally no significant differences between the normal and pre-disease states in terms of gene or protein expressions . Most traditional biomarkers are based on information about differential expressions , and thus mainly aim to distinguish the disease state from the normal state rather than diagnosing the pre-disease state before the onset of a disease . Therefore , identifying the pre-disease state , or the early-warning signals of the disease state , is an important challenge in medicine , and is not only beneficial for the early diagnosis and treatment of complex diseases but also provides dynamical insights into the molecular mechanism of complex diseases at a network level . To tackle this problem , the new concept of dynamic network biomarker ( DNB ) with its three statistical conditions was proposed to detect early-warning signals before disease onset at the molecular network level , and was applied to the analyses on various diseases [1–3] . Recently , our DNB model has also been adopted by many groups , successfully identifying the tipping points of cell fate decision [4 , 5] and further studying immune checkpoint blockade [6] . DNB theory suggests that a molecular module or DNB will appear at the critical state ( the pre-disease state or tipping point ) , and that this can be taken as an early-warning signal during the disease progression from normal to disease onset [1–3] . Specifically , we can theoretically prove that when a biological system from a normal state approaches the critical state , a DNB module or a group of molecules ( or variables ) appear and satisfy the following three statistic conditions [1–3]: The above three statistical conditions are generic features of critical states , which hold for general biological systems regardless of their detail differences . Thus , we can simplify these three conditions as an index Im of Eq ( 1 ) to evaluate the DNB module and detect the early-warning signals or the critical state in multiple samples as follows: Im=SDin⋅PCCinPCCout ( 1 ) where SDin is the average standard deviation ( SD ) for molecules inside the DNB module , PCCin is the average Pearson correlation coefficient ( PCC ) in absolute value for molecules inside the module , and PCCout is the average PCC in absolute value for molecules between the inner and outer molecules of the module . Clearly based on the three conditions , Im will drastically increase when the biological system approaches the critical state , and thus it can signal the immediate disease state or predict disease state . In contrast to the information of differential expressions widely used in traditional biomarkers to “diagnose disease” , DNB is based on the information of differential associations , thereby having the ability to “predict disease” or “diagnose the un-occurred disease” . DNB is a type of network biomarker , which can be used for the diagnosis of a pre-disease state rather than a disease state . In other words , DNB can be used for early diagnosis of a disease or to distinguish the pre-disease state from the normal state in complex diseases . According to the above three statistical conditions , DNB is clearly independent of differential expressions and is based on higher-order statistical information ( i . e . , the second-order moments ) rather than the first-order statistics ( i . e . , the mean values or first-order moments ) used for traditional molecular biomarkers . However , although the theory of DNB can ensure the recognition of early-warning signals of complex diseases , it requires multiple data samples to evaluate the three statistical conditions of DNB , which limits its application to clinical practice because multiple samples for each individual are generally not available . Here , by exploiting the high-dimensional information of the observed data ( e . g . , omics data ) and its differential distribution ( i . e . , volcano distribution ) [7] , we propose a novel method to identify DNB modules and critical states on a single-sample basis . In other words , this single-sample DNB ( sDNB ) method based on the volcano distribution can detect the critical state using only a single sample , thus having a wide range of applications on biology and medicine . One influenza virus infection dataset and three cancer metastasis datasets were used to validate the effectiveness and efficiency of this method for quantifying critical states or tipping points on a single-sample basis . For the influenza virus infection dataset , we obtained individual sDNBs , which identified the critical states or early-warning signals just before the onset of disease symptoms ( i . e . , the clinical symptoms of influenza , such as fever , runny nose , and sore throat ) [8]; no signal was detected in asymptomatic samples that showed few to no overt clinical symptoms of influenza [8] , with the exception of one false-positive sample . For the cancer metastasis datasets , our method detected the critical states before the distant metastasis stage ( stage IV ) in stage IIIB and stage III cancer samples . Functional enrichment analysis showed that the functions of sDNB genes are consistent with the phenotype of viral infection for influenza virus infection and cancer processes for cancer metastasis . The analyses of real data also provided biological insights into the molecular mechanisms of the critical transitions from the perspectives of both molecules and networks for these complex diseases . sDNB is the first such a method to predict the pre-disease state or quantify the tipping point based on only one sample . Note that , completely different from the traditional classification or machine learning methods which require a large number of case/control samples ( for supervised or unsupervised learning ) to obtain the predictor ( overlearning problem , population-based predictor ) , sDNB is a model-free method and does not require any learning process on sample data . In other words , the predictor “sDNB” is constructed by the three statistical conditions for each specific sample that are actually based on the essential dynamical features of critical states for general biological systems , and thus inherently has no overlearning problem ( even for a small sample size ) and in particular is an individual-based predictor .
Four datasets , including dataset GSE30550 [8] from the GEO database ( http://www . ncbi . nlm . nih . gov/geo/ ) and datasets on lung adenocarcinoma ( LUAD ) , stomach adenocarcinoma ( STAD ) , and thyroid carcinoma ( THCA ) from the TCGA database ( http://cancergenome . nih . gov ) , were used to validate the sDNB method . Dataset GSE30550 was normalized by the robust multi-array average ( RMA ) method , and the IDs of probe sets were mapped to the gene symbols . Probe sets without corresponding gene symbols were not considered in this study . The LUAD , STAD , and THCA datasets contained RNA-Seq data and included both tumor and tumor-adjacent samples . The tumor samples were divided into different stages based on clinical ( stage ) information from TCGA , while samples without stage information were ignored . We estimated sDNB score by using the three statistical conditions of DNB . Based on a number of reference samples ( e . g . , the dataset of normal or control samples ) , we can obtain expression distribution for each gene as its reference distribution . The expression of the gene in a new sample d ( e . g . , a case sample for statistical testing ) can be compared with its reference distribution to estimate the deviation of its expression from the reference samples ( n samples ) . The expression deviation of a gene in the new sample can be expressed as the distance from the expectation of its reference distribution ( Fig 1B ) . Specifically , as Condition 1 , the expression deviation of a gene in a single sample against n reference samples , i . e . , the single-sample Expression Deviation ( sED ) for gene x , can be defined as sED ( x ) =|x−x¯| ( 2 ) where x is the expression of gene x in the new single sample , and x¯ is the average expression value of gene x in the reference samples . We assumed the number of samples in reference data to be n , and thus the Pearson correlation coefficient ( PCC ) between two genes ( x , y ) in the reference sample data can be calculated as PCCn ( x , y ) =∑i=1n ( xi−x¯ ) ( yi−y¯ ) ∑i=1n ( xi−x¯ ) 2∑i=1n ( yi−y¯ ) 2 ( 3 ) where xi and yi are the expressions of gene x and gene y for the ith sample in the reference samples , respectively . x¯ and y¯ are the average gene expressions of gene x and gene y in the reference samples , respectively . PCCn ( x , y ) is the correlation between two genes ( x , y ) in n reference samples . After a new single sample is added to the reference samples ( Fig 1 ) , the new PCC can be calculated for the two genes by Eq ( 3 ) based on total n + 1 samples ( i . e . , n reference samples and one new sample d ) , i . e . , PCCn+1 ( x , y ) . The difference between PCCn+1 and PCCn for the two genes is caused by the new single sample added to the reference data ( Fig 1B ) , and hence it characterizes the specific correlation information of this single sample against the reference samples . Thus , as the conditions 2–3 , we define the single-sample PCC ( i . e . , sPCC ) of the two specific genes ( x , y ) against n reference samples as follows [7]: sPCC ( x , y ) =PCCn+1 ( x , y ) −PCCn ( x , y ) ( 4 ) which is clearly a differential PCC between n+1 samples and n samples . Since PCC follows the normal distribution , sPCC in Eq ( 4 ) follows the differential normal distribution with n common samples . The significance of sPCC can be evaluable by a statistical method or the volcano distribution , i . e . , the single-sample network theory [7] . Specifically , the “Z” score can be calculated for each sPCC by Eq ( 5 ) , and the p-value of each sPCC can be approximately obtained from the standard normal cumulative distribution based on the “Z” score . Hence , the significance of sPCC ( x , y ) for any two genes ( x , y ) can be evaluated by the p-value of the “Z” score from Eq ( 5 ) as follows: Z ( x , y ) =sPCC ( x , y ) ( 1−PCCn2 ( x , y ) ) / ( n−1 ) ( 5 ) where PCCn ( x , y ) is the Pearson correlation coefficient between two genes ( x , y ) in the reference samples , n is the sample size of the reference data , sPCC ( x , y ) is the differential PCC between PCCn+1 and PCCn for the two genes ( x , y ) in Eq ( 4 ) , Z ( x , y ) is the “Z” score of the Z-test for the two genes ( x , y ) , and the p-value can be calculated as the standard normal cumulative distribution function [9] . Note that we can directly evaluate the significance of Z based on the volcano distribution without approximation [7] . Also we can directly use Z ( x , y ) as the normalized differential PCC for the single sample without the statistical test . Actually , such an implementation can be considered as a new transformation from gene expression data to the correlation-like data for each sample . Therefore , based on the significant sPCCs of all pairs of genes or molecules , we can determine their corresponding network , which is perturbed by the single sample , and this network in turn also characterizes this single sample [7] . Based on the three statistical conditions of DNB shown in Eq ( 1 ) , the composite index or score Is of DNB ( with K genes or molecules ) to identify the pre-disease state for a single sample ( single-sample DNB: sDNB ) from all sED and sPCC in a module can be expressed as Is=sEDin⋅sPCCinsPCCout ( 6 ) where Is is the score for sDNB based on the single sample . Here , sEDin indicates the average expression deviation of all K genes in the sDNB module relative to the reference samples , sPCCin is the average correlation ( for all K2 pairs ) among whole genes in the sDNB module in absolute value , and sPCCout is the average correlation ( for all pairs ) between the K inner and ( n-K ) outer genes of the sDNB module in absolute value . Next , we describe how to determine the K genes or molecules of DNB module , which has the highest score of Is . The detail formulation or derivation of Eq ( 6 ) is given in S1 Text , where Is is shown to approximately represent the DNB score of the single sample . A potential sDNB module can be detected for every single sample against the reference data ( Fig 2 ) . Generally , each individual or sample has a number of modules , and the module with the highest score Is is the candidate DNB module for this specific sample . For a specific sample , we have the following algorithm to estimate the candidate DNB module . With such an algorithm , we can get the candidate sDNB for each sample one by one . If the sDNB for a sample during a biological process or disease progression has the highest score among all samples , this sample is considered to be in the critical state , and the corresponding period is also the critical period . Functional annotations were performed by searching the NCBI gene database ( http://www . ncbi . nlm . nih . gov/gene ) . The enrichment analyses were separately obtained using web service tools from the Gene Ontology Consortium ( GOC , http://geneontology . org ) and g:Profiler ( http://biit . cs . ut . ee/gprofiler/ ) and client software from INGENUITY IPA ( http://www . ingenuity . com/products/ipa ) .
In this study , four datasets , GSE30550 from the GEO database ( http://www . ncbi . nlm . nih . gov/geo/ ) and LUAD , STAD , and THCA from the TCGA database ( http://cancergenome . nih . gov ) , were chosen to validate the effectiveness of this method of quantifying the critical states of diseases . Dataset GSE30550 comprises expression profiles of humans with influenza virus infection . It contains data from 17 healthy adults who were inoculated with live influenza virus H3N2 and gene expression profiles for the 17 adults at 16 time points ( -24 , 0 , 5 , 15 , 21 , 29 , 36 , 45 , 53 , 60 , 69 , 77 , 84 , 93 , 101 , and 108 hours ) by microarray ( S1 Fig ) , i . e . , there are 17 samples at each time point , corresponding to the 17 adults , respectively . Nine of the 17 adults developed disease symptoms of influenza , and the other eight were asymptomatic ( S1 Fig ) . There are 11 , 961 probe sets and 17 samples in the original GSE30550 dataset , and 11 , 619 gene symbols were mapped from the ID of the probe sets . The gene expression values of the probe sets mapped to the same gene were calculated by an averaging operation . The gene expression profiles at -24 h ( 24 h before inoculating ) were deemed as the normal states of the samples without virus inoculation , and the profiles of 16 samples ( the data on sample 13 was lost at -24 h ) at -24 h were chosen as the reference dataset or reference samples ( S1 Fig ) . From the LUAD ( lung cancer ) , STAD ( stomach cancer ) , and THCA ( thyroid cancer ) datasets , 459 tumor samples and 58 tumor-adjacent samples were obtained for LUAD . The tumor samples were grouped into seven stages ( stage IA , IB , IIA , IIB , IIIA , IIIB , and IV ) of lung cancer ( Table 1 and S1 Table ) . One hundred fifty-six tumor samples and 33 tumor-adjacent samples were obtained for STAD . The tumor samples were grouped into seven stages ( stage IA , IB , IIA , IIB , IIIA , IIIB , and IV ) of stomach cancer ( Table 1 and S1 Table ) . Three hundred fifty-seven tumor samples and 58 tumor-adjacent samples were obtained for THCA . The tumor samples were grouped into four stages ( stage I , II , III , and IV ) of thyroid cancer ( Table 1 and S1 Table ) . The tumor-adjacent samples were considered as normal controls and were used as reference samples in this study . The gene expression profiles of samples at time point −24 h ( 24 hours before inoculation ) were chosen as reference samples , and totally 16 normal samples were included in the reference data . The candidate sDNB for each adult was identified by comparing the case sample with the reference samples . The threshold of sPCC was set to the p-value of 0 . 01 in the process of constructing the single-sample network ( Fig 2 ) , and the sDNB score was set to 2 . 0 for detecting the critical state , or early-warning signals , of the disease , or symptomatic , state for every sample . For all nine symptomatic samples , the scores Is of their sDNB modules were significantly high before the disease state , and thus correctly signaled the imminent emergence of the disease state ( before disease symptom appearance ) ( Fig 3A and 3C ) . In contrast , for the seven asymptomatic samples , no early-warning signal was detected based on the sDNB scores , but one asymptomatic sample ( s17 ) exhibited false-positive early-warning signals at later time points ( Fig 3B and 3C ) . Notably , all eight asymptomatic samples were Caucasian/White except sample s17 ( Indian ) who may have had a different threshold . Note that most of the subjects ( 14 of 17 samples ) in this dataset were Caucasian/White ( S1 Fig ) . Another possibility is that sample s17 did reach the critical state but returned to the normal state without further disease progression . For most adults ( 16 of 17 symptomatic and asymptomatic adults ) in dataset GEO30550 , our method could correctly detect critical states or early-warning signals before disease symptom appearance based on the sDNB scores ( single samples ) . False-positive warning signals for only one adult appeared in the asymptomatic samples , possibly due to the causes described above . The module size of sDNB in every symptomatic adult was different , e . g . , there were 1553 sDNB genes for adult s7 , 696 for adult s5 ( S2 Table ) , and only 350 for adult s10 , based on the same conditions ( S2 Table ) . The average number of overlapping genes between any two sDNBs is approximately 37 . 8% ( S4 Table ) . There were 25 overlapped genes among all nine sDNBs in the symptomatic samples ( S3 Table ) . Functional annotations were done for these overlapped genes by searching NCBI for Homo sapiens , and results are shown in S3 Table . Enrichment analysis of the 25 overlapped genes among all 9 sDNBs of symptomatic samples ( S3 Table ) was performed using web services in Gene Ontology Consortium and g:Profiler and the client software of IPA . The sDNB modules identified in the symptomatic samples are shown in S2 Table , and the overlapped genes among all sDNBs in symptomatic samples are shown in S3 Table . There were 25 genes in the overlapping of 9 sDNBs among all symptomatic samples , and the results of enrichment analysis for the 25 overlapped genes are shown in Table 2 . The overlapped gene functions included some processes of response to virus , consistent with phenotypes of the nine samples infected by the influenza virus . Because the functions of the 25 overlapped genes were enriched to the processes of defense response , negative regulation to virus , or antivirus response ( Table 2 ) , it appears that the process of immunity or defense against the influenza virus may start in the immune system , and the immune systems of the nine symptomatic samples could not stop the further “invasion” of the influenza virus , resulting in the influenza phenotype . The time points identified by sDNBs may be the critical points of influenza virus “invasion” ( defeating the immune system ) . The functional enrichment of the overlapped genes of all sDNBs is consistent with the phenotype of invasion of the influenza virus and the response of the immune system to defend the virus . Nineteen of the 25 overlapped genes are reported to be related to virus response , and 6 of the 19 are associated with the influenza virus ( S3 Table ) . Hence , most of the genes identified by sDNB may be potential target genes for further study of the mechanism of the interaction between the influenza virus and human beings in the future . Note that although the data of the 6 genes are common in all nine symptomatic subjects , they do not have sufficient information to detect the early-warning signal for each subject . Actually , for the diagnostic purpose , it is preferred to use all measured genes ( e . g . , 20000 genes ) , which include available information to identify sDNB for signaling the critical state of the disease progression of each subject . Fifty-eight tumor-adjacent samples were taken as reference samples for LUAD ( Table 1 ) , 33 as reference samples for STAD ( Table 1 ) , and 58 as reference samples for THCA ( Table 1 ) . The potential sDNB for each sample was detected by the following method ( Fig 2 ) . The threshold of sPCC was set as the p-value of 0 . 01 to construct the single-sample network ( Fig 2 ) , and the module with the maximal score in each sample was regarded as the potential sDNB for this sample . The progression and development of cancer can be divided into stages , such as stage I , stage II , stage III , and stage IV . Metastasis , the major cause of recurrence and death in cancer patients , is a complex interplay between malignant cancer cells and surrounding tumor microenvironments [10] . Stage IV is usually an advanced or metastatic cancer in which the tumor has spread or metastasized to other organs or parts of the human body [11 , 12] . All of the samples were grouped into different cancer stages based on clinical information from the TCGA database . The index score Is of each potential sDNB module was calculated for every single sample , and the average index score of every stage for sDNB was used to identify the critical state or quantify the early-warning signals for cancer metastasis . For LUAD , STAD , and THCA , all the peaks for the average sDNB score appeared before stage IV , which is the cancer metastasis stage ( Fig 4 ) , and these peaks were considered the early-warning signals for cancer metastasis . There are seven stages ( IA , IB , IIA , IIB , IIIA , IIIB , and IV ) in the cancer progression of LUAD ( S1 Table ) , and the maximal score of the average sDNB index was detected in stage IIIB ( Fig 4A ) , which is the last stage before cancer metastasis . There were 10 samples of stage IIIB LUAD in TCGA ( Table 1 ) , with 10 sDNBs identified by our method ( S8 Table ) . Thirty-three genes appearing in at least eight ( 80% ) sDNBs were regarded to be related to the cancer metastasis of LUAD ( S5 Table ) . Some genes in this list have been shown to be associated with the process of cancer metastasis . For instance , SRPK1 is regarded as the molecular determinant of tumor cell migration and cancer metastasis [13] . TOP2A is related to brain metastasis for non-small-cell lung cancer [14] . CDC25C is related to the metastasis of cancer [15 , 16] . IQGAP3 is also related to cancer metastasis [17 , 18] . PRAME is a cancer metastasis gene in uveal melanoma [19] and in lung cancer [20] . XRCC2 is related to the metastasis of colorectal cancer [21] . TUBB3 is related to breast cancer metastasis to the brain [22] and metastasis in pancreatic cancer [23] . HDGF is related to the regulation of cancer metastasis [24 , 25] , and especially to the metastasis of lung cancer [26 , 27] . SPAG5 is related to the metastasis of prostate cancer [28] . The above genes have all been reported to be associated with cancer metastasis , and they might also regulate and/or provide early-warning signals for the cancer metastasis process in LUAD . Functional enrichment showed that the common genes in at least 80% of sDNBs are identified as genes involved in the biological processes of the nuclear division , the mitotic cell cycle , the organelle fission , and so on ( Table 3 ) by GOC ( the gene ontology consortium ) and g:Profiler , and these biological processes are associated with the progression of cancer . These common genes were also related to stage 4 non-small-cell lung carcinoma and metastatic non-small-cell lung cancer by functional enrichment in IPA ( Table 3 ) ; this is consistent with our assumption , based on the DNB theory , about the critical state of tumor metastasis for non-small-cell lung cancer prior to stage IV . There are also seven stages ( IA , IB , IIA , IIB , IIIA , IIIB , and IV ) in the cancer progression of STAD ( S1 Table ) , and the maximal score of the average sDNB index was detected in stage IIIB ( Fig 4B ) , which is the last stage before cancer metastasis . There were 20 samples recorded as stage IIIB STAD in TCGA ( Table 1 ) , and 20 sDNBs were identified from these 20 samples ( S9 Table ) . Eighteen genes appeared in at least 10 ( 50% ) of the sDNBs and were considered to be related to the cancer metastasis of STAD ( S6 Table ) . Some genes in this list have been reported to be associated with the process of cancer metastasis , e . g . , COL11A1 has been identified as a remarkable biomarker for carcinoma progression and metastasis [29] in breast cancer [30] and serous ovarian cancer [31] . CST1-overexpressing cell lines exhibit increased metastasis in a mouse model [32 , 33] . High expression of CST4 can promote bone metastasis in vivo [33] . CTHRC1 is upregulated and enhances the epithelial-mesenchymal transition of tumor cells to promote cancer invasion and metastasis in colorectal cancer [34–36] and melanoma [37] . ESM1 regulates cell growth and the metastatic process by activation of NF-κB in colorectal cancer [38] . The overexpression of FGF19 is significantly associated with tumor-distant metastasis in thyroid cancer [39] . High expression of IBSP is associated with bone metastasis in breast and prostate cancers [40 , 41] . PRAME is also one of the 33 genes in the overlapped sDNB of LUAD [19 , 20] . PRAME is a cancer metastasis gene involved in uveal melanoma [19] and lung cancer [20] . Wnt2 plays an important role in the metastasis of pancreatic cancer [42 , 43] . The above genes are associated with cancer metastasis , and they may also regulate and/or provide early-warning signals for the cancer metastasis process in STAD . Functional enrichment analysis showed that the common genes in at least 50% of sDNBs were involved in the biological processes of collagen catabolic process , multicellular organismal catabolic process , etc . ( Table 4 ) according to GOC and g:Profiler , and these biological processes may characterize the alteration of tumor metabolism [44 , 45] . These common genes were also related to the proliferation of cells , upper gastrointestinal tract cancer , and digestive organ tumor by functional enrichment from IPA ( Table 4 ) ; this is consistent with our test , based on the DNB theory , for quantifying the critical states of tumor metastasis in gastric cancer . There are four stages ( I , II , III , and IV ) in the cancer progression of THCA ( S1 Table ) . The peak score for the average sDNB index appeared in stage III ( Fig 4C ) , which is also the last stage before cancer metastasis . There are 82 stage III samples ( Table 1 ) , from which 82 sDNBs were identified ( S10 Table ) . Fifty-one genes appeared in at least 41 ( 50% ) sDNBs and were considered to be related to cancer metastasis in THCA ( S7 Table ) . Some genes in this list have been reported to be associated with the process of cancer metastasis . In particular , the expression of CITED1 is correlated with lymph node metastasis in patients with colorectal cancer [46] . CSF2 is one of the pivotal orchestrators of basal breast cancer growth and metastasis [47] . DPP4 shows positive metastatic activity in cancer cells [48] . FN1 plays a critical role in metastasis and is associated with advanced stages and higher metastatic potential in patients with renal cancer [49–51] . GRM4 is involved in the metastasis of osteosarcoma and affects the survival of osteosarcoma patients [52] . The expression of IGSF1 is associated with the invasion and metastasis of neoplasms by mediating homotypic and heterotypic intercellular adhesion and binding [53] . KLK10 plays essential roles in tumor invasion and metastasis in gastric cancer [54] and epithelial ovarian carcinomas [55] . The expression level of KLK7 is correlated with prognosis of liver metastasis in patients with colorectal cancer [56] . LAD1 is identified as a potential marker in renal cell cancer , showing univariate association with distinct metastasis [57] . Knockdown of LAM3 suppresses human lung cancer cell invasion and metastasis in vitro and in vivo [58] . LIPH is related to distant metastasis in breast cancer [59] . PROS1 can lead to regulation of local invasion and metastasis [60] . Enhanced SERPINA1 expression is significantly associated with invasion and metastasis in gastric cancer [61] . SLC34A2 strongly inhibits tumor growth and metastasis ability in non-small-cell lung cancer [62] . TENM1 is related to tumor metastasis in prolactin pituitary tumors [63] . TMPRSS4 mediates tumor cell invasion , migration , and metastasis [64] . The above genes are associated with cancer metastasis and might also regulate the critical state in the cancer metastasis process in THCA . Functional enrichment showed that the common genes in at least 50% of the sDNBs are associated with thyroid cancer , papillary thyroid cancer , thyroid gland tumor , etc . ( Table 5 ) according to IPA , which is consistent with the test for thyroid cancer . We also estimated the significance of sDNB to correctly signal the critical state ( Stage III ) for thyroid cancer . We first randomly picked up 82 samples from all THCA samples ( see Table 1 ) , and calculated their average score of sDNB . Then , the average score of sDNB for the random samples was compared with that of the 82 samples in Stage III . Such a random sampling was repeated 10000 times . The probability that the average sDNB score of the random samples is greater than that of all the samples in Stage III is regarded as the statistical significance for the identification of disease deterioration , and actually the p-value of the statistical significance is 0 . 0318 in THCA .
In this study , by exploiting the high-dimensional information of the observed data and the volcano distribution of differential networks , a new method was proposed to identify tipping points or critical states ( which appear just before the disease state ) based on single-sample DNB ( sDNB ) . In contrast to the information of differential expressions used in traditional biomarkers to diagnose disease , sDNB is based on the information of differential associations , thereby having the ability to predict disease or “diagnose the un-occurred disease” . This method was applied to quantify the early-warning signals for the process of influenza virus infection and cancer metastasis on a single-sample basis . The results for the influenza virus infection show that high sDNB scores indeed signaled the imminent emergence of disease symptoms ( at least 8 hours before their appearance ) for every symptomatic sample , and there were no significant high scores for asymptomatic samples with the exception of adult s17 . A potential explanation for this false-positive result on adult s17 is that this asymptomatic adult was the only non-Caucasian/White subject among the asymptomatic adults , and may thus have had a different threshold . Another possibility is that adult s17 did reach the critical state but recovered to the normal state before further deterioration into the disease state , thereby causing a significant signal . This method is also robust for quantifying early-warning signals by identifying the sDNB . When the threshold of sPCC was set at the p-value of 0 . 05 to construct the single-sample network ( Fig 2 ) , there were large fluctuations in the samples of symptomatic adults approaching disease symptoms and small fluctuations in the samples of asymptomatic adults , with the exception of adult s17 ( S2 Fig ) . When the threshold of the sDNB score was set to 1 . 6 , we obtained similar early-warning signals for predicting influenza symptoms , as shown in Fig 3 and S2 Fig . There were 10 sDNBs ( S11 Table ) and 54 overlapped genes ( S12 Table ) among the sDNBs based on this threshold , and functional enrichment showed that these 54 genes can also characterize the virus infection response ( S13 Table ) , similar to the results shown in Table 2 . Hence , the threshold of sPCC is robust , i . e . , it does not significantly affect the results , although the threshold of the sDNB score for detecting the critical states is an empirical value in this study . It is our important future work to identify the sDNB threshold in a systematic and efficient way . The results for cancer metastasis showed that sDNBs could detect the critical state of cancer metastasis before stage IV that is the stage when cancer-distant metastasis occurs . In particular , for LUAD , the overlapped genes of sDNBs in stage IIIB could be enriched to the processes of stage 4 non-small-cell lung carcinoma and metastatic non-small-cell lung cancer by IPA ( Table 3 ) , indicating that the function of the sDNBs identified in stage IIIB is related to the metastasis of LUAD in stage IV and that sDNBs provide the early-warning signals that can be used to predict the onset of metastasis for LUAD before it occurs . Note that sDNB is a model-free method , and does not requires the learning on sample data; it is completely different from the traditional classification or machine learning methods which are population-based predictors requiring a large number of case/control samples to train the model and eliminate the overlearning problem . In other words , sDNB is an individual-based predictor based on the three statistical conditions for each specific sample , and thus inherently has neither overlearning problem nor assumption on the model . Hence , even for the same disease , the composition of sDNB as well as the size of sDNB for each sample or individual may be different , but its Is drastically increases whenever approaching the critical state . However , we use a unified threshold in this paper , on the composite index of sDNB or Eq ( 6 ) , for determining the critical state , which is based on the whole disease samples . The critical state is considered as a stage early reversible to the normal state . Thus , appropriate treatment for subjects in the critical state is considered much effective in contrast to the subjects in the disease state . However , how to make such a treatment is beyond the scope of this work , and will be a future topic . In addition , theoretically , any omics data ( e . g . , transcriptomic data , proteomics data , or metabolomics data ) which can dynamically reflect the change of the disease progression , can be used to detect the critical state or tipping point . Thus , depending on the disease type , we may choose an appropriate type of the omics data . With current high-throughput technologies , generally RNAs can be quantified in a relatively stable way in contrast to proteins and metabolites . Therefore , the transcriptomic data ( e . g . RNA-Seq or microarray ) are effective for sDNB identification from the computational viewpoint , although metabolomics and proteomics data can also be used to identify the critical state . In summary , the method described in this paper developed a novel method , sDNB , which is the first such a method to predict disease state based only on a single sample , opening a new way to quantify the critical state of diseases in individual patients . Thus , the method can be directly applied not only to personalized pre-disease diagnosis but also to the molecular mechanism analysis of disease progression at the network level . In a similar way , sDNB could also be used to detect the tipping points or critical states of many nonlinear biological processes , such as cellular differentiation and cellular proliferation [4–6] . | The concept of dynamic network biomarkers ( DNB ) was proposed for detecting the critical state or tipping point of a complex disease ( a pre-disease state immediately preceding the disease state ) , and has been applied to study the mechanism of cell fate decision and immune checkpoint blockade . But DNB cannot be used to identify the critical state or tipping point for a single patient because evaluating DNB for critical state required the data of multiple samples . The proposed method can identify the critical state of a complex disease for a single patient by implementing the concept of DNB . This method not only can be applied to detect the critical state or tipping point of a single sample , but also can be used to study the mechanism of complex disease at a single sample level . The ability of accurately and efficiently identifying the critical state for a single sample can benefit the development of personalized medicine . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"endocrine",
"tumors",
"immunology",
"carcinomas",
"microbiology",
"basic",
"cancer",
"research",
"cancers",
"and",
"neoplasms",
"biomarkers",
"orthomyxoviruses",
"cell",
"processes",
"oncology",
"viruses",
"rna",
"viruses",
"medical",
"microbiology",
"lung",
"and",
"intrathoracic",
"tumors",
"gene",
"expression",
"microbial",
"pathogens",
"cell",
"cycle",
"and",
"cell",
"division",
"thyroid",
"carcinomas",
"immune",
"response",
"biochemistry",
"metastasis",
"cell",
"biology",
"influenza",
"viruses",
"viral",
"pathogens",
"thymic",
"tumors",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-small",
"cell",
"lung",
"cancer",
"organisms"
] | 2017 | Quantifying critical states of complex diseases using single-sample dynamic network biomarkers |
Scrub typhus is a neglected tropical disease that causes acute febrile illness . Diagnosis is made based upon serology , or detection of the causative agent–Orientia tsutsugamushi–using PCR or in vitro isolation . The enzyme-linked immunosorbent assay ( ELISA ) is an objective and reproducible means of detecting IgM or IgG antibodies . However , lack of standardization in ELISA methodology , as well as in the choice of reference test with which the ELISA is compared , calls into question the validity of cut-offs used in diagnostic accuracy studies and observational studies . A PubMed search and manual screening of reference lists identified 46 studies that used ELISA antibody cut-offs to diagnose scrub typhus patients , 22 of which were diagnostic accuracy studies . Overall , 22 studies ( 47 . 8% ) provided little to no explanation as to how the ELISA cut-off was derived , and 7 studies ( 15 . 2% ) did not even state the cut-off used . Variation was seen locally in reference standards used , in terms of both the diagnostic test and cut-off titer . Furthermore , with the exception of studies using ELISAs manufactured by InBios , there was no standardization of the selection of antigenic strains . As a result , no consensus was found for determining a cut-off , ELISA methodology , or for a single value diagnostic cut-off . We have concluded that there is a lack of consensus in the determination of a cut-off . We recommend interpreting the results from these studies with caution . Further studies will need to be performed at each geographic location to determine region-specific cut-offs , taking into consideration background antibody levels to discriminate true disease from healthy individuals .
Scrub typhus is a neglected tropical disease caused by the obligate intracellular bacterium Orientia tsutsugamushi [1] . Transmission of the bacteria to humans occurs via the bite of larval trombiculid mites , known commonly as chiggers [2] . It was formerly thought to be confined to the ‘tsutsugamushi triangle’ , encompassing Pakistan , Northern Australia and parts of Russia . However , cases acquired in Chile [3 , 4] , possibly Africa [5 , 6] , as well as the Middle East [7] ( by a proposed novel species O . chuto ) , have been reported , suggesting that its endemicity may be more widespread than previously thought . Patients typically present with acute febrile illness , but if left untreated , this may progress to systemic infection and multi-organ failure , contributing to an estimated median mortality rate of 6 . 0% for untreated and 1 . 4% for treated scrub typhus [8] highlighting the importance of early and accurate diagnosis . A characteristic necrotic lesion , or eschar , at the inoculation site may serve as a diagnostic clue , however its presence varies , ranging from 9%-97% depending on the population [9 , 10] . Given that other febrile illnesses such as typhoid , dengue and leptospirosis have similar clinical manifestations as scrub typhus , laboratory test is essential to differentiate scrub typhus from other undifferentiated fever [11] . Serological methods are more often used to diagnose scrub typhus due to their simplicity and cost-effectiveness [12] . The indirect immunofluorescence assay ( IFA ) is considered the “gold standard , ” but the requirement of a fluorescent microscope and the subjective nature of reading slides limits its application in rural areas where this disease is most prevalent [8 , 11–14] . The Weil-Felix test is convenient to perform but suffers from poor sensitivity and specificity [13 , 14] . Given the limitations of other serological methods , the enzyme-linked immunosorbent assay ( ELISA ) is acknowledged as a reasonably simple to perform alternative , providing an objective optical density ( OD ) result using an automated plate reader , that is reproducible in most clinical laboratory settings [13] . Despite the apparently standardized and objective ELISA platform , the diagnostic accuracy is influenced by methodological and patient factors . Methodological factors may include the composition of antigenic strains and their origin , and the choice of diagnostic cut-off . Patient factors are mainly centered on elevated levels of background immunity in endemic areas that may give rise to false positive results . Therefore , to ensure accuracy of diagnosis , standardized methodologies and locally validated OD cut-off levels for ELISA are urgently needed [8] . This review therefore aims to summarize ( 1 ) the differences in ELISA methodologies , ( 2 ) the OD cut-offs used for diagnosing scrub typhus in research , and ( 3 ) the rationale behind the selection of certain OD cut-offs for scrub typhus diagnosis in previously published diagnostic accuracy studies and observational studies .
A scoping review was performed . Searches were performed by one author ( MP ) on the PubMed electronic database using the following search terms: “scrub typhus , ” “tsutsugamushi” , “immunoassay” , and “ELISA” . The search was restricted to papers published in English , up to 16th October 2017 . The titles and abstracts were screened for relevance . The full-text of relevant articles were assessed to determine eligibility . Diagnostic accuracy and observational studies using ELISA to diagnose scrub typhus in human were included . We excluded co-infection studies , case studies and studies investigating variations of the conventional ELISA methods ( e . g . , dot-ELISA ) . Reference lists of the relevant articles were also screened in order to identify additional studies . The protocol of this review was registered in the International Prospective Register for Systematic Review ( PROSPERO ) with registration number CRD42017078596 . Data was extracted by one author ( MP ) , and where the information was unclear a second researcher was consulted ( SDB ) . Details of the sample size , location , study date , reference test , cut-off , method used to calculate the cut-off , and ELISA methodology ( antigenic strain and antibody isotype ) were compiled into summary tables . The studies were grouped according to study design ( diagnostic accuracy study or observational study ) , type of ELISA ( in-house or commercial ) , and study location . The data was summarized using narrative synthesis . We did not evaluate minutiae of individual ELISA protocols , but instead focusing on the wider issues such as the methodologies used to determined diagnostic cut-offs .
Out of a total of 24 observational studies , seven ( 29 . 2% , 7/24 ) stated the method to determine the diagnostic cut-off and four ( 16 . 7% , 4/24 ) studies were unclear about how they derived the cut-offs stating “as used in other studies” or similar wording ( Tables 2 and 3 ) . Of the remaining studies , 13 ( 54 . 2% , 13/24 ) provided no clear explanation as to how the cut-off was selected , however 0 . 5 OD was used for IgM and/or IgG diagnosis for 11 ( 45 . 8% , 11/24 ) of these studies ( Tables 2 and 3 ) . Of the 19 observational studies using the InBios ELISA , seven ( 36 . 8% , 7/19 ) ( Table 2 ) obtained local controls to determine a region-specific cut-off using the mean + 2 or 3 SD method . In the case of the NMRC in-house ELISA studies , the majority of studies ( 80 . 0% , 4/5 ) ( Table 3 ) , instead of calculating a single cut-off , patients were diagnosed with scrub typhus if they passed two criteria: 1 ) IgM OD ≥0 . 5 at a 1:100 dilution , and 2 ) a summed total OD of ≥1 . 0 of 4 sequential 4-fold dilutions . i . e . , 1:100 , 1:400 , 1:1 , 600 , 1:6 , 400 ) ( Table 3 ) .
The application of appropriate diagnostic cut-offs is important for timely scrub typhus patient management using appropriate antibiotic therapy and to prevent complications leading to significant detrimental effect . This review has determined that there was a significant lack of consensus regarding methodologies , application and diagnostic cut-offs for ELISAs used for the diagnosis of scrub typhus infections . However , the reasons are complex and require further investigation . Approximately half of the observational studies provided no or insufficient justification for the OD cut-offs , and two studies did not specify the cut-off they used . Although the 0 . 5 OD cut-off was used commonly in InBios ELISAs studies and used by the Indian Council of Medical Research ( ICMR ) , this is probably an appropriate estimation for certain parts of India with limited application in other geographic locations . This cut-off should be applied only in regions where it has been validated by testing samples from healthy controls to determine the level of background immunity in the normal population . In some cases , it is difficult to select a cut-off as demonstrated by Blacksell et al , where optimal OD cut-offs ranged from 0 . 2–0 . 6 OD depending on the reference standards used [12] . Several studies used the same cut-off for IgG and IgM , despite the differences in immunity dynamics of the different antibody isotypes–this should be taken into consideration when interpreting results of such tests [15 , 24 , 27 , 28] , as generally , upon infection a spike in IgM is seen , followed by increased levels of IgG , which also confers long-term protection . There was a lack of uniformity of approach regarding the diagnostic accuracy studies to determine appropriate ELISA cut-offs for various geographic locations . The reference methodologies varied from Bayesian LCM using composite scrub typhus infection criteria ( STIC ) , IFA , through to mean + SD in healthy controls . In most cases , there was no clear justification for the reference test cut-offs employed , and it is likely that some of these cut-offs were not appropriate for the location in which they were being used . For example , while an IFA cut-off of 1:400 is often set in Thailand , it has been suggested to have a high false-positivity rate [29] . Subsequently , the diagnostic accuracy of composites such STIC have also been suggested to overestimate scrub typhus positivity compared with index [30] . Bayesian LCM is being increasingly used to determine true diagnostic accuracies , as they do not assume any reference diagnostic test is perfect [30] . A recent study calculated–using this method–an admission IgM IFA cut-off of ≥1:3 , 200 or at least a 4-fold rise to ≥1: 3 , 200 in the convalescent-phase sample to provide the highest accuracy [29] . Only one study in this review used Bayesian LCM; combining IFA , PCR , eschar and culture results as reference standards to interpret ELISA results [23] . Given that the reference standards all have different accuracies , using a composite in a Bayesian approach helps to eliminate bias . Other studies used different approaches that may compromise accuracy . For example , in one study , a response to an unnamed antibiotic , along with positivity by either PCR or presence of an eschar , was used as the diagnostic criteria [26] . Generally , doxycycline is prescribed to treat scrub typhus , however since it is a broad-spectrum antibiotic and also used to treat leptospirosis and murine typhus , a response to treatment may not point specifically to scrub typhus as the cause of illness . A number of factors may have an influence on the diagnostic accuracy of ELISAs including antigenic composition and sample population . Differences in ELISA methodologies were observed where studies used local antigenic strains or incorporated these into pooled Karp , Kato and Gilliam antigens to supposedly increase the accuracy of the test . In general , higher ODs were obtained when using homologous antigens , therefore variation in cut-offs were likely to be seen depending on the antigen being used and the locally circulating strains . In India , the use of the InBios ELISAs ( which used Karp , Kato , Gilliam and TA716 strain antigens ) was widely implemented , providing a more standardized means of diagnosing scrub typhus . Jiang et al demonstrated a trivalent r56-kDa protein to be superior to both monovalent r56-kDa Karp and whole-cell Karp , Kato and Gillam ELISAs [16] . The antigens used for deployed soldiers or travelers need to be carefully considered , and results need to be interpreted with caution , given their background immunity is likely to differ significantly from those living in endemic areas . Nevertheless , standardized , region-specific antigen preparations should be used in ELISAs , taking into consideration the circulating strains . Regarding study populations , the use of samples from diseased or normal subjects as well as the geographic origin of the subjects can affect the derived diagnostic cut-off . In one study , serum samples were collected from Australia and Thailand , but it was unclear to which population the cut-off was applied , or whether the cut-off was calculated using results from both the populations despite differences in endemicity [18] . In addition to a lack of ELISA methodology standardization there was also lack of consensus in what is considered as the gold standard reference assay to determine diagnostic cut-offs . The absence of standardized methods and appropriate cut-offs has implications for seroepidemiology and clinical studies , as well as clinical decision making . On one hand , lower cut-off would result in false positives results risking unnecessary treatment and increasing probability of antimicrobial resistance . On the other hand , higher cut-off would result in false negative results risking cases to be missed . This review has several limitations . First , it only investigated studies published in English , which may limit literature retrieval . Second , only one author performed the article selection and data extraction , however , any unclear data was discussed amongst the authors in order to limit bias . Lastly , the ELISA protocol was not examined as a factor . This needs to be considered when interpreting results , as differences in protocol ( e . g . the amount of antigen used in plate coating ) can influence the sensitivity and specificity of ELISA tests , that in turn influence the selection of optimal cut-offs . To limit the heterogeneity caused by different ELISA protocol , variations of the conventional ELISA were excluded from the review , and the InBios ELISA studies were grouped together in the analysis . Further research will need to be conducted to determine local levels of background immunity , as well as to identify circulating strains , in order to make informed decisions for a region-specific , standardized ELISA methodology and cut-off . | Scrub typhus is a neglected tropical disease that causes acute fever and can cause serious complications without appropriate antibiotic treatment . Diagnosis is usually made by the detection of specific antibodies or the causative agent–Orientia tsutsugamushi . Specific antibodies can be detected using ELISA technology however there is an apparent lack of standardization in the development of cut-offs used in diagnostic accuracy studies and observational studies . This study assessed 46 studies that used ELISA antibody cut-offs to diagnose scrub typhus patients . Overall , 22 studies ( 47 . 8% ) provided little to no explanation as to how the ELISA cut-off was derived , and 7 studies ( 15 . 2% ) did not even state the cut-off used . Furthermore , with the exception of studies using ELISAs manufactured by InBios company , there was no standard approach to the selection of antigenic strains , and therefore they may not be representative of the local antigenic strains causing disease . As a result , we have concluded that there is a lack of consensus in the determination of a cut-off . We recommend interpreting the results from these studies with caution and further studies will need to be conducted at each geographic location to determine region-specific cut-offs take into consideration background antibody levels to discriminate true disease from healthy individuals . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"antimicrobials",
"dermatology",
"typhus",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"drugs",
"immunology",
"microbiology",
"observational",
"studies",
"bacterial",
"diseases",
"research",
"design",
"antibiotics",
"pharmacology",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"eschar",
"immunoassays",
"antibody",
"isotype",
"determination",
"scrub",
"typhus",
"diagnostic",
"medicine",
"immunity",
"microbial",
"control",
"biology",
"and",
"life",
"sciences"
] | 2019 | The validity of diagnostic cut-offs for commercial and in-house scrub typhus IgM and IgG ELISAs: A review of the evidence |
The chaperone BiP participates in several regulatory processes within the endoplasmic reticulum ( ER ) : translocation , protein folding , and ER-associated degradation . To facilitate protein folding , a cooperative mechanism known as entropic pulling has been proposed to demonstrate the molecular-level understanding of how multiple BiP molecules bind to nascent and unfolded proteins . Recently , experimental evidence revealed the spatial heterogeneity of BiP within the nuclear and peripheral ER of S . cerevisiae ( commonly referred to as ‘clusters’ ) . Here , we developed a model to evaluate the potential advantages of accounting for multiple BiP molecules binding to peptides , while proposing that BiP's spatial heterogeneity may enhance protein folding and maturation . Scenarios were simulated to gauge the effectiveness of binding multiple chaperone molecules to peptides . Using two metrics: folding efficiency and chaperone cost , we determined that the single binding site model achieves a higher efficiency than models characterized by multiple binding sites , in the absence of cooperativity . Due to entropic pulling , however , multiple chaperones perform in concert to facilitate the resolubilization and ultimate yield of folded proteins . As a result of cooperativity , multiple binding site models used fewer BiP molecules and maintained a higher folding efficiency than the single binding site model . These insilico investigations reveal that clusters of BiP molecules bound to unfolded proteins may enhance folding efficiency through cooperative action via entropic pulling .
Protein homeostasis , or proteostasis , is characterized by the integration of biological pathways that modulate protein biogenesis , maturation , transport , and degradation . As a critical element to cell survival , networks of molecular chaperones , foldases , and quality control components minimize the effects of cell stress in order to revert to a homeostatic environment [1] . Proteostasis occurs in distinct subcellular environments and is constantly monitored by stress-signaling pathways . In eukaryotes , the endoplasmic reticulum ( ER ) is the first membrane-enclosed organelle of the secretory pathway , which ascertains the fidelity of protein folding , maturation , biogenesis ( i . e . translation and ER translocation ) , and ER-associated degradation ( ERAD ) . In the yeast S . cerevisiae , multiple ER quality control mechanisms have been identified to modulate these critical ER processes , including associated chaperone/co-chaperone interactions . Specifically , molecular chaperones dissociate aggregates , self-associating conglomerations of unfolded and misfolded proteins , which would otherwise interfere with the cell homeostasis leading to cell dysfunction and death [2] . Despite the ubiquitous nature of molecular chaperones , a variety of insults can overwhelm the ER's processing capacity including nutrient deprivation , pathogenic infection , cell differentiation , or alterations in calcium concentration or redox status . As a consequence of ER stress , aberrant proteins accumulate within this organelle , triggering intracellular pathways collectively referred to as the unfolded protein response ( UPR ) . In eukaryotes , the UPR transcriptionally up-regulates genes encoding molecular chaperones [3] , ERAD machinery [4]–[7] , key enzymes of lipid biosynthesis [8] , and other components of the secretory pathway [9]–[11] . Notably , several key features of the UPR are conserved across eukaryotes; although expanded in scope , the mammalian UPR has similar attributes to that of S . cerevisiae , particularly with respect to the Ire1p-dependent regulation of unfolded proteins and BiP modulation of the response ( reviewed [12] ) . The elucidation of these pathways – specifically the interplay between UPR and ERAD – has become of growing importance in therapeutics as loss of proteostasis has been suggested to lead to a number of human diseases including Alzheimer's , Parkinson's Disease and Type II Diabetes [13] . In the early secretory pathway , protein fidelity is attributed to select chaperone/co-chaperone interactions ( Hsp70 and Hsp40 proteins , respectively ) conserved via evolution from yeast to humans . As one of two distinct Hsp70 molecular chaperones in the ER , BiP/Kar2p binds preferentially to hydrophobic residues of nascent or unfolded proteins [14] , [15] . BiP , the yeast homolog of binding protein immunoglobulin ( referred to as Kar2/Grp78 [16] ) , has been identified as an essential component of ER translocation , protein folding and maturation , karyogamy , and ERAD [17]–[20] . To facilitate protein folding , co-chaperones stimulate the binding of BiP to substrates whereas nucleotide exchange factors ( NEFs ) assist in BiP's stochastic release via cycles dependent upon adenosine triphosphate ( ATP ) . For example , co-chaperone Sec63 directly interacts with BiP , increasing its affinity for nascent proteins as they advance through the translocation pore in S . cerevisiae [21]–[23] . In yeast , the posttranslational translocation of nascent peptides is mediated by a heptameric Sec complex , composed of Sec63 , Sec62 , as well as the heterotrimer Sec61 , which serves as the protein-conducting channel [24] , [25] . Photo-cross-linking experiments have shown that nascent peptides are in continuous contact with Sec61 during protein translocation [26] . More recently , cryo-electron microscopy established that a single Sec61 heterotrimer enables the progress of nascent proteins across the ER membrane , a conserved feature manifested in both yeast and mammals [27] . In addition to ER translocation , BiP's interaction with co-chaperone Scj1 has been implicated in protein folding and maturation [28] , [29] , and degradation of aberrant proteins [30] . ER translocation , protein folding and maturation , as well as ERAD are conserved mechanisms across eukaryotes . As such , the model eukaryotic organism , S . cerevisiae , provides an effective experimental platform to elucidate an improved mechanistic-understanding of proteostasis , specifically with regards to ER chaperone/co-chaperone interactions . Proteomic studies have identified absolute levels of protein expression and verified the location of ER-resident proteins [31] , [32] . These data suggest that the ER-resident chaperone BiP exceeds the level of all co-chaperones in the ER by at least an order of magnitude at conditions of normal growth , and is significantly up-regulated during the UPR [6] , [33] indicating a significant increase in BiP's total abundance . Furthermore , BiP binds to substrates with varying affinities [14] , suggesting BiP responds to the protein's folding requirements . Interestingly , from an experimental perspective , the spatial localization of ER-resident chaperones or co-chaperones has been evaluated for only Sec63 during the process of translocation . In yeast , membrane protein Sec63 must by necessity be localized at the ER membrane in order for nascent proteins to translocate [34] . Collectively , this evidence suggests that BiP's spatiotemporal profile may be a contributing factor to its diversity and functionality in the ER . This hypothesis was previously posited and computationally explored [35] . Those results were in agreement with Sec63 experimental results , and further suggested that BiP clusters may exist in order to facilitate the efficient translocation of nascent proteins . That BiP performs disparate functions owes to its tendency to bind many different types of proteins . Binding of multiple chaperones to unfolded proteins has been established and determined to be kinetically favorable [36] . The transport of nascent proteins into the ER involves many BiP molecules working in concert . Algorithms to predict binding sites have been developed , and there are many examples of proteins that have repeated hydrophobic stretches of amino acids [15] , [37] , [38] , which predict the presence of multiple binding sites . Aggregates have also been found to have the analogous binding sites [39] , while their large size implies that multiple BiP molecules could engage them at individual sites simultaneously . Here , we refer to clustering as the process by which multiple BiP molecules bind to individual binding sites that can be predicted from hydrophobic residues along the length of the protein . This is in contrast to aggregation , where self-associating conglomerations of unfolded and misfolded proteins combine into larger toxic structures . Experimental evidence has revealed that the refolding of misfolded proteins and aggregates occurs in the presence of a molar excess of chaperones [40] , which led investigators to propose that multiple chaperones apply a cooperative stretching force known as entropic pulling [41] , [42] . The random motion of several bound individual chaperones on a peptide can sum up to an effective unfolding enabling re-initialization of the folding process . The additional molecules provide an inertial brace that stabilizes the interaction between chaperone and protein . In the case of chaperone-mediated disaggregation , the brace is the aggregate itself . A similar mechanism enables chaperones to assist nascent peptides during ER translocation . Cooperative action underlies many cellular processes including signal transduction [43] , protein transport [44] , and chemotaxis [45] . In the chaperone system , binding is not cooperatively enhanced . Rather , the rate of solubilization and renaturing of proteins increases with the number of chaperone molecules [40] . In this study , we created a computational model to investigate the extent that an ER-resident chaperone , BiP — spatially localized to “clusters” — may influence the extent of protein folding . Our model includes BiP , the co-chaperones Scj1 and Sec63 , and multiple states corresponding to unfolded proteins and complexes . This work implements ER-resident chaperone/co-chaperone interactions , experimental insights [21] , [46]–[48] , estimates of species concentrations determined for S . cerevisiae [32] , and binding affinities between BiP , Sec63 , and synthetic peptides [46] , . When experimental data were not available , established estimates from the mammalian literature for these highly conserved mechanisms and proteins were used ( Text S1 , Table S1 , Supporting Information ) . To assess the potential advantages of clusters , this model was used to evaluate the extent to which gradients of BiP molecules may facilitate its activities in protein folding and aggregate disassembly . Previous models [49]–[59] have included varying aspects of chaperone activities and interactions , yet only accounted for a single binding site scenario; in contrast , our model focuses on multiple binding as the mechanism to facilitate BiP's roles in the ER . This study provides a detailed analysis of ( i ) the quantitative impact of chaperone clustering activity in the ER and contributing factors leading to efficient protein folding; and ( ii ) the potential mechanisms and interplay among components of ER quality control . Together , this framework provides an improved mechanistic understanding of chaperone/co-chaperone interactions , as well as possible strategies to minimize the accumulation of misfolded proteins .
We created an ordinary differential equation ( ODE ) model to study the efficiency of protein folding due to the molecular heterogeneity of ER-resident chaperone , BiP . Four sub-models were created that differ by the stoichiometry of binding sites to the protein species: one , two , three , and four ( as shown in Figure 1 ) . To evaluate model performance two metrics were accounted for: ( i ) folding efficiency ( i . e . , fraction of proteins folded ) ; and ( ii ) chaperone cost ( e . g . , the molecular resources needed to achieve a specified level of efficiency ) . A schematic of the ER , as well as prominent protein-protein interactions , is shown in Figure 2 . A total of 60 species and 125 reactions are evident in the largest model . Within a model , numerous states have been depicted in Figure 3 . A comprehensive list of all reactions , states , rates and initial conditions is referenced in the Supporting Information . The initial units of species abundance were converted to concentration by incorporating an ER volume of 0 . 7 µm3 [60] . Model parameters were obtained from literature sources ( where available ) , as detailed in the Supporting Information ( Text S1 , Tables S1–S7 ) . Our model monitored the fate of soluble proteins within the ER lumen by investigating the composition of six modules , as follows: In this study we assessed two model metrics: folding efficiency and chaperone cost . The former is given by the total number of folded proteins at the end of the simulation divided by the total number of unfolded proteins ( yielding a fractional range between zero and one ) , ( 4 ) Chaperone cost is defined as the average number of bound chaperones per unfolded protein per unit time . This metric combines the time spent on the protein with the total number of chaperones bound at the end of the simulation , ( 5 )
Steady-state solutions for the four model cases ( corresponding to 1 , 2 , 3 or 4 binding sites ) were completed for different values of BiP association to unfolded proteins . Figure 4 compares the models in terms of folding efficiency ( i . e . , total folded as a percentage of total protein ) and association rate . In the absence of cooperativity , the single binding site model ( Model 1 ) yields increased levels of folded protein , as unfolded protein binding sites are more easily saturated , providing more BiP coverage of the unfolded protein population ( Figure 5 ) . When one examines the time of interaction between BiP and unfolded proteins , BiP covers more proteins , each for a longer period of time ( in protein per second ) as compared to the alternative models ( Figure 6 ) . This effect occurs at low ratios of BiP∶U , hence the chaperone is classified as a ‘holdase’ [73] . However , the simpler non-cooperative models are incomplete in describing the entropic pulling data [40] , hinting that multiple BiPs must also act as a cooperative ‘unfoldase’ , in line with previous observations [73] . In comparison to other models herein , the degree of folding in the single binding site model is more highly dependent on the association rate ( Figure 4 ) . Multiple BiP binding events minimize the potential of an unfolded protein towards either misfolding or aggregation pathways , as a consequence of redundant binding events . We have not accounted for ATP molecules in our simulations , since this aspect would only be of concern in a depleted ATP environment [70] . In line with the entropic pulling contributing to BiP function , we increased the rates of folding , unfolding , and disaggregation by a factor of C , to reflect the cooperativity of multiple chaperones participating in these select intracellular activities . With C = 10 ( e . g . , the lower end of the range ( 1–100 ) reported in the literature [41] , Model 2 resulted in the highest level of folded protein . Less folding was observed in Models 3 and 4 as compared to Model 2 since coverage competes with cooperativity ( Figure 7 ) . When cooperativity is implemented , the folding efficiency for Models 2 , 3 , and 4 increases; Model 2 performs optimally for C>5 , as shown in Figure 8 . We then varied the concentrations of total BiP and unfolded protein to examine the effect on the two metrics described previously . As expected , increased concentrations of BiP led to higher levels of folding and less aggregation . Unexpectedly , we discovered that the ratio of BiP∶U is a more important factor than the concentrations of either species alone . In the noncooperative scenario , Model 1 produced the most folding ( Figure 9 ) ; however , when cooperativity was added , Models 2–4 attained higher folding efficiencies ( Figure 10 ) . These results suggest that when the BiP∶U ratio is low ( e . g . , conditions of ER stress ) , cooperativity provides an advantage for multiple binding . At higher BiP concentrations ( i . e . , relative to the concentration of U ) , cooperativity became a factor of less importance since the majority of unfolded proteins were protected from aggregation . As a result , more binding sites were occupied , leading to an equalization in the total amount of folding among the four models , i . e . the cooperativity effect was less pronounced . Figure 11 shows that chaperone cost ( i . e . , average chaperones bound per second compared to unfolded , misfolded and aggregated proteins ) decreased substantially for Models 2 , 3 and 4 in comparison with Model 1 , as shown for the cooperative case . In general , it is better to maintain a lower cost metric resulting in fewer chaperones bound per second . Due to the faster rates of disaggregation , unfolding , and folding in the cooperative scenario for Models 2 , 3 and 4 , chaperones maintained a shorter interaction with proteins . More chaperones were engaged with a single protein in Models 2–4 , yet this result was counteracted by decreased time that chaperones were bound to the protein . To investigate the correlation between parameters and folding efficiency for the different models and cooperativity scenarios , a heatmap is shown in Figure 12 . In this study , we varied BiP's association rate , the aggregation rate from 103 to 108 M−1 s−1 and varied the folding , unfolding , misfolding , BiP disassociation , and sequestering rates from 102 to 10−3 s . Over these six orders of magnitude , the folding efficiencies were recorded then correlations were completed between parameter ranges and folding efficiencies . Note: this analysis varied one parameter at a time , while keeping the others constant . In addition to the single parameter study , we performed a variance-based global sensitivity analysis , in which we varied seven parameters ( the BiP association rate , the BiP disassociation rate , the aggregation rate , the unfolding rate , the misfolding rate , the folding rate , and the sequestration rate ) over two orders of magnitude simultaneously , and produced 100 , 000 parameter sets as input to the seven models ( four non-cooperative and three cooperative models ) . We ran each simulation to steady state and recorded the metrics of folding efficiency and chaperone cost . From the variance-based global sensitivity analysis we learned that the sequestration rate and the aggregation rate were the dominant contributors to the variance of the output . However the variance was quite small . Our graphs then revealed for all seven parameters that the output mean across regions of parameter space was essentially constant within a model . This remarkable result indicates that the model output is rather invariant to changes in parameters . Instead our results show that model structure ( the number of binding sites ) and the cooperativity factor play a critical role in the behavior of the models . In addition , we also varied the concentrations of BiP and unfolded protein ( U ) . All of these results are in Text S2 , the Global Sensitivity Analysis Supplement . Finally , a translocation scenario was implemented to evaluate the impact of BiP clustering in a dynamic environment . In five different scenarios , a protein flux of 10 , 100 , 1000 , 10000 , and 100000 molecules per second was added to the ER [74] over a period of 100 seconds . Thus , many more molecules transverse the membrane to enter the ER lumen , with 106 molecules initially localized in the lumen as in the steady-state case . This approach was used to mimic general ER stress in yeast . We determined that the translocation flux is highly negatively correlated ( −0 . 96 to −0 . 99 ) with folding efficiency ( Figure 13 ) . This result was expected; as the protein flux increases , nascent proteins accumulate at the cytosol/ER membrane interface due to the limited number of pore complexes while BiP preferentially localized to ER the membrane as compared to the lumen . In the non-cooperative model , Model 1 has the highest efficiency due to the coverage effect . When cooperativity is accounted for , the multiple binding models yield a higher folding efficiency . If no unfolded proteins exist in the lumen , initially most proteins are protected and the folding efficiency is close to 1 ( simulation data not shown ) .
The chaperone BiP participates in many critical ER processes , including translocation , protein folding , disaggregation , and degradation . To elucidate an improved mechanistic understanding of ER proteostasis , we constructed a computational model to evaluate ER-resident chaperone/co-chaperone interactions in which multiple BiP molecules interact with nascent and unfolded proteins to facilitate protein folding and maturation . In contrast to established models that focused only on a single site for chaperone binding events , we modeled the mechanism of entropic pulling , in which several chaperones operate in concert to unfold and disaggregate peptides by incorporating a stretching force caused by random motions of the individual chaperones . In order to investigate the acceleration of nascent proteins across an organelle membrane , entropic pulling unifies aspects of both the Brownian ratchet model [61] and power stroke model [75] , [76] exceedingly well . In S . cerevisiae , entropic pulling was implemented successfully to track chaperone interactions during mitochondria translocation and to assess nascent proteins and aggregates [41] . Our model that incorporates this synergy represents a progress towards a mechanistic understanding of chaperone interactions . Protein aggregation was modeled as a separate module to monitor protein fate during simulations . Results indicated that most unfolded and aggregated proteins carried out a transient interaction with chaperone molecules . Despite the stochastic binding events between BiP and unfolded proteins , the sequestration of aggregates can entrap chaperone molecules leading to decreased chaperone levels . The comparison of BiP-protein interactions , in terms of folding efficiency and levels of chaperone cost , was quantified for models containing divergent numbers of binding sites . Our results indicate that for a given concentration of BiP and proteins ( i . e . , nascent , unfolded , or misfolded ) , single binding site models provided the highest degree of BiP coverage . However , experimental evidence previously showed that multiple chaperone molecules can work in concert to increase protein refolding and remove aggregates in vivo . Furthermore , our model revealed that the BiP-protein interaction provides additional advantages , such that multiple bound BiP molecules prevent misfolding of U . Given the parameter uncertainty , we conducted a study that varied seven parameters ( Figure 12 ) in order to examine the effects of folding efficiency in the system . Initially , each parameter was individually altered , as a global search required many sets and covered only a fraction of the parameter space . We observed that some parameters were positively correlated with folding efficiency and others were negatively correlated . The strongest effect came from the disassociation rate of BiP from unfolded proteins , because the longer the BiP could stay bound , the greater chance that folding could occur . Note: this analysis varied one parameter at a time , while keeping the others constant . Global sensitivity analysis , where all parameters were varied simultaneously , is found in the Global Sensitivity Analysis Supplement , Text S2 . Due to the highly conserved features between the model eukaryote , S . cerevisiae , and mammalian protein-folding machinery , it is extremely likely that these findings for ER translocation and protein-folding events will translate to higher eukaryotes including humans . In fact , mammalian BiP ( Grp78 ) appears to have two functions in protein translocation: ( i ) it is involved in the insertion of nascent proteins into the Sec61 complex or opening of the pore itself [77] , [78] , and ( ii ) it binds to the nascent protein that laterally advances through the channel , in a manner similar to a molecular ratchet that facilitates translocation [79]–[81] . Recently , experimental studies of the mammalian homolog of the Sec complex – co-chaperone Sec63 in yeast – has been shown to recruit BiP to the translocon ( i . e . Sec61 ) and activates BiP for interaction with its substrates [82] , analogous to the BiP's recruitment to the translocon in yeast , as described previously . The function of many subunits of the Sec complex in mammalian cells has remained elusive due to limited experimental assessments; however , recent progress has begun to elucidate translocation efficiency , gating kinetics and functional profiling , and transport effects of subunits that comprise the mammalian Sec complex [83]–[85] . Developing spatially-relevant computational models is important as in vivo experiments , such as single particle tracking ( SPT ) and super-resolution fluorescence imaging techniques used to capture spatial effects at nanometer resolution , are relatively new technologies [86] , [87] . Interestingly , under conditions of cell homeostasis BiP has been found to distribute heterogeneously throughout the yeast ER , as depicted by live cell imaging and immunofluorescence techniques [23] . In a similar manner , we conducted fluorescence spectroscopy experiments to quantify the extent that BiP gradients exist within the ER lumen ( unpublished data ) . Under conditions of ER stress , a greater degree of BiP clustering was observed . The spatial heterogeneity of BiP is displayed via live cell imaging; in contrast , the translocation pore composed of Sec61 is distributed homogenously within the ER membrane ( Figure S1 , Supporting Information ) . Via computationally intensive efforts , and only through providing cooperative action do the advantages of clustering become evident , providing a mechanistic context for the observed differences . In conclusion , the chaperone BiP plays several roles in the ER , namely translocation , protein folding , ER-associated degradation , and modulation of the UPR . All of these functions require that BiP perform multiple tasks to complete the process . In translocation , the accepted model is that of a Brownian ratchet , in which multiple BiP molecules bind to nascent proteins to transport them into the lumen [62] , [63] . BiP's attempt to correctly fold aberrant proteins often takes multiple cycles of binding and release . We show that multiple binding facilitates aggregate dismantling through more coverage on the structures' large surfaces . In addition , our model suggests that the clustering of BiP molecules would be beneficial in terms of efficiency and chaperone cost during protein-folding processes in the ER . | The misfolding of proteins carries important implications for diseases such as Alzheimer's , Parkinson's , cancer , and diabetes . Once misfolded , proteins tend to associate into aggregates that pose a toxic threat to the cell . Chaperones are proteins that rescue the cell from an accumulation of these maladjusted proteins through dissociation of toxic oligomers and proper ( re ) folding . The endoplasmic reticulum ( ER ) is an organelle that serves as the staging ground for the chaperone activities of protein transport , folding , and maturation in the early secretory pathway . We have developed a computational model to investigate potential mechanisms that enable multiple ER-resident molecules working in concert to effectively fold peptides and transport nascent proteins across the ER membrane . Although previous models focused on chaperone interactions with peptides , we have explored the influence of cooperativity among chaperone molecules to assist in protein folding and maturation . We found that chaperone cooperation led to a higher yield of folded molecules compared to when chaperones bound to peptides in a 1∶1 stoichiometry . We have concluded that the clustering or multiple binding of chaperones may facilitate protein folding in vivo . | [
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] | [
"systems",
"biology",
"computer",
"and",
"information",
"sciences",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"computerized",
"simulations"
] | 2014 | BiP Clustering Facilitates Protein Folding in the Endoplasmic Reticulum |
Overexpression of epidermal growth factor receptor ( EGFR ) has been associated with cancer . Targeted inhibition of the EGFR pathway has been shown to limit proliferation of cancerous cells . Hence , we employed Traditional Chinese Medicine Database ( TCM Database@Taiwan ) ( http://tcm . cmu . edu . tw ) to identify potential EGFR inhibitor . Multiple Linear Regression ( MLR ) , Support Vector Machine ( SVM ) , Comparative Molecular Field Analysis ( CoMFA ) , and Comparative Molecular Similarities Indices Analysis ( CoMSIA ) models were generated using a training set of EGFR ligands of known inhibitory activities . The top four TCM candidates based on DockScore were 2-O-caffeoyl tartaric acid , Emitine , Rosmaricine , and 2-O-feruloyl tartaric acid , and all had higher binding affinities than the control Iressa® . The TCM candidates had interactions with Asp855 , Lys716 , and Lys728 , all which are residues of the protein kinase binding site . Validated MLR ( r2 = 0 . 7858 ) and SVM ( r2 = 0 . 8754 ) models predicted good bioactivity for the TCM candidates . In addition , the TCM candidates contoured well to the 3D-Quantitative Structure-Activity Relationship ( 3D-QSAR ) map derived from the CoMFA ( q2 = 0 . 721 , r2 = 0 . 986 ) and CoMSIA ( q2 = 0 . 662 , r2 = 0 . 988 ) models . The steric field , hydrophobic field , and H-bond of the 3D-QSAR map were well matched by each TCM candidate . Molecular docking indicated that all TCM candidates formed H-bonds within the EGFR protein kinase domain . Based on the different structures , H-bonds were formed at either Asp855 or Lys716/Lys728 . The compounds remained stable throughout molecular dynamics ( MD ) simulation . Based on the results of this study , 2-O-caffeoyl tartaric acid , Emitine , Rosmaricine , and 2-O-feruloyl tartaric acid are suggested to be potential EGFR inhibitors .
Target-specific therapies have generated much attention in addition to conventional cancer treatments [1]–[3] . By targeting key molecules essential for cellular function , replication , or tumorigenesis , such therapies may exert cytostatic or cytotoxic effects on tumors while minimizing nonspecific toxicities associated with chemotherapy or irradiation [4] . The epidermal growth factor receptor ( EGFR ) signaling pathway is one of the most important pathways in mammalian cells [5] . Specific ligands , such as epidermal growth factor ( EGF ) and transforming growth factor alpha ( TGFα ) , bind and activate EGFR , triggering autophosphorylation of the intracytoplasmic EGFR tyrosine kinase domain [6] , [7] . The phosphorylated tyrosine kinase residues serve as binding sites for signal transducers and activators of intracellular substrates , which then stimulate intracellular signal transduction cascades that upregulate biological processes such as gene expression , proliferation , angiogenesis , and inhibition of apoptosis [8] . EGFR overexpression has been shown to activate downstream signaling pathways , resulting in cells that have aggressive growth and invasive characteristics [9] . Tumor cell motility , adhesion , metastasis , and angiogenesis have also been associated with stimulated EGFR pathways [10]–[12] . Since EGFR over-expression often differentiates tumor cells from normal cells , it is possible for EGFR inhibitory molecules to act on tumor cells and attenuate their proliferation rates [4] . Several tyrosine kinase inhibitors were approved for clinical use . Iressa® ( gefitinib ) is highly selective for EGFR tyrosine kinase and is commonly used for treating lung cancer [13] . EGFR downstream signaling is competitively inhibited by Iressa® at its ATP binding site [14] . Other therapeutic agents with inhibitory mechanisms similar to Iressa® include Erlotinib ( Tarceva® ) against non-small cell lung cancer ( NSCLC ) and pancreatic cancer [15] , [16] , and Vandetanib ( Zactima® ) against late stage medullary thyroid cancer [17] . Lapatinib ( Tykerb® ) is a dual inhibitor of EGFR and HER2 tyrosine kinases approved for metastatic breast cancer [18] , [19] . Though the effect of Iressa® on lung cancer has been well established , severe side effects has also been reported [20] . Adverse reactions listed under Iressa® product information include diarrhea , skin rash and dryness , nausea , vomiting , haemorrhage , anorexia , asthenia , and in some cases , interstitial lung disease with fatal outcomes [21] . The adverse effects of available treatments necessitate continuous search efforts for alternatives with less toxicity . Computational predictions in biology and biomedicine are of significant importance for generating useful data which otherwise be time-consuming and costly through experiments alone [3] , [22]–[27] . Computational predictions , combined with information derived from structural bioinformatics analysis , can provide useful insights and timely information for both basic research and drug development [28] , [29] . Much cutting-edge cancer drug development has been conducted through the use of computational bioinformatics and modeling [30]–[37] . The powerful ability of modern computational prediction and bioinformatics were adopted in this research to search for novel EGFR inhibitors . Traditional Chinese medicines ( TCM ) are natural substances with therapeutic effects on many diseases [38]–[40] . The vast number of TCM represents a rich resource that can be explored for drug development . We had investigated kinase inhibitor candidates from TCM targeting HER2 and HSP90 receptors before [28] , [41]–[42] . Though EGFR kinase inhibitors have been investigated through different screening and modeling scenarios [43]–[47] , none from TCM compounds has been reported to date . This study utilized the world’s largest TCM Database@Taiwan [48] to screen for potential EGFR inhibitors from TCM compounds and applied structure- and ligand-based methods to evaluate the suitability of candidates as EGFR inhibitors .
The EGFR protein sequence ( EGFR_HUMAN , P00533 ) used in this study was obtained from Swiss-Prot [50] , and the 3D structure ( PDB: 2ITY ) [51] used for analyses was downloaded from Protein Data Bank . The tyrosine kinase was encoded from Phe712-Leu979 , and the ATP binding site was located at Leu718–Val726 . The Traditional Chinese Medicine ( TCM Database@Taiwan ) database ( http://tcm . cmu . edu . tw ) was used to screen for potential EGFR inhibitors from more than 20 , 000 compounds within the database . All compounds were operated using the Prepare Ligands module with Lipinski’s rule of five using Discovery Studio 2 . 5 ( DS 2 . 5; Accelrys Inc . , San Diego , CA ) . Iressa® was selected as the control . The LigandFit program ( DS 2 . 5 ) was used to locate the best docking pose for different confirmations under the Chemistry at HARvard Macromolecular Mechanics ( CHARMm ) force field [52] . Results for the docking studies were ranked according to Dock Score . Twenty ligands with demonstrated inhibition against EGFR were used in this study ( Table S1 ) [53] . Descriptors for each ligand were identified using the Calculate Molecular Properties program in DS 2 . 5 . Predictive models containing five optimum descriptors were generated using the Genetic Function Approximation ( GFA ) algorithm . Descriptors in the model with the highest r2 value were used to construct ligand activity prediction models . A MLR model using the descriptors from the top GFA algorithm was constructed using Matlab Statistics Toolbox ( MathWorks , Natick , MA ) and validated using MLR Leave-One-Out validation [54] . The MLR model was trained with 17 randomly selected ligands with EGFR inhibitory activity ( Table S1 ) and used to predict the activity ( pIC50 ) of the control and TCM candidates . The identical descriptors were normalized to the range of [−1 , +1] and plugged into the libSVM program to generate a SVM prediction model[55] . Following model training with the 17-ligand training set , the SVM model was used to predict the activity of the control and TCM candidates . Ligands used in the previous sections were also used for 3D-QSAR analysis . The 2-dimensional ( 2D ) and 3-dimensional ( 3D ) ligand structures were drawn with ChemBioOffice 2008 ( PerkinElmer Inc . , Cambridge , MA ) under a Molecular Mechanics 2 ( MM2 ) force field . Following ligand alignment , Comparative Molecular Field Analysis ( CoMFA ) and Comparative Molecular Similarities Indices Analysis ( CoMSIA ) models were constructed using partial least squares statistical method ( PLS ) . Cross-Validated ( CV ) correlation coefficient ( q2 ) and non-cross-validation correlation coefficient ( r2 ) were used to validate the models . Biological activities of Iressa® and TCM candidate compounds were predicted using the generated 3D-QSAR contour map . Molecular dynamics ( MD ) of Iressa® and the TCM candidates were simulated using DS2 . 5 Standard Dynamics Cascade and Dynamics package . Sample preparation was conducted under the following parameters: [minimization] steepest descent and conjugate gradient: each with maximum steps of 500 , [heating time] 50 ps , [equilibration time] 200 ps . The simulations were produced with a total production time of 20 ns with NVT , constant temperature dynamics of Berendsen weak coupling method , a temperature decay time of 0 . 4 ps , and a target temperature of 310K . Root mean square deviations ( RMSD ) of protein-ligand complex and individual ligands , total energy of protein-ligand complex , hydrogen bond ( H-bond ) , and H-bond distance were analyzed using the Analyze Trajectory function following MD simulation .
The top four TCM candidates with the highest Dock Score were 2-O-caffeoyl tartaric acid , Emitine , Rosmaricine , and 2-O-feruloyl tartaric acid ( Table 1 ) . Corresponding scaffolds of the top TCM candidates are illustrated in Figure 1 . Iressa® , Emitine , and Rosmaricine had amine groups available for H bonding whereas 2-O-Caffeoyl tartaric acid and 2-O-feruloyl tartaric acid had carbonyl groups . The different residues available for H bonding resulted in different binding poses ( Figure 2 ) . Binding of Iressa® ( Figure 2a ) , Emitine ( Figure 2c ) , and Rosmaricine ( Figure 2e ) to tyrosine kinase were located within the pocket , with H-bonds formed between the amine group of the ligand compounds and the carboxyl group of Asp855 . 2-O-Caffeoyl tartaric acid ( Figure 2b ) and 2-O-feruloyl tartaric acid ( Figure 2e ) docked outside the tyrosine kinase pocket and formed multiple H-bonds through their carboxyl groups with Lys716 and Lys728 . The binding location of 2-O-caffeoyl tartaric acid and 2-O-feruloyl tartaric acid directly blocks the ATP binding site of tyrosine kinase located from Leu718–Val726 . Dock scores of each TCM candidate is given in Table 1 . All candidates have higher dock scores than Iressa® , indicating higher binding affinities to the tyrosine kinase receptor than Iressa® . Representative descriptors from the top GFA algorithm include: Num_H_Acceptors_Lipinski ( equivalent of N+O count ) , Molecular_SurfaceArea ( the total surface area for each molecule using a 2D approximation ) , Kappa_1 ( Kappa Shape Indices ) , PMI_Y ( principle moment of inertia Y-component ) , and Shadow Xlength ( length of molecule in the X dimension ) . The descriptors were validated using Leave-One-Out method which is the most objective of all available cross-validation methods [56] . The MLR model established with the determined descriptors was: The SVM model was also established with the five identified descriptors using libSVM . Correlation between the predicted and observed pIC50 activities on EGFR ligands of known activity using the constructed MLR and SVM models were illustrated in Figure 3a and 3b , respectively . Correlation coefficients based on the training set were 0 . 7858 for the MLR model and 0 . 8754 for the SVM model . Activity predictions of Iressa® and the TCM candidates using MLR and SVM were summarized in Table 1 . Both models indicate that Iressa and the TCM candidates are compounds with acceptable predicted activities . Predicted activities ( pIC50 ) of Iressa by the trained MLR and SVM models were 6 . 715 and 5 . 110 , respectively . The Iressa activity predicted by SVM was closer to experimentally determined Iressa activities ( pIC50 ) between 4 . 76–5 . 96 [57] , thus SVM values may be more accurate predictions of the actual activity . The results of CoMFA and CoMSIA model generation are detailed in Table 2 . Steric field was the sole factor in the CoMFA model since the electrostatic field value was zero . Cross-validated ( q2 ) and non-cross-validated ( r2 ) correlation coefficient values of 0 . 721 and 0 . 986 , respectively , indicated a high level of confidence for this model . The small standard error of estimates ( SEE ) and large F-test value further supported the reliability of this model . In contrast , CoMSIA models were influenced by multiple factors including steric field , hydrophobic region , and hydrogen bond donor/acceptors . Among all generated versions of the CoMSIA model , CoMSIA_SHD had the highest r2 ( 0 . 988 ) , lowest SEE ( 0 . 133 ) , and highest F value ( 134 . 272 ) , thus was selected as the optimum CoMSIA model for use in this study . The pIC50 of 20 ligands predicted by the constructed CoMFA and CoMSIA models were compared with observed pIC50 reported by Fidanze et al . [53] in Table 3 . In general , both models gave similar predicted values and were close to the experimentally determined activities . Correlations between predicted and observed pIC50 using CoMFA and CoMSIA models are summarized in Figure 4a and 4b , respectively . High correlation coefficients validated the reliability of the constructed CoMFA ( r2 = 0 . 9860 ) and CoMSIA ( r2 = 0 . 9877 ) models . Ligand activities of Iressa® and the TCM candidates can be predicted based on structural conformation to the 3D-QSAR feature map , including features in steric field , hydrophobic field , and H-bond donor/acceptor characteristics . As illustrated in Figure 5 , Iressa and the TCM candidates were able to match the generated 3D-QSAR model features . The benzene in Iressa® favored steric and hydrophobic fields , and H-bond was favored between its amine group and Asp855 . In 2-O-Caffeoyl tartaric acid , the benzene structure favored steric and hydrophobic fields , and the carboxyl group favored H-bond formation with Lys716 and Lys728 . The carbon chain structure in Emetine contoured to the steric and hydrophobic fields , and the amine group favored H-bond formation with Asp855 . Rosmaricine had benzene and isopropyl structures that favored steric and hydrophobic fields , and an amine group that favored H-bond with Asp 855 . The benzene structure in 2-O-feruloyl tartaric acid favored steric fields and the carboxyl group favored H-bond formations with Lys716 and Lys728 . Iressa® and the TCM candidates have structural components that contour to the features of the 3D-QSAR model , thus were likely to be biologically active . Binding stability of the control and TCM candidates was validated using MD simulation . RMSDs of protein-ligand complex ( Figure 6a ) and individual ligand ( Figure 6b ) stabilized after 10 ns . The RMSDs of the protein-ligand complexes stabilized at approximately 1 . 6Å . With regard to individual ligands , the RMSDs of Iressa and 2-O-caffeoyl tartaric acid was 2 . 0 and 1 . 6Å , respectively . All other compounds registered RMSD values of approximately 1 . 0Å . The lower RMSD values of the TCM candidates suggest more stability within the receptor compared to Iressa . The energy trajectory of each compound is shown in Figure 6c . Complexes formed by Rosmaricine and 2-O-feruloyl tartaric acid had the lowest total energy ( <−14 , 800 kcal/mol ) , followed by Iressa® and Emetine ( approximately −14 , 700 kcal/mol ) , and 2-O-caffeoyl tartaric acid ( −14 , 600 kcal/mol ) . Stabilization of total energy in ligand-protein complexes was achieved after 12 ns . H-bond distance profiles in the EGFR receptor were summarized in Figure 7 . A single H-bond between the amine group on Iressa® and the carboxyl group on Asp855 was formed after 9 . 74 ns and stabilized after 20 ns ( Figure 7a ) . Two H-bonds were formed between the carboxyl group of 2-O-caffeoyl tartaric acid and Lys716 and Lys728 of the EGFR receptor ( Figure 7b ) . The formation of two H-bonds contributed to a higher stability between 2-O-caffeoyl tartaric acid and the EGFR receptor . However , an increase in H-bond distance was observed towards the end of the 20 ns simulation period , suggesting a weakening of the H-bond at Lys728 . Emetine formed a total of four H-bonds with the receptor , two with Asp722 and two with Ala855 ( Figure 7c ) . Bond distances stabilized after 10 ns for Ala722 and 4 ns for Asp855 . Rosmaricine formed three H-bonds each at Asp841 and Arg855 ( Figure 7d ) . The multiple H-bonds enabled Rosmaricine to remain in a stable state within the protein . 2-O-Feruloyl tartaric acid also formed multiple H-bond at Lys716 and Lys728 , enhancing its stability within the receptor site ( Figure 7e ) . However , similar to 2-O-caffeoyl tartaric acid , an increase in H-bond distance was also observed at Lys728 for 2-O-feruloyl tartaric acid . These observations suggest that the bond at Lys728 weakens throughout the MD simulation process , and that the H-bond at Lys716 may be the primary bond for 2-O-caffeoyl tartaric acid and 2-O-feruloyl tartaric acid . In addition , periodic fluctuations in H-bond distances were observed in 2-O-caffeoyl tartaric acid , Rosmaricine , and 2-O-feruloyl tartaric acid . These phenomena can be attributed to the rotation of the amine group where the H-bond is formed . These MD results support our docking findings which identify Asp855 , Lys716 , and Lys 728 as key residues for docking . As determined in the CoMSIA model , hydrophobic interactions were key factors contributing to ligand bioactivity . Toward the final 20 ns of analysis , hydrophobic amino acids surrounding the docking region were Leu718 , Val726 , Ala743 , Cys775 , Phe795 , Cys797 , and Leu844 . The hydrophobic subgroups of Iressa® , Emetine , and Rosmaricine were surrounded by Val726 , Cys797 , and Leu844 ( Figure 8a ) . Hydrophobic groups of 2-O-caffeoyl tartaric acid were also surrounded Val726 , Cys797 , and Leu844 ( Figure 8b ) . The hydrophobic region of 2-O-feruloyl tartaric acid was attracted to the Phe795 on EGFR ( Figure 8b ) . The significance of matching the hydrophobic region of the ligand to that of the receptor may be to increase stability of the ligand-protein complex , and contribute to the bioactivity of the activated ligand . Our results indicate that Iressa® and the TCM candidates remained stable within the EFGR hydrophobic area following MD simulations . Structural and ligand based methods supported 2-O-caffeoyl tartaric acid , Emetine , Rosmaricine , and 2-O-feruloyl tartaric acid as potential EGFR inhibitors . Structurally , the TCM candidates were capable of forming H-bonds with key residues Asp855 , Lys716 , and Lys728 and matched hydrophobic regions of the receptor . Bioactivity of the candidates were evaluated using validated MLR , SVM , CoMFA , and CoMSIA models . All models indicated that the TCM candidates have good predicted bioactivity . Molecular simulation results further supported the high potential for the TCM candidates in drug development . Iressa® , the drug currently used clinically , bound to the ERGF receptor through a single H-bond at Asp855 . In comparison , multiple H-bonds formed at Asp855 and additional H-bonds formed at Ala722 and Arg841 increase the stability of Emetine and Rosmaricine , respectively . The ability of carboxyl groups in 2-O-caffeoyl tartaric acid and 2-O-feruloyl tartaric acid to form multiple H-bond networks that directly blocked the ATP binding site was also a unique characteristic worthwhile of further investigation . Contour to hydrophobic regions of the TCM candidates within the receptor site provides additional support for the stability of the protein-ligand complex . In summary , using different simulation and validation methods , we have identified four TCM compounds that may have potential as novel EGFR inhibitors . As the four TCM compounds have two distinctive types of binding locations and bond formation within the EGFR binding site , we suggest exploring the possibility of connecting Emetine/Rosmaricine with 2-O-caffeoyl tartaric acid/2-O-feruloyl tartaric acid through a spacer . The connection could allow more of points of attachment , which in turn would contribute to more stable binding within the tyrosine kinase site . | Tumor growth is associated with overexpression of epidermal growth factors receptors . Targeted control of EGFR by EGFR inhibitors is an attractive therapy alternative to conventional cancer treatment that offers specificity and reduced adverse effects . The purpose of this study was to identify natural compounds from traditional Chinese medicine that may be used as EGFR inhibitors . The top four TCM compounds with the highest binding affinity to EGFR were selected and their suitability as EGFR inhibitors confirmed with different statistical prediction models . The candidate compounds had higher bioactivity than Iressa® , the drug that is clinically used . The TCM compounds also met key structural components that were characteristic among known inhibitors . In addition , the binding between TCM compounds and EGFR were stable which is a fundamental requirement for any targeting drug . Results from bioactivity prediction , structural component matching , and binding stability all point to the possibility of these TCM compounds as suitable EGFR inhibitor candidates . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] | [
"physics",
"computer",
"science",
"medicinal",
"chemistry",
"computational",
"chemistry",
"computer",
"modeling",
"chemistry",
"biology",
"computational",
"biology",
"computerized",
"simulations",
"biophysics"
] | 2011 | Identification of Potent EGFR Inhibitors from TCM Database@Taiwan |
Cells often mount transcriptional responses and activate specific sets of genes in response to stress-inducing signals such as heat or reactive oxygen species . Transcription factors in the RpoH family of bacterial alternative σ factors usually control gene expression during a heat shock response . Interestingly , several α-proteobacteria possess two or more paralogs of RpoH , suggesting some functional distinction . We investigated the target promoters of Rhodobacter sphaeroides RpoHI and RpoHII using genome-scale data derived from gene expression profiling and the direct interactions of each protein with DNA in vivo . We found that the RpoHI and RpoHII regulons have both distinct and overlapping gene sets . We predicted DNA sequence elements that dictate promoter recognition specificity by each RpoH paralog . We found that several bases in the highly conserved TTG in the −35 element are important for activity with both RpoH homologs; that the T-9 position , which is over-represented in the RpoHI promoter sequence logo , is critical for RpoHI–dependent transcription; and that several bases in the predicted −10 element were important for activity with either RpoHII or both RpoH homologs . Genes that are transcribed by both RpoHI and RpoHII are predicted to encode for functions involved in general cell maintenance . The functions specific to the RpoHI regulon are associated with a classic heat shock response , while those specific to RpoHII are associated with the response to the reactive oxygen species , singlet oxygen . We propose that a gene duplication event followed by changes in promoter recognition by RpoHI and RpoHII allowed convergence of the transcriptional responses to heat and singlet oxygen stress in R . sphaeroides and possibly other bacteria .
Transcriptional responses to stress are critical to cell growth and survival . In bacteria , stress responses are often controlled by alternative σ factors that direct RNA polymerase to transcribe promoters different from those recognized by the primary σ factor [1] , [2] . Therefore , identifying the target genes for a particular alternative σ factor can help identify the functions necessary to respond to a given stress . For example , the transcriptional response to heat shock in Escherichia coli uses the alternative σ factor σ32 to increase synthesis of gene products involved in protein homeostasis or membrane integrity [3] . From available genome sequences , proteins related to E . coli σ32 are conserved across virtually all proteobacteria . This so-called RpoH family of alternative σ factors is characterized by a conserved amino acid sequence ( the “RpoH box” ) that is involved in RNA polymerase interactions [4] , [5] . RpoH family members also possess conserved amino acid sequences in σ factor regions 2 . 4 and 4 . 2 that interact with promoter sequences situated approximately −10 and −35 base pairs upstream of the transcriptional start sites , respectively [6] . However , the definition of functional promoters for this family of alternative σ factor using only the presence or the extent of sequence identity for the predicted −10 and −35 binding regions is not a sufficient predictor of transcription activity [7] . While bacteria often possess many alternative σ factors , they usually possess only one member of the RpoH family . However , several α-proteobacteria , including Brucella melitensis [8] , Sinorhizobium meliloti [9] , [10] , Bradyrhizobium japonicum [11] , [12] , Rhizobium elti [13] and Rhodobacter sphaeroides [14] , possess two or more RpoH homologs . In some cases , one or more of these RpoH homologs completely or partially complement the phenotypes of E . coli ΔrpoH mutants , suggesting that these proteins can functionally interact with RNA polymerase and recognize similar promoter elements [8]–[11] , [14] , [15] . However , in the nitrogen-fixing plant symbiont Rhizobium elti , the ΔrpoH1 mutant was sensitive to heat and oxidative stress while the ΔrpoH2 mutant was sensitive to osmotic stress [13] . Therefore , the additional members of the RpoH family in α-proteobacteria may have roles in other stress responses . Previous work demonstrated that either R . sphaeroides RpoHI or RpoHII can complement the temperature sensitive phenotype of an E . coli ΔrpoH mutant; that singly mutant R . sphaeroides strains lacking either rpoHI or rpoHII are able to mount a heat shock response; and that RNA polymerase containing either RpoHI or RpoHII can initiate transcription from a common set of promoters in vitro [14]–[16] . Combined , these observations suggest that RpoHI and RpoHII have some overlapping functions in R . sphaeroides . On the other hand , in vitro transcription assays identified promoters that were selectively transcribed by either RpoHI or RpoHII [14] , [15] . Moreover , rpoHII is under direct transcriptional control of RpoE , a Group IV alternative σ factor that acts as the master regulator of the response of R . sphaeroides to singlet oxygen stress [17]–[19] . These later results and the recent observation that a ΔrpoHII mutant is more sensitive to singlet oxygen stress than the wild-type strain [15] , [17] suggest that RpoHI and RpoHII also have distinct functions in R . sphaeroides . Finally , global protein profiles of R . sphaeroides mutants lacking rpoHI , rpoHII , or both genes , suggested that RpoHI and RpoHII have distinct and overlapping regulons [15] , [17] , [20] . However , the extent of genes that are direct targets for RpoHI and RpoHII is still unknown because past studies have been unable to distinguish direct from indirect effects on gene expression or identify all the direct targets for either of these σ factors . In this study , we characterized the RpoHI and RpoHII regulons using a combination of expression microarrays , chromatin immunoprecipitation and computational methods which have been previously been shown to predict correctly direct targets for other alternative σ factors or DNA binding proteins [19] , [21] . We found that the genes predicted to be common to the RpoHI and RpoHII regulons function in protein repair or turnover , membrane maintenance , and DNA repair . Genes specific to the RpoHI regulon encode other proteins involved in protein maintenance and DNA repair , whereas genes specific to the RpoHII regulon include proteins involved in maintaining the oxidation-reduction state of the cytoplasmic thiol pool . We used information on the members of each regulon to generate and test hypotheses about DNA sequences that determine promoter specificity of these two RpoH homologs . The observed properties of these two R . sphaeroides RpoH homologs illustrate how duplication of an alternative σ factor and subsequent changes in promoter recognition could have allowed convergence of transcriptional responses to separate signals . In the case of R . sphaeroides , we predict that these events allowed convergence of the transcriptional responses to heat shock and singlet oxygen stresses to be under control of these two RpoH paralogs .
To define members of the RpoHI and RpoHII regulons , we monitored transcript levels and protein-DNA interactions in R . sphaeroides strains ectopically expressing either RpoHI or RpoHII . To generate these strains , we constructed low copy plasmids carrying rpoHI or rpoHII under the control of an IPTG-inducible promoter [22] and conjugated them into R . sphaeroides mutant strains lacking rpoHI [16] or rpoHII [15] , respectively . To induce target gene expression , we exposed exponentially growing aerobic cultures to IPTG for one generation before cells were either harvested to extract total RNA for analysis of transcript levels or treated with formaldehyde to prepare samples for chromatin immunoprecipitation on a chip ( ChIP-chip ) assays . The Western blot analysis used to measure levels of these alternative σ factors demonstrates that cells ectopically expressing RpoHI and RpoHII contained each protein at levels comparable to those following either heat shock or singlet oxygen stress ( Figure 1 ) . Thus , these strains can be used to characterize members of the RpoHI and RpoHII regulons . As controls for this experiment , we measured the abundance of individual RpoH proteins and a control transcription factor ( PrrA ) [23] , which is not known to be dependent on either alternative σ factor for its expression , when wild type cells were exposed to either heat or singlet oxygen stress . This analysis showed that RpoHI is detectable prior to heat stress , but its levels increase 10 and 20 minutes after the shift to increased temperature ( Figure 1A ) . RpoHI levels remain elevated after the temperature shift but they decline within 60 minutes after heat shock , suggesting that as in the case of E . coli σ32 , there is an initial rise in RpoHI levels immediately on heat shock before they return to a new steady state level at elevated temperature [24] . RpoHII was also detected prior to exposure to singlet oxygen and within 10 minutes of exposure to this reactive oxygen species , levels of this protein were increased ( Figure 1B ) . Levels of RpoHII found within 20 minutes after exposure to singlet oxygen remained relatively constant over the time course of this experiment , suggesting a continuous requirement for RpoHII during this stress response ( Figure 1B ) . The abundance of the control transcription factor PrrA did not follow these same trends , suggesting that the observed increases in individual RpoH proteins was associated with these stress responses . In addition , the abundance of individual RpoH proteins did not increase significantly to both stress responses , as expected if these increases were not due to a general increase in protein levels in response to different signals . To identify transcripts that were increased in abundance as a result of RpoHI or RpoHII activity , we compared mRNA levels of cells expressing RpoHI or RpoHII ectopically to those of control cells lacking either rpoHI or rpoHII . We selected differentially expressed genes with a significance level set for a false discovery rate ≤5% and that displayed at least 1 . 5-fold higher transcript levels in cells expressing either RpoH family member . This analysis revealed that transcripts from 241 and 186 genes were increased by expression of RpoHI and RpoHII , respectively ( Figure 2 ) . These two sets of differentially expressed genes have 60 genes in common . We recognize that some of these differentially-expressed transcripts might be not be direct targets for RpoHI and RpoHII . Therefore , to determine which of the above genes were directly transcribed by RNA polymerase holoenzyme containing either RpoHI or RpoHII , we performed ChIP-chip assays from comparable cultures to map direct interactions of RpoHI or RpoHII with genomic DNA . We were able to raise specific antibodies against RpoHII that performed well for the ChIP-chip assay , but repeated attempts to raise suitable antibodies against RpoHI failed . Therefore , we placed a FLAG polypeptide tag [25] at the N-terminus of the RpoHI protein sequence and used anti-FLAG monoclonal antibodies to perform the ChIP-chip assay . As a control we tested and showed that addition of the polypeptide tag did not alter the activity and specificity of RpoHI by comparing the mRNA level profiles of cells expressing the tagged version of RpoHI with cells expressing wild-type RpoHI ( Figure S1 ) . In addition , other control experiments showed there was no detectable cross-reaction between FLAG-RpoHI and the antibody used to precipitate RpoHII , and vice versa ( data not shown ) . From the ChIP-chip analysis we identified 812 and 1353 genomic regions enriched after immunoprecipitation with antibodies against RpoHI and RpoHII , respectively , using a significance level set for a false discovery rate ≤5% . Because the signal from a single σ factor binding site extends on average over a 1 kb region , some enriched regions may contain multiple binding sites . To increase the resolution of the putative RpoHI and RpoHII binding sites , we identified the modes of the ChIP-chip signal distributions within each enriched region . This adjustment increased the number of putative binding sites for RpoHI and RpoHII to 1085 and 1765 , respectively . We then identified all the annotated genes that contained a ChIP-chip peak within 300 base pairs upstream of their start codons as a way to define candidate genes or operons in the RpoHI or RpoHII regulons . Included in this list of potential regulon members were genes that are predicted to be co-transcribed using a previous computational analysis of R . sphaeroides operon organization ( http://www . microbesonline . org/operons/ ) [26] . Therefore , by these criteria , the upper limits of the total numbers of genes potentially regulated by RpoHI or RpoHII are 1120 and 1616 , respectively ( Figure 2 ) . We recognized that a significant number of the putative RpoHI or RpoHII promoters may not be assigned from the ChIP-chip dataset alone , especially because promoter orientation needs to be considered and that because σ factor or RNA polymerase binding events do not always promote transcription . Therefore , we refined the respective RpoHI and RpoHII regulons by intersecting the lists of target genes identified from the ChIP-chip analysis with the lists of candidate genes identified from the expression profiling analysis . After this intersection , we predict that the RpoHI regulon contains 175 genes and the RpoHII regulon contains 144 genes with 45 genes common to both regulons ( Figure 2 ) . Upon examining the annotations of these predicted target genes , the 45 genes that are members of both the RpoHI and RpoHII regulons are predicted to encode mainly for functions related to the electron transport chain , protein homeostasis , and DNA repair ( Table 1 and Table S1 ) . The 130 predicted members of the RpoHI regulon also encode functions in these three groups , but with a larger representation for functions associated with protein homeostasis . The 99 predicted members of the RpoHII regulon include fewer proteins predicted to play a role in protein homeostasis and a larger number of proteins predicted to help maintain the oxidation-reduction state of the cytoplasmic thiol pool . However , a large number of genes in both the unique and overlapping RpoHI and RpoHII regulons are annotated as having no predicted functions . Overall , this analysis revealed that RpoHI or RpoHII activate a large set of distinct and overlapping sets of target genes . Previous work indicated that RpoHI and RpoHII can recognize and initiate transcription from similar promoter sequences [14] , [15] , [20] . The characterization of their respective regulons also suggests that some promoters can be transcribed by both σ factors while others are specific to either RpoHI or RpoHII . Therefore , we hypothesized that while the promoter sequences of the two σ factors may be similar , different sequence-specific interactions of RpoHI or RpoHII with promoter elements are the basis of promoter specificity for transcription initiation by RNA polymerase . To overcome the limited resolution of the ChIP-chip experiment and predict determinants of promoter specificity for RpoHI or RpoHII , we searched the regions upstream of genes in each regulon for conserved sequence elements ( 137 sequences for RpoHI and 120 sequences for RpoHII ) . The conserved sequence elements we identified mapped to putative promoter elements that were within 100 bp of the coordinates of the modes of the distributions of the ChIP-chip signal . Thus , the predictions of these searches identified conserved sequence elements that were in agreement with the experimental data . In addition , even though we analyzed the individual RpoHI and RpoHII regulons independently for these motifs , the sequence alignment algorithm converged to the same sequence elements for promoters that were predicted to be recognized by both RpoHI and RpoHII . This result is not surprising given that both σ factors have similar amino acid sequences in their DNA recognition regions and are thus expected to recognize similar promoter sequences . However , this observation supports the hypothesis that RpoHI and RpoHII recognize common promoter sequences in their respective target genes as opposed to distinct promoters . To predict specificity sequence determinants for each RpoH paralog , the putative distinct and overlapping promoter sequences were sorted into three groups according to the expression profiling and ChIP-chip data sets and converted into sequence logos ( Figure 3 , Table S2 ) . The sequence logos derived from the three groups include: two groups that are preferentially or selectively bound and transcribed by either RpoHI or RpoHII and one group that is bound and transcribed by both σ factors . As noted above , some promoters appear to be bound by RpoHI or RpoHII without inducing detectable changes in transcript levels . We aligned these promoters separately to determine if they possessed unique characteristics , but no significant differences were detected ( data not shown ) . The conservation of a TTG motif in the −35 region in all three logos is consistent with the importance of this triplet in a previous analysis of at least one promoter known to be recognized by both RpoHI and RpoHII [27] . However , there was also evidence for sequence-specific elements in the logos for each RpoH paralog . In the logo for the RpoHI-dependent promoters , a cytosine is overrepresented at position −37 and a thymine is overrepresented at position −9 . In the logo for RpoHII-dependent promoters , cytosine and thymine are overrepresented at positions −14 and −13 , respectively . Overall , the comparison between RpoHI and RpoHII-specific promoter logos allowed us to identify significant differences in the promoter sequences that may be used to adjust promoter selectivity and strength for RpoHI or RpoHII . In addition , the predicted sequence elements for RpoHI or RpoHII promoters are not mutually exclusive . Rather , it appears that promoter specificities for RpoHI or RpoHII are distributed along a gradient using a combination of specific bases at various positions of the −35 or −10 promoter elements . To test predictions about specificity determinants derived from these logos , we cloned several putative promoters upstream of a lacZ reporter gene and integrated these into the genome of a R . sphaeroides ΔrpoHI ΔrpoHII mutant [15] via homologous recombination . The activity of each promoter was measured by assaying β-galactosidase activity in these R . sphaeroides reporter strains ectopically expressing either RpoHI or RpoHII ( Figure 4 ) at levels comparable to those found during a stress response ( see above and Figure 1 ) . The RSP_1173 , RSP_1408 , and RSP_1531 promoters ( which were either predicted to be members of the RpoHI regulon or , in the case of RSP_1173 , known to be heat inducible and transcribed by RpoHI [16] , had significant activity in the strain expressing RpoHI , but not when the same strain expressed RpoHII ( Figure 4 ) . In contrast , the RSP_2314 , RSP_2389 , and RSP_3274 promoters ( which were either predicted to be members of the RpoHII regulon by our analysis or known to be induced by conditions that generate singlet oxygen [17] , [18] , [20] ) showed activity in the presence of RpoHII but not RpoHI ( Figure 4 ) . Finally , the RSP_1207 and RSP_2617 promoters ( which were predicted to be transcribed by both RpoH proteins and , in the case of RSP_1207 , known to be transcribed by RNA polymerase holoenzyme containing either RpoH homolog [15] showed activity in cells containing either RpoHI or RpoHII ( Figure 4 ) . Overall , these results support predictions about members of the RpoHI or RpoHII regulons derived by combining the transcription profiling , ChIP-chip and computational analyses . To test the predictions about the contributions of individual bases to promoter recognition , we measured the activity of R . sphaeroides RpoHI with an existing library of mutant E . coli groE promoters fused to a lacZ reporter in an E . coli tester strain [7] . The data from this analysis revealed that base substitutions in the TTG motif of the −35 region of this RpoH-dependent promoter ( positions −36 , −35 , and −34 ) reduced its activity by at least 80% with RpoHI ( Figure 5A ) , as expected from the predictions of promoter logo . We also found a slight increase in promoter activity when position −32 was changed to a cytosine , even though the C-32 is not conserved in RpoHI promoters . This observation is consistent with the results of a previous mutational analysis showing that E . coli σ32 prefers a cytosine at position −32 when the alanine at position 264 of its amino acid sequence is substituted to an arginine ( corresponding to R267 of RpoHI ) [28] , but also suggests that the −32 position is not utilized to distinguish between RpoHI- and RpoHII-specific promoters . In the −10 region of the groE promoter , substitutions of the cytosine at position −14 for an adenine or guanine , the cytosine at position −13 for an adenine , or substitution of the thymine at position −11 for a cytosine , each reduced RpoHI-dependent promoter activity . In addition , a substitution of the adenine at position −12 for a cytosine or changing the thymine at position −9 for any other base reduced RpoHI-dependent activity by >90% . These observations are consistent with the conservation of a thymine at position −9 of the derived RpoHI promoter logo ( Figure 3 ) . To test the predicted requirement of RpoHI for a thymine at position −9 , we also analyzed the properties of two R . sphaeroides promoters in this E . coli tester strain . Activity of the RpoHI-dependent RSP_1531 promoter was reduced by 90% when the thymine at position −9 was changed to a cytosine , whereas the RpoHII-dependent RSP_2314 promoter had higher RpoHI-dependent activity when a thymine was placed at position −9 ( Figure 5B ) . Therefore , this analysis confirmed that position −9 plays a critical role in promoter specificity for RpoHI . In conclusion , the measured effects of mutations in the E . coli groE promoter on RpoHI-dependent transcription confirmed that our models captured elements that are critical for promoter recognition by RpoHI . We were unable to test activity of R . sphaeroides RpoHII against this groEL promoter library in the same E . coli tester strain ( data not shown ) . Instead , we generated a small set of point mutations in the P1 promoter of the R . sphaeroides cycA promoter ( Figure 3 ) which was previously shown to be transcribed by both RpoHI and RpoHII [27] and measured activity from single-copy fusions of these mutant promoters to lacZ in cells that either lacked both RpoH homologs or that contained a single rpoH gene under control of an IPTG-inducible promoter ( Materials and Methods ) . By analyzing this promoter library , we found that a G to T mutation at position −36 of cycA P1 ( G-36T ) increased its transcription by both RpoHI and RpoHII ( Figure 5C ) . This result is consistent with the high predicted information content for T at this position for both RpoHI and HII ( Figure 3 ) , as well as the previous observation that the overall increase in activity of cycA P1 is caused by the G-36T mutation [27] . While our RpoHI and RpoHII promoter models ( Figure 3 ) predict that a C could be allowed at position −36 , a G-36C mutation lowered activity with RpoHII and had no positive impact on transcription by RpoHI ( Figure 5C ) . Due to the significantly increased in activity from the G-36T mutation in cycA P1 , all of the other promoter mutations we tested were generated in this background . Mutations we tested in the −35 region , T-35C and G-34C , resulted in virtually complete loss of cycA P1 activity with either RpoHI and RpoHII when compared to their G-36T parent promoter ( Figure 5C ) , indicating that these bases are essential for transcription initiation by both RpoH homologs . Based on the relatively low information content predicted by our models for other positions in the −35 element ( Figure 3 ) , we did not test the effects of other mutations in this region on promoter selectivity by RpoH homologs . In the predicted −10 region , A-12 has very high information content for both RpoHI and RpoHII , but the sequence logo suggests a T at this position might allow selective recognition by RpoHI ( Figure 3 ) . Indeed , a promoter containing a T at position −12 is still active only with RpoHI , suggesting that A-12 is essential for RpoHII activity but not RpoHI activity . The T at position −9 of cycA P1 is also predicted to have significantly higher information content for RpoHI than RpoHII , while a C at this position should have more information content for RpoHII than RpoHI ( Figure 3 ) . As predicted , we found RpoHII retained significant activity after placing a T-9C mutation in the context of the G-36T cycA P1 promoter . Furthermore , we found that this mutation completely abolished its activity with RpoHI , illustrating the high information content of a T at this position for transcription by this RpoH homolog . The importance for a T at the analogous position was also observed when testing activity of mutant E . coli groE promoters with RpoHI ( T-9C mutation Figure 5A ) or assaying function of the R . sphaeroides RSP_1531 promoter ( which contains a T , Figure 5B ) that is only transcribed to a detectable level by RpoHI ( Figure 4 ) . Finally , we also replaced the A at position −10 of the cycA P1 promoter with a G , as the sequence logo suggests there to be little information content at this position for either RpoHI or RpoHII ( Figure 3 ) . As predicted , there is little impact of the A-10G mutation on promoter function , though activity with RpoHII is more significantly reduced than that with RpoHI activity ( Figure 5C ) .
This work revealed a surprisingly extensive overlap of the RpoHI and RpoHII regulons even though these two homologs activate transcriptional responses to different signals in R . sphaeroides . This suggests that genes activated by these two pathways of the transcriptional regulation network play a role in the physiological response to both these , and even possibly , other stresses . Indeed , the genes regulated by both RpoHI and RpoHII encode known or annotated functions involved in protein homeostasis , DNA repair , and maintenance of cell membrane integrity ( Table 1 ) . These types of functions are central to cell viability and may be relevant for the physiological responses to multiple stresses that can have broad primary and secondary effects on cells . Indeed , the predicted functions of the overlapping members of the RpoHI and RpoHII regulons encode functions that are also part of the general stress response regulons for σS in E . coli or σB in Bacillus subtilis [31] , [32] . Interestingly , σS homologs are mostly present in β- and γ-proteobacteria , but to date absent from sequenced genomes of α-proteobacteria like R . sphaeroides ( http://img . jgi . doe . gov/ ) [33] . Thus , it is possible that the set of genes controlled by both RpoHI and RpoHII is part of a general stress response that is common to the heat shock , singlet oxygen and possibly other uncharacterized signals in R . sphaeroides [14] , [15] , [17] , [18] , [20] . This hypothesis is supported by the observation that R . sphaeroides and R . elti strains lacking both RpoHI and RpoHII are more sensitive to several conditions than strains lacking only one of these proteins [13] , [15] , [20] . In considering the scope of functions that are regulated by both RpoHI and RpoHII , it is also important to note that this set of genes may be larger than the one we characterized because some promoters known to be transcribed by both σ factors were only marginally affected by ectopic expression of either RpoHI or RpoHII . For example , the RSP_2310 ( groES ) promoter was shown to be transcribed by both RpoHI and RpoHII in previous in vitro experiments [14] and was detected by our ChIP-chip experiment to be bound by both RpoHI and RpoHII , but did not meet all the criteria of our analysis . Thus , the groES promoter , like other promoters , may be subject to complex regulation in vivo . Our data also significantly extend the number and types of functions that are specifically controlled by RpoHI or RpoHII ( Table 1 ) . We expected to find specific sets of target genes because strains lacking either RpoHI or RpoHII displayed different phenotypes [14] , [15] , [17] , [20] . While previous results indicated that accumulation of ∼25 proteins was dependent on RpoHII [17] , our data indicate that some 150 genes are directly controlled by each R . sphaeroides RpoH paralog . Genes in the direct but RpoHI-specific regulon encode functions that are involved in protein homeostasis , maintaining membrane integrity , and DNA repair , as is found for the E . coli σ32 regulon [3] ( Table 1 ) The RpoHI specific regulon is also predicted to encode cation transporters and proteins in the thioredoxin-dependent reduction system ( Table 1 ) . Ion transporters can aid the heat shock stress response since exporting cations like iron , which may be released by thermal denaturation of damaged iron-sulfur or other metalloproteins , decreases secondary effects caused by formation of toxic reactive oxygen species [34] . The thioredoxin-dependent reduction system reduces disulfide bonds and peroxides , which are created by protein oxidation , and thereby helps maintain cytoplasmic proteins in a reduced state [35] . Inclusion of these functions in the RpoHI regulon suggests that oxidative damage may be an important secondary effect of heat shock , perhaps caused by protein denaturation or permeabilization of the cell envelope . Overall , these results support the hypothesis that the function of RpoHI in R . sphaeroides is similar to that of σ32 in E . coli for the response to heat shock stress . In addition , it is also possible that RpoHI plays a role in the R . sphaeroides response to other forms of stress . There is precedent for roles of σ32 homologs in other stress responses by other bacteria since the activity of RpoH in Caulobacter crescentus is increased by heavy metal stress [36] . In contrast , rpoHII transcription is under direct control of a Group IV alternative σ factor ( RpoE ) that serves as the master regulator of the singlet oxygen stress response [18] . In addition , an R . sphaeroides ΔrpoHII mutant is more sensitive to singlet oxygen than a wild-type or ΔrpoHI strain [15] , [17] . Therefore , members of the direct RpoHII-specific regulon might be expected to play an important role in the response to singlet oxygen stress . Among the genes in the RpoHII-specific regulon are others predicted to function in maintaining membrane integrity and performing DNA repair , both potential targets for damage by singlet oxygen . However , the RpoHII–specific regulon contains fewer genes encoding functions related to protein homeostasis than found in the RpoHI regulon ( Table 1 ) . Other functions apparently unique to the RpoHII regulon include the glutathione-dependent reduction system , which like the thioredoxin-dependent system repair oxidized protein residues and maintain a reduced cytoplasm ( Table 1 ) . Even though the thioredoxin- and gluthatione-dependent reduction systems serve similar cellular functions , they are apparently under the control of different RpoH-dependent transcriptional networks in R . sphaeroides . Thus , it is possible that the thioredoxin- and gluthatione-dependent reduction systems preferentially function on different oxidized substrates . Glutathione-dependent reduction systems are known to function on lipids or other types of protein oxidative damage that might be experienced by the cell following singlet oxygen damage [35] . We also found that the RpoHII-specific regulon includes the multi-subunit NADH:quinone oxidoreductase and genes encoding enzymes in heme and quinone biosynthesis ( Table 1 ) . Each of these functions are critical for the respiratory and photosynthetic electron transport chains of R . sphaeroides and are known or predicted to contain one or more oxidant-sensitive metal centers . Thus , placement of these genes in the RpoHII-specific regulon suggests that these membrane or bioenergetic functions are damaged by and need to be replaced in the presence of singlet oxygen . Overall , our data indicates that the RpoHII-specific regulon controls expression of functions in the repair of oxidized proteins and replacement or assembly of critical electron transport chain components . Furthermore , the different types of repair functions found in the RpoHII regulon predict that singlet oxygen can damage numerous cellular components . Our global gene expression data , results from analysis of gene fusions , as well as previously reported in vitro experiments [14] , [15] all indicate that RNA polymerase containing either RpoHI or RpoHII can recognize some promoters in common . This observation is not surprising considering that RpoHI and RpoHII have similar amino acid sequences in their respective promoter recognition regions and are each able to rescue growth of E . coli σ32 mutants [14]–[16] . Likewise , the sequence logos derived here revealed that the promoter sequences recognized by each of the R . sphaeroides RpoH homologs are similar to both each other and to that recognized by E . coli σ32 [37] . Our experiments provide definitive evidence that some promoters are transcribed either exclusively or predominantly by RpoHI or by RpoHII . We were also able to predict and confirm the importance of bases for activity with individual RpoH homologs ( particularly those in the −35 element ) . We have computational and experimental observations that can explain some aspects of promoter selectivity by RpoHI and RpoHII . For example , our experiments identify T-9 and other positions in the −10 element as potential candidates in this discrimination , as one or more substitutions have larger effects on activity with individual RpoHI homologs . Mutation of T-9 to any other base reduced RpoHI-driven expression of GroE promoter by more than 90% , and this same effect was observed using an authentic RpoHI promoter from R . sphaeroides . Importantly , changing the −9 position of an RpoHII R . sphaeroides promoter to T permitted expression by RpoHI . Together , these data suggest that T-9 is either required for or significantly enhances expression of RpoHI promoters , but is likely to be less important for expression of RpoHII promoters , as there is only weak conservation of -9T in RpoHII promoters . Our data also predict that other bases , which are overrepresented in the RpoHII promoters , could be critical for expression by that σ factor . As is the case with E . coli σ32 there are likely to be specificity determinants that lie outside the canonical −35 and −10 elements [7] , [37] . Thus , additional in vivo and in vitro experiments with a larger suite of mutant promoters and a library of mutant RpoH proteins are needed to better define the determinants of promoter selectivity by RpoHI and RpoHII . In conclusion , our results suggest that , at least in R . sphaeroides , RpoHI controls functions that are necessary for maintenance of protein homeostasis and membrane integrity after temperature increase and other cytoplasmic stress , similar to the well-characterized role of E . coli σ32 in the heat shock response [3] . However , we propose that , in R . sphaeroides , some RpoHI-regulated functions are also useful for survival in the presence of other forms of stress because these target genes also contain promoters that are recognized by RpoHII . We propose that the duplication of an ancestral RpoH protein to create a second homolog of this alterative σ factor provided R . sphaeroides the opportunity to connect stress response functions to another stimulus . In this model , rpoHII was placed under the control of the master regulator of the singlet oxygen stress response and the two RpoH proteins evolved to recognize somewhat different but compatible promoter elements to assure the optimal regulation of distinct but overlapping stress regulons . As a result of these events , the transcriptional responses of R . sphaeroides to heat shock and singlet oxygen stress were separable but allowed to converge and contain a common set of functions . It will be interesting to identify and examine other examples of such convergence across bacteria and other organisms that possess multiple homologs of RpoH or other transcription factors .
E . coli strains were grown in Luria-Bertani medium [38] at 30°C or 37°C . R . sphaeroides strains were grown at 30°C in Sistrom's succinate-based medium [39] . E . coli DH5α was used as a plasmid host , and E . coli S17-1 was used as a donor for plasmid conjugation into R . sphaeroides . The media were supplemented with kanamycin ( 25 µg/ml ) , ampicillin ( 100 mg/ml ) , chloramphenicol ( 30 mg/ml ) , spectinomycin ( 50 mg/ml ) , tetracycline ( 10 mg/ml for E . coli and 1 mg/ml for R . sphaeroides ) , trimethoprim ( 30 µg/ml ) , or 0 . 1% of L- ( + ) -arabinose when required . Unless noted , all reagents were used according to the manufacturer's specifications . The list of bacterial strains and plasmids used in this study are summarized in Table S3 . Plasmids for ectopically expressing RpoHI or RpoHII were constructed by separately cloning the rpoHI or rpoHII genes downstream of the IPTG-inducible promoter in pIND4 [22] . DNA fragments containing rpoHI or rpoHII were amplified from R . sphaeroides 2 . 4 . 1 genomic DNA using oligonucleotides containing BsrDI and BglII restriction sites ( for RpoHII , RSP_0601_BsrDI_F GTAGCAATGCATGGCACTGGACGGATATACCGATC , RSP_0601_BglII_R GTAAGATCTTCATAGGAGGAAGTGATGCACCTCC , and for RpoHI , RSP_2410_BsrDI_F GTAGCAATGCATGAGCACTTACACCAGCCTTC , and RSP_2410_BglII_R GTAAGATCTTCAGGCGGGGATCGTCATGCC ) . These resulting fragments were digested with BsrDI and BglII and ligated into pIND4 that was digested with BseRI and BglII to create pYSD40 ( rpoHI ) and pYSD41 ( rpoHII ) , respectively . The pYSD42 plasmid expressing the FLAG-tagged version of RpoHI was constructed following the same procedure but with an oligonucleotide primer containing a sequence encoding for three consecutive copies of the FLAG epitope ( DYKDDDDK ) at the N-terminus ( RSP_2410_3FLAG_BsrDI GTAGCAATGCATGGACTACAAGGACCACGACGGCGACTACAAGGACCACGACATCGACTACAAGGACGACGACGACAAGAGCACTTACACCAGCCTTCCCGCTC ) . pYSD40 , pYSD41 , and pYSD42 were conjugated into R . sphaeroides ΔrpoHI [16] and R . sphaeroides ΔrpoHII respectively . To monitor levels of RpoHI and RpoHII after heat shock , exponential phase aerobic cultures ( 69% nitrogen , 30% oxygen and 1% carbon dioxide ) of wild type R . sphaeroides strain 2 . 4 . 1 grown at 30°C , were transferred to a 42°C warm bath with samples collected before heat treatment and at 10 min time intervals after heat shock , up to 60 min . To assess induction resulting from singlet oxygen stress , similarly grown wild type cells were treated with 1 µM methylene blue and exposed to 10 W/m2 incandescent light with samples collected before treatment and at 10 min time intervals after treatment , up to 60 min . Exponentially growing aerobic cultures of R . sphaeroides ΔrpoHI and ΔrpoHII mutants carrying the pYSD40 or pYSD42 plasmids respectively , were treated with 100 µM IPTG for one generation and harvested . All cell samples were resuspended in 3 M urea containing 1× protease inhibitor cocktail ( Thermo Scientific , Rockford , IL ) and sonicated . Samples were centrifuged to remove debris and total protein concentration of the samples determined with a protein assay kit following the manufacturer protocol ( Bio-Rad , Hercules , CA ) . An equal amount of total protein for each sample was loaded onto a NuPAGE acrylamide gel ( Invitrogen , Carlsbad , CA ) and run in 1× 4-morpholineethanesulfonic acid running buffer at 150 V for ∼90 min . Proteins were transferred to Invitrolon PVDF membranes ( Invitrogen , Carlsbad , CA ) , which were subsequently incubated for 1 hr in 1× Tris-buffered saline , 0 . 1% Triton-X , and 5% milk protein . The membranes were incubated with rabbit polyclonal antibodies raised against either RpoHI , RpoHII or PrrA . Horseradish-peroxidase-conjugated goat anti-Rabbit IgG antibody ( Thermo Scientific , Rockford , IL ) was used as secondary antibody for detection with Super Signal West Dura extended duration substrate ( Thermo Scientific , Rockford , IL ) . Triplicate 500 ml cultures were grown aerobically with bubbling ( 30%O2 , 69% N2 , 1% CO2 ) until they reached early exponential phase ( OD at 600 nm of 0 . 15 ) . At this point IPTG ( Isopropyl β-D-1-thiogalactopyranoside ) was added to a final concentration of 100 µM to induce gene expression from the pIND4 derivatives . After 3 hours incubation ( OD at 600 nm of 0 . 30 ) , 44 ml of cell culture were collected and 6 ml of 5% v/v phenol in ethanol was immediately added . Cells were collected by centrifugation at 6 , 000 g and frozen at −80°C until sample preparation . RNA extraction , cDNA synthesis , labeling , and hybridization were performed as previously described on Genechip Custom Express microarrays ( Affymetrix , Santa Clara , CA ) [40] . Processing , normalization , and statistical analysis of the expression profile data were performed in the R statistical software environment ( http://www . r-project . org/ ) [41] . Data were normalized using the affyPLM package with default settings [42]–[44] . The expression microarray data have been deposited in the NCBI's Gene Expression Omnibus [45] and are accessible through GEO Series accession number GSE39806 ( http://www . ncbi . nlm . nih . gov/projects/geo/query/acc . cgi ? acc=GSE39806 ) . Cells were harvested at mid-exponential growth ( OD at 600 nm of 0 . 30 ) from the same cell cultures used for the expression microarray experiment to prepare samples for a ChIP-chip assay [19] . RpoHI-FLAG was immunoprecipitated using commercial monoclonal antibodies against the FLAG polypeptide ( DYKDDDDK ) ( Sigma Aldrich , St Louis MO ) . RpoHII was immunoprecipitated with anti-R . sphaeroides RpoHII rabbit serum . Labeled DNA was hybridized on a custom-made tiling microarray , synthesized by NimbleGen ( Roche NimbleGen Inc , Madison , WI ) , covering the genome of R . sphaeroides 2 . 4 . 1 [19] . Before data analysis , dye intensity bias and array-to-array absolute intensity variations were corrected using quantile normalization across replicates ( limma package in the R environment ) [46] . Regions of the genome enriched for occupancy by RpoHI or RpoHII were identified using CMARRT with a false-discovery rate ≤0 . 05 [47] . The ChIP-chip data have been deposited in the NCBI's Gene Expression Omnibus [45] and are accessible through GEO Series accession number GSE39806 ( http://www . ncbi . nlm . nih . gov/projects/geo/query/acc . cgi ? acc=GSE39806 ) . DNA sequences were manipulated using custom Python scripts . Operon structure predictions for R . sphaeroides 2 . 4 . 1 were obtained from VIMSS MicrobesOnline ( http://www . microbesonline . org/operons/ ) [26] . The promoter sequences predicted to be recognized by RpoHI and RpoHII were discovered using Bioprospector [48] set to search for bi-partite conserved sequence motifs . The promoter sequence alignments were refined using HMMER 1 . 8 . 5 [49] . The logo representations of the promoter sequence alignment were generated using WebLogo ( http://weblogo . berkeley . edu/ ) [50] , [51] . To assay the in vivo activity of RpoHI and RpoHII at target promoters , β-galactosidase assays were conducted with R . sphaeroides ΔrpoHI ΔrpoHII mutant strains containing individual reporter gene fusions . To construct this set of reporter strains ∼350 base pair regions upstream of putative target genes: RSP_1173 , RSP_1408 , RSP_1531 , RSP_2314 , RSP_2389 , RSP_3274 , RSP_1207 and RSP_2617 , were amplified from genomic DNA using sequence specific primers , with NcoI and XbaI restriction sites at the ends of the upstream and downstream primers respectively . The amplified DNA fragments were purified , digested with NcoI-XbaI and then cloned in a pSUP202 suicide vector containing a promoterless lacZ gene ( pYSD51 ) . These Tcr plasmids were then conjugated into an R . sphaeroides ΔrpoHI ΔrpoHII mutant [15] , generating single copy promoter-lacZ fusions integrated in the genome . pYSD40 , pYSD41 or pIND4 ( empty vector ) were then conjugated into each of these reporter strains . Exponential phase cultures of these strains , grown by shaking 10 mL in 125 mL conical flasks , were then treated with 100 µM IPTG for one generation and samples analyzed for β-galactosidase activity . β-galactosidase assays were performed as previously described [52] . The data , presented in Miller units , represents the average of three independent replicates . To test bases that contribute to RpoHI and RpoHII promoter specificity , β-galactosidase assays were conducted in R . sphaeroides tester strains containing reporter gene fusions of the cycA ( RSP_0296 ) P1 promoter with a variety of point mutations ( see Results ) . These reporter strains were constructed as described above , with individual point mutations being generated by overlap extension PCR [53] . β-galactosidase assays were conducted as described above and the data represents the average of three independent replicates . Background LacZ activity from control strains for each promoter fusion containing only the empty pIND4 plasmid ( i . e . not expressing either RpoHI or RpoHII ) was subtracted from the measured LacZ activity for each mutant promoter . The construction of the E . coli CAG57102 mutant strain , the promoter library , and the β-galactosidase assay used to test the activity of R . sphaeroides RpoHI in vivo on mutant promoters were described previously [7] . To express R . sphaeroides RpoHI the E . coli rpoH gene of pSAKT32 [7] was replaced with the R . sphaeroides rpoHI gene . At least triplicate assays for β-galactosidase activity were performed on all strains . | An important property of living systems is their ability to survive under conditions of stress such as increased temperature or the presence of reactive oxygen species . Central to the function of these stress responses are transcription factors that activate specific sets of genes needed for this response . Despite the central role of stress responses across all forms of life , the processes driving their organization and evolution across organisms are poorly understood . This paper uses genomic , computational , and mutational analyses to dissect stress responses controlled by two proteins that are each members of the RpoH family of alternative σ factors . RpoH family members usually control gene expression during a heat shock response . However , the photosynthetic bacterium Rhodobacter sphaeroides and several other α-proteobacteria possess two or more paralogs of RpoH , suggesting some functional distinction . Our findings predict that a gene duplication event followed by changes in DNA recognition by RpoHI and RpoHII allowed convergence of the transcriptional responses to heat and singlet oxygen stress in R . sphaeroides and possibly other bacteria . Our approach and findings should interest those studying the evolution of transcription factors or the signal transduction pathways that control stress responses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"bacteriology",
"genome",
"expression",
"analysis",
"microbial",
"metabolism",
"functional",
"genomics",
"genome",
"evolution",
"gene",
"regulation",
"microbiology",
"dna",
"transcription",
"gene",
"function",
"microbial",
"evolution",
"molecular",
"genetics",
"microbial",
"physiology",
"gene",
"expression",
"biology",
"systems",
"biology",
"bacterial",
"physiology",
"bacterial",
"evolution",
"genetics",
"genomics",
"gene",
"networks",
"genetics",
"and",
"genomics"
] | 2012 | Convergence of the Transcriptional Responses to Heat Shock and Singlet Oxygen Stresses |
The Esx-1 ( type VII ) secretion system is critical for virulence of both Mycobacterium tuberculosis and Mycobacterium marinum , and is highly conserved between the two species . Despite its importance , there has been no direct visualization of Esx-1 secretion until now . In M . marinum , we show that secretion of Mh3864 , a novel Esx-1 substrate that remains partially cell wall–associated after translocation , occurred in polar regions , indicating that Esx-1 secretion takes place in these regions . Analysis of Esx-1 secretion in infected host cells suggested that Esx-1 activity is similarly localized in vivo . A core component of the Esx-1 apparatus , Mh3870 , also localized to bacterial poles , showing a preference for new poles with active cell wall peptidoglycan ( PGN ) synthesis . This work demonstrates that the Esx-1 secretion machine localizes to , and is active at , the bacterial poles . Thus , virulence-related protein secretion is localized in mycobacteria , suggesting new potential therapeutic targets , which are urgently needed .
Mycobacteria , and in particular M . tuberculosis , represent a major human health problem globally [1] . The Esx-1 secretion system [early secreted antigen 6 kilodaltons ( Esat-6 ) secretion system 1] , which is primarily encoded by genes within , and adjacent to , the region of difference 1 ( RD1 ) , is a major virulence determinant of both M . tuberculosis and M . marinum , apparently regulating bacterial spread to host cells [2]–[7] . In M . tuberulosis the RD1 locus ( rv3871-rv3879c ) encodes the canonical Esx-1 substrates Cfp-10 and Esat-6 , as well as Rv3871 and Rv3877 , two of the three core proteins in the secretory apparatus [3] , [8] . The third core constituent , Rv3870 , is encoded just upstream of RD1 [3] , [8] , but the Rv3870 protein is not functional in the absence of this locus . Importantly , the RD1 locus is highly conserved between M . tuberculosis and M . marinum [4] , [5] , [9] , and all Esx-1 deficient mutants analyzed in M . marinum thus far have been functionally complemented by their M . tuberculosis homologues , demonstrating that the genetic conservation extends to function [5] , [10] , [11] . Thus , M . marinum constitutes a highly relevant system in which to study functional aspects of the Esx-1 secretion system , likely to extend to M . tuberculosis . It is becoming increasingly clear that pathogenic bacteria are able to specifically localize virulence-related secretory systems and protein secretion to distinct compartments within their cell envelopes , and it is generally believed that such localization may be important for virulence [12]–[18] . However , protein secretion has never been visualized in mycobacteria , and it is therefore not known whether secretion in these bacteria is compartmentalized; in particular , there has been no visualization of Esx-1 , likely because of technical difficulties arising from the complex and hydrophobic nature of the mycobacterial cell wall . Moreover , analysis of this problem has not been possible because none of the described Esx-1 substrates are known to remain associated with the bacterial surface upon translocation , essentially precluding their use as tools to visualize sites of active Esx-1 secretion . We therefore sought to identify a novel Esx-1 substrate with properties allowing such analysis , and report here that Mh3864 ( Marinum homologue of Rv3864; MMAR_5439 ) is such a protein . Analysis of Mh3864 demonstrated that active Esx-1 secretion occurs in polar regions . Furthermore , using Mh3870 ( Marinum homologue of Rv3870; MMAR_5445 ) as a marker for Esx-1 , we show that the secretory apparatus also localizes to the poles . Interestingly , however , the steady-state distribution of Mh3864 in the M . marinum cell wall is not strictly polarized , and we propose a mechanism that may account for this feature .
In a transposon mutagenesis screen we identified an Mh3864-insertion mutant by virtue of its smooth colony morphology , which is a common feature of mutants affected in the Esx-1 secretion system ( Figure S1A ) . The Mh3864::tn mutant was deficient in CFP-10 secretion , and exhibited modestly reduced growth in macrophages compared to wild type M . marinum ( Figure S1B and S1C ) , suggesting roles for Mh3864 in Esx-1 secretion and virulence . To analyze the subcellular localization of Mh3864 we fractionated M . marinum cultures into secreted fraction ( culture filtrate; CF ) , cell envelope fraction ( Env ) and cytosolic fraction ( Cyt ) for Western blot analysis using a rabbit antiserum raised against an 89-residue peptide derived from the C-terminal region of Mh3864 . This antiserum specifically recognized a ∼40 kDa protein species , corresponding to the expected size of Mh3864 , in all three fractions from wild type bacteria ( Figure 1A , left panel ) . No reactivity was observed in Mh3864::tn fractions , demonstrating specificity of the antiserum . In bacteria lacking the entire RD1-region ( ΔRD1 ) , Mh3864 was produced but not secreted into the CF , indicating that Mh3864 is a secreted protein , dependent on Esx-1 for its export . Moreover , the specific genetic requirements for Mh3864 secretion were very similar to those previously shown for Cfp-10 and Esat-6 , because mutants of Mh3866 , Mh3867 , Mh3868 , Mh3871 and Mh3881c also failed to secrete Mh3864 , whereas transposon insertions in genes encoding Mh3876 , Mh3878 or Mh3879c did not have this effect ( Figure 1A , left panel ) [3] , [5] , [10] , [11] . Thus , Mh3864 is an Esx-1 substrate that has significant association with the cell envelope . In mutants that failed to secrete Mh3864 , the protein was either completely absent or its cellular concentration reduced , which might be explained by the common finding that stability of Esx-1 constituents and substrates appears to require an intact secretion system [5] , [19] . A band of lower molecular weight appeared in the Mh3876::tn strain , which may represent a proteolytic fragment of Mh3864 . As controls we analyzed FAP , which is secreted into the CF via the general secretory pathway [20] , and GroEL , which is not secreted into the CF ( Figure 1A , middle and right panels ) . These controls indicated that none of the strains were generally deficient in protein secretion , and that there was no nonspecific leakage of cytosolic or envelope material into the CF . Moreover , the Mh3864-encoding gene was transcribed in all strains except Mh3864::tn , suggesting that the influence of Esx-1 on Mh3864 secretion/stability was exerted at the protein level ( Figure 1B ) . FACS analysis demonstrated that Mh3864 was surface exposed on wild type , but not on ΔRD1 bacteria ( Figure 1C ) . Complementation with the M . tuberculosis derived RD1-2F9-cosmid restored surface exposure [2] , [21] . Because Mh3864 was produced but not secreted in ΔRD1 bacteria ( Figure 1A and 1B ) , this further indicated that Mh3864 secretion requires Esx-1 and also highlights the functional conservation of this secretory pathway between M . tuberculosis and M . marinum . As a fraction of Mh3864 remains surface associated upon secretion , we hypothesized that immunofluorescence ( IF ) microscopy analysis of newly secreted Mh3864 might allow us to gain insight into the localization of active Esx-1 secretion . To this end we treated bacteria with trypsin , which removed Mh3864 without killing the bacteria , and reinoculated treated cells into broth to allow for protein synthesis . Subsequently , we fluorescently labeled the bacterial cell wall with Ester-350 ( Alexa Fluor-350 carboxylic acid , succinimidyl ester ) and probed for new Mh3864 using our antiserum ( Figure 1D and 1E ) . No Mh3864 staining was observed on trypsinized bacteria ( Figure S2 ) , demonstrating that surface exposed Mh3864 was efficiently removed . Interestingly , after allowing for new protein synthesis in trypsinized cells , Mh3864 appeared primarily at the poles , including both old poles and new poles formed at the division septum ( Figure 1D ) . Analysis of many cells demonstrated that 90% of the stained bacteria had fluorescent foci in polar regions and 23% had foci in non-polar regions ( Figure 1E; this adds to >100% because some bacteria had polar and non-polar foci ) , indicating that Esx-1 secretion occurs primarily at the poles . The finding that newly secreted Mh3864 localized to the poles suggested that the Esx-1 apparatus might have a polar distribution in the cell envelope . To analyze the spatial distribution of Esx-1 directly , we generated an antiserum against Mh3870 , a membrane-associated component of the Esx-1 secretion apparatus . This antiserum , raised against a 135-residue peptide corresponding to amino acids 334–468 , specifically recognized Mh3870 as a ∼75 kDa species in the cell envelope fraction of wild type bacteria ( Figure 2A ) . Mh3870 reactivity was absent in ΔRD1 , presumably due to destabilization of the Mh3870 protein in the absence of an intact secretory apparatus [5] , [19] , and reappeared upon complementation with RD1-2F9 . In the cytosolic fraction , the antiserum reacted with two protein species of unknown origin . This nonspecific reactivity was unrelated to RD1 ( as it appeared in ΔRD1 ) , and does not affect analysis of the spatial distribution of envelope-associated Mh3870 , as these bands were absent from the envelope fractions . Anti-Mh3870 could not be used to localize Esx-1 on wild type M . marinum , because the serum did not react with intact wild type cells ( Figure 2B , left panel ) . Moreover , even affinity purified anti-Mh3870 antibodies were incompatible with methods to label thin-sections for electron microscopy analysis ( not shown ) . However , Mh3870 was accessible to antibodies in a KasB-deficient mutant strain ( Figure 2B , left panel ) , which has a more permeable cell wall [22] . The increased accessibility of Mh3870 in this strain was specifically due to loss of KasB , as trans-complementation with kasB ( pKasB ) eliminated the Mh3870 staining . Previous analysis of the kasB mutation has shown that it specifically causes a 2 to 4 carbon reduction in the length of cell wall mycolic acids , which normally are ∼80 carbons long , and a slight change in the mycolate composition . These seemingly small changes cause a drastic increase of cell wall permeability , most likely due to effects in the outer lipid coat of the mycobacterial cell wall where mycolic acids are believed to reside [22] . In silico analysis of both Mh3870 and its M . tuberculosis homologue Rv3870 , which are 90% identical in primary structure , has suggested that these proteins are integral membrane proteins containing AAA-ATPase domains between residues 456 to 665 ( see Materials and Methods ) . The finding that Mh3870 was accessible to antibodies on intact cells ( Figure 2B , left panel ) represents the first experimental data on the topology of this protein and strongly suggests an extracytoplasmic location for at least some epitopes within residues 334–468 . However , our data do not exclude an intracytoplasmic location of the predicted AAA-ATPase domain . The subcellular localization of Mh3870 was unaffected by absence of KasB ( Figure 2A ) , and the amount of surface exposed Mh3864 was similar in wild type and KasB-negative bacteria ( Figure 2B , right panel ) , indicating that Esx-1 secretion is unaffected by KasB deficiency . Furthermore , IF-microscopy analysis of newly secreted Mh3864 on KasB-negative bacteria ( Figure 2C , and Figures S2 and S3 ) demonstrated similar surface distribution to wild type ( Figure 1E ) , suggesting that kasB-inactivation had no effect on the spatial distribution of Esx-1 secretion . Thus , the KasB-mutant could be used to study the localization of Mh3870 , as a marker for the Esx-1 apparatus . Strikingly , IF-microscopy demonstrated that Mh3870 localized almost exclusively to the bacterial poles ( Figure 2D and 2E ) . Quantification of a large number of cells indicated that staining was specific ( Figure 2E , left panel ) . ∼96% of stained cells had Mh3870 at a pole , while only ∼12% had non-polar staining ( Figure 2E , right panel ) . Among the polarly stained bacteria , the vast majority ( 78 . 3% ) were stained in a unipolar fashion mainly at the new bacterial pole ( i . e . septum ) , indicating that Esx-1 localized primarily to this region ( Figure S4 ) . Thus , our analysis of Mh3870 and of a newly secreted Esx-1 substrate ( Mh3864 ) strongly suggested that the Esx-1 apparatus localizes to , and is active at , polar regions . To analyze sites of Esx-1 secretion in a milieu more representative of the mycobacterial in vivo situation , we visualized Mh3864 localization on wild type M . marinum in infected macrophages ( Figure 3 ) . While staining of intracellularly growing bacteria was rare , stained cells exhibited a unipolar localization of Mh3864 . No staining was observed with preimmune serum , demonstrating specificity ( not shown ) . Thus , in infected host cells , Esx-1 activity is concentrated to one of the bacterial poles , suggesting that polarized Esx-1 secretion is relevant in vivo . Because M . marinum has previously been shown to form actin tails at one of their poles after reaching the cytosol of infected host cells [23] , we were also interested in examining if Esx-1 activity localized to such poles ( Figure 3 ) . To this end we used fluorescently conjugated phalloidin , which binds polymerized actin and allows for visualization of actin tails . Indeed , Mh3864 localized to poles with actin tails , indicating that the Esx-1 machine is active at poles that are also competent to induce actin polymerization . Moreover , because the Esx-1 machine localized primarily to new poles ( Figure 2D and Figure S4; see also Figure 5A ) , this also implied that actin polymerization occurs preferentially at new bacterial poles . While Mh3864 itself is not required for actin tail formation ( not shown ) , further studies are warranted to elucidate a possible role for Esx-1 , which is required for M . marinum to reach the cytosol , in actin tail formation . IF-microscopy analysis of the steady-state distribution of Mh3864 on GFP-expressing bacteria showed a less polarized distribution than that of newly secreted Mh3864 ( Figure 4A and 4B ) . As expected , wild type bacteria showed specific immunofluorescence , and Mh3864::tn did not ( Figure 4B , left panel ) . Of the stained bacteria , 81 . 9% had fluorescent foci in polar regions and 53 . 1% had foci in non-polar regions ( Figure 4B , right panel ) . Similar analysis on non-GFP-expressing wild type and KasB-negative bacteria whose walls had been fluorescently labeled with Ester-594 confirmed this finding and also indicated that the steady-state distribution of Mh3864 was unaffected by KasB-deficiency ( Figure 4C and Figure S5 ) . Compared to data in figure 4B , there was a ∼10% distribution-shift towards polar regions in the current analysis , which likely can be explained by improved visualization of septal regions/new poles by Ester-594 . Taken together , steady-state analysis of Mh3864 ( Figure 4C ) indicated a largely polarized surface distribution , but with a ∼2-fold increase of staining in non-polar regions as compared to newly secreted Mh3864 ( Figure 1E and Figure 2C ) . Moreover , the steady-state distributions of Mh3870 and Mh3864 were partially distinct; Mh3870 localized to non-polar regions in merely 12% of stained cells ( Figure 2E , right panel ) , whereas Mh3864 did so in ∼44% of cells ( Figure 4C ) . These findings implied that , during bacterial growth , at least some Mh3864 protein moved to non-polar regions after translocation to the cell wall at the poles . As Mh3864 was present in distinct aggregates on the bacterial surface , and because immunofluorescence may exaggerate the signal from molecular aggregates , we analyzed the steady-state distribution of surface Mh3864 by immuno transmission electron microscopy ( Figure 4D ) . This analysis confirmed the focal appearance of Mh3864 on the bacterial surface , and allowed us to analyze the distribution in more detail; ∼73% of all gold aggregates localized to polar regions while ∼27% were non-polarly distributed ( Figure 4E ) . In order to study Esx-1 localization in more detail , and to gain insights into the cell wall properties at these sites , we analyzed co-localization between Mh3870 and fluorescently labeled vancomycin ( Vanc-FL ) on Ester-350 labeled bacteria ( Figure 5A ) . Vancomycin binds to the pentapeptide precursor ( Lipid II ) during the production of cell wall PGN , and it is well established that Vanc-FL can be used to probe sites of PGN-insertion into the preexisting cell wall [24]–[27] . For wild type M . marinum the MIC value of vancomycin was exceedingly high ( ≥80 µg/ml ) , and we were unable to obtain Vanc-FL staining in these bacteria ( not shown ) . However , KasB-negative bacteria were inhibited at a much lower concentration of vancomycin ( ≤1 µg/ml ) , and also stained efficiently with Vanc-FL ( Figure 5A ) . This analysis demonstrated that M . marinum inserts new cell wall PGN at both new poles/septa and old poles [27] , indicating that both poles may represent dynamic and active regions . However , new poles often stained more intensely with Vanc-FL than old poles , suggesting that new poles might represent more active sites of cell wall growth . Colocalization of Mh3870 with Vanc-FL at septa ( Figure 5A ) confirmed that Mh3870 localized mainly to new poles , indicating that Esx-1 localizes primarily to a region of active cell wall turnover . Because our IF-microscopy analysis suggested that some Mh3864 protein might move from its polar site of secretion during bacterial growth , we hypothesized that Mh3864 might move with the cell wall towards non-polar regions , as it was being pushed from the poles by continuous insertion of new PGN . To test if the wall migrates during bacterial growth , we stained bacteria with Ester-594 , allowed them to grow for 3 generations , incubated the cultures with Vanc-FL , and finally stained the bacteria with Ester-350 ( Figure 5B ) . This allowed us to visualize “old wall” ( Ester-594 ) , sites of PGN-insertion ( Vanc-FL ) , and the “current wall” ( Ester-350 ) . This analysis demonstrated that old wall was absent from polar regions of PGN-insertion , whereas staining of the current wall covered the entire bacterial surface ( Figure 5B ) , indicating that the old wall had indeed migrated towards non-polar regions during bacterial growth .
Our data demonstrate that the Esx-1 secretion apparatus localizes to bacterial poles , primarily to new poles with active cell wall synthesis . These findings were made possible by the use of a mutant strain with a more permeable outer lipid coat ( KasB-neg . ) , which allowed penetration by antibodies and fluorescent probes . Importantly , analysis of a novel Esx-1 substrate that remains partially cell wall-associated ( Mh3864 ) showed that active Esx-1 secretion occurs primarily at bacterial poles . Interestingly , Mh3864 also localized to bacterial poles in infected macrophages , suggesting that polarized Esx-1 secretion is relevant in the context of an infected host . Thus , the Esx-1 apparatus localizes to , and is active at , the bacterial poles . The role of Mh3864 homologues in mycobacterial virulence remains unclear . Analysis in M . tuberculosis indicates that Rv3864-deficient bacteria are attenuated in vivo [28] , whereas its homologue in Mycobacterium leprae ( ML0058c ) is a pseudogene and studies in Mycobacterium microti speak against a required role for Rv3864 in virulence [29] . Moreover , a study in M . tuberculosis has shown that Rv3616c ( EspA ) , a homologue of Rv3864 , is an Esx-1 substrate required for virulence [19] . Thus , although there has been no systematic comparison of the functions of Rv3616c and Rv3864 or their orthologues in any mycobacterial species , the apparently conflicting data regarding the role of Rv3864 in virulence might possibly be explained by redundancy , in at least some mycobacterial species . Our work identifies Mh3864 as the first bona fide Esx-1 substrate that remains partially cell surface-associated , and accessible to antibodies on intact wild type cells , and also suggests a role for Mh3864 in M . marinum virulence . It is therefore intriguing to speculate that its functional homologue in M . tuberculosis might represent a potential vaccine candidate . Specialized secretion systems , such as Esx-1 , are common among pathogenic bacteria; for example , type III secretion is critical for virulence of Salmonella , Shigella and Yersinia [30] , and type IV secretion is similarly required for Helicobacter , Legionella and Agrobacterium [31] . Interestingly , the type III and type IV secretion machines may be specifically active at bacterial poles [15] , [18] , implying that polar localization of virulence related protein secretion is a common feature in pathogens . However , it remains unknown if polar localization of these well-studied secretory systems is required for virulence , possibly because the molecular mechanisms of localization are intimately connected to proficient secretion . Concerning mycobacteria , identification of the genetic requirements and the mechanisms governing Esx-1 localization will open the path to address this important question . These studies also allowed us to propose a link between cell wall growth and Esx-1 localization . According to this model Mh3864 is secreted via Esx-1 in polar regions with active PGN biosynthesis . As new PGN is inserted at the poles it may push the existing cell wall PGN layer , including associated Mh3864 , towards non-polar regions , explaining why some Mh3864 localizes to non-polar regions during steady-state growth . However , Mh3870 remains polarized at steady-state , emphasizing the distinct behavior of the Esx-1 secretion machine . Interestingly , this model , which takes into account both the site of secretion and the dynamics of cell wall growth , is in principle similar to findings in Streptococcus pyogenes and Listeria monocytogenes [16] , [25] , [32] , [33] . Thus , with regard to the steady-state distribution of wall-associated surface proteins it appears that a functional relationship between site of secretion and the dynamics of cell wall growth might be of general importance in Gram-positive bacteria , including mycobacteria . Identification of the mechanisms governing Esx-1 localization will be of great interest since they may be required for mycobacterial virulence , and amenable to therapeutic intervention .
Wild type M . marinum M-strain and an isogenic deletion mutant lacking RD1 ( ΔRD1 ) has been described previously [34] , as well as insertional transposon mutants of Mh3866 , Mh3867 , Mh3868 , Mh3871 , Mh3876 , Mh3878 , Mh3879c and Mh3881c [5] , [10] . An insertional transposon mutant of KasB , and its trans-complement ( pKasB ) has been described [22] . ΔRD1 was complemented with RD1-2F9 by integration of this cosmid into the chromosomal attB-site [2] , [21] . A wild type M . marinum M-strain expressing gfp that has been integrated into the attB-site has been previously described [34] . M . marinum strains were grown in Middlebrook 7H9-broth ( Difco ) supplemented with 0 . 2% glycerol , 0 . 05% Tween 80 , and 10% albumin-dextrose-catalase enrichment , or on 7H10 agar ( Difco ) supplemented with 0 . 5% glycerol and 10% oleic acid-albumin-dextrose-catalase enrichment . Cultures were supplemented with antibiotics as appropriate . For fractionation of M . marinum cultures , they were grown in Sauton's defined medium ( Teknova ) . Wild type M . marinum expressing gfp from the chromosome was subjected to an M4 ( mariner transposon mutagenesis in M . marinum ) mutagenesis screen as previously described in detail [35] . The site of transposon insertion was determined as described [35] . Finally , PCR analysis and DNA sequencing demonstrated that the transposon was inserted in an inverted position immediately down-stream of nucleotide 252 in the Mh3864-encoding gene , corresponding to a truncation after amino acid 84 in Mh3864 . Strains were grown to mid-log phase ( OD600 = 0 . 7+/−0 . 2 ) in 7H9 . The bacteria were washed extensively in Sauton's minimal medium , and inoculated into 20 ml ( final volume ) of this medium . Short-term cultures were collected ∼48 h after inoculation . Bacteria were pelleted by centrifugation , and the supernatant was filtered through a 0 . 2 µm filter ( culture filtrate ) . After a ∼65-fold concentration of the culture filtrates using Vivaspin 15R ( 2 . 000 MWCO; Sartorius Biolab ) , the final volume was determined for later normalization . The bacterial pellet was weighed for normalization purposes , and subsequently resuspended in 2 ml fractionation buffer ( 100 mM HEPES , pH 7 . 5; 300 mM KCl; 10% glycerol; 10 mM MgCl2; 1 mM DTT; 0 . 01% Tween 80 ) supplemented with complete , EDTA-free , protease inhibitor coctail ( Roche ) . Bacterial cell lysates were prepared by bead beating at 4°C , and centrifugated at 3000×g for 10 min to pellet glass beads and remaining intact bacteria . The cell envelope fraction , containing both the cell membrane and the cell wall , was collected by subjecting the supernatant to ultracentrifugation ( 100 . 000×g ) for 1 h , and resuspended in 0 . 4 ml fractionation buffer . The supernatant from the ultracentrifugation was collected as cytosolic fraction , and its volume determined . For Western blot analysis of fractions , loading was normalized to the weights of the original bacterial pellets , and samples were separated by SDS-PAGE , using 4–20% gradient gels ( Bio-Rad ) . Membranes were developed with West Pico ( Pierce ) . RNA was purified from mid-log phase M . marinum cultures ( OD600 = 0 . 7+/−0 . 2 ) using RNeasy Mini Kit ( Qiagen ) , essentially as described by the manufacturers . However , bacterial lysates were first prepared by bead beating as described above , and we included an additional step of DNase I ( New England Biolabs ) treatment to ensure degradation of chromosomal DNA . C-DNA for mh3864 and groEL were generated in the same tube by RT-PCR on 1 µg RNA using reverse primers mh3864_3prR ( 5′-ttcgtcgtcttccttcttgtcgct-3′ ) and groEL_3prR ( 5′-tctcggtggtcagcaccatacgtg-3′ ) , respectively . In control tubes , RT-polymerase was omitted; no PCR products ( see below ) were obtained when these controls were used as template , demonstrating absence of contaminating chromosomal DNA ( not shown ) . Generated c-DNA was used as template for PCR-analysis of mh3864 and groEL using primer pairs mh3864_5prF ( 5′-gctcttcaaaggaatcgccgacaa-3′ ) and mh3864_3prR , and groEL_5prF ( 5′-tgagcaagctgattgagtacgacg-3′ ) and groEL_3prR , respectively . Equal amounts were loaded for gel analysis using 1% agarose gels . To generate a rabbit antiserum against Mh3864 , an 89-residue peptide corresponding to amino acids 330–418 was cloned into pGEX-KG as a translational GST-fusion . Escherichia coli BL21 Codon Plus ( Stratagene ) harboring the construct was grown at 37°C and expression of the fusion peptide was induced with 1 mM IPTG . After purification on a glutathione sepharose column ( GE Healthcare ) followed by an S300 column ( GE Healthcare ) , the GST-tag was separated off by overnight thrombin digestion that cleaved a thrombin cleavage site located between the tag and the Mh3864 peptide . The Mh3864 peptide was subsequently purified through a glutathione sepharose column followed by an S200 column ( GE Healthcare ) . A 135-residue peptide corresponding to amino acids 334–468 of Mh3870 was expressed in a similar manner . However , the Mh3870 fusion peptide was insoluble and extracted from inclusion bodies by 6 M Guanidine Hydrochloride , and refolded overnight in TRIS/HCl buffer containing 3 . 5 M urea . After refolding the urea concentration was reduced to 1 M by buffer exchange . The GST-tag was removed by thrombin digestion as described above , and the Mh3870 peptide was purified on a RP C4 column ( W . R . GRACE ) . Finally , the purified Mh3864 and Mh3870 peptides were confirmed by mass spectrometry analysis . Rabbits were immunized with 200 µg of the purified peptides using TiterMax ( TiterMax USA , Inc . ) as adjuvance , and subsequently similarly boosted with 100 µg antigen . The use of TiterMax was critical since it provided a good immune response , but does not contain any mycobacterial components . For both antigens the rabbits were also bleed prior to immunization in order to obtain relevant preimmune controls , and Western blot analysis of M . marinum fractions indicated lack of reactivity for both of these preimmune sera ( not shown ) . Carboxylic acid , succinimidyl esters conjugated to either Alexa fluor-350 or 594 ( Ester-350 or Ester-594 ) , and Bodipy-conjugated vancomycin ( Vanc-FL ) were from Molecular probes . For IF-microscopy and FACS-analysis , our generated rabbit antisera against Mh3864 and Mh3870 were used at 1:500 and 1:200 , respectively . For IF-microscopy , binding of rabbit IgG was detected with goat anti-rabbit IgG ( H+L ) antibodies conjugated with either Alexa fluor-488 or 594 ( Molecular Probes ) , diluted to 1:250 . For FACS-analysis rabbit IgG-binding was detected with an allophycocyanin ( APC ) -conjugated affinity purified F ( ab' ) 2 fragment donkey anti-rabbit IgG ( H+L ) ( Jackson ImmunoResearch Laboratories ) , at 1:100 . Gold ( 6 nm ) -conjugated F ( ab' ) 2 goat anti-rabbit IgG ( Electron Microscopy Sciences ) was used at 1:200 for electron microscopical detection of bound rabbit IgG . For Western blotting , monoclonal mouse anti-GroEL Abs ( Colorado state ) were used at 1:50 , and polyclonal rabbit anti-FAP serum ( Colorado state ) was used at 1:15000 . Rabbit antisera against Mh3864 and Mh3870 were used at 1:5000 and 1:2000 , respectively . Peoxidase-conjugated affinity purified F ( ab' ) 2 fragment donkey anti-rabbit IgG ( H+L ) ( Jackson ImmunoResearch Laboratories ) and peroxidase-conjugated affinity purified F ( ab' ) 2 fragment donkey anti-mouse IgG ( H+L ) ( Jackson ImmunoResearch Laboratories ) were used at 1:5000 for detection of rabbit and mouse IgG , respectively . Bacteria were grown to mid-log phase ( OD600 = 0 . 7+/−0 . 2 ) in 7H9-broth , and collected by centrifugation . Cells were washed in Tris-buffered saline supplemented with 0 . 05% Tween-20 ( TBST ) , and needled twice through a 26G1/2 needle ( Becton Dickinson ) to disrupt bacterial aggregates . Aggregates were pelleted by two separate centrifugation steps ( 2000 rpm , 1 min ) , where the supernatants , enriched for single cell bacteria , were transferred to new tubes . The bacterial concentration was determined using a hemacytometer , and suspensions were diluted to a final concentration of ∼108 bacteria/ml . If the bacteria were to be stained with fluorescently conjugated Carboxylic acid , succinimidyl ester , they were prepared in phosphate buffered saline ( PBS ) , and labeled as instructed by the manufacturer . After labeling , the bacteria were washed twice with TBST to inactivate unbound ester compounds . 1 ml of bacterial suspensions , prepared as described above , were pelleted and resuspended in 0 . 1 ml TBST containing indicated serum/antibodies . After incubation at room temperature ( RT ) for 1 h with agitation , the suspensions were washed twice with TBST . For IF-microscopy the bacteria were then similarly incubated with the appropriate fluorescently conjugated secondary antibody . Upon washing the bacteria were mounted with ProLong antifade ( Molecular Probes ) onto glass cover slips and analyzed with Axioplan 2 Zeis microscope using a 100× objective . For scoring staining as polar , the fluorescence had to be at , or immediately adjacent to , a pole . For visualization of Mh3864 and actin tails on M . marinum in infected macrophages , infected cells were washed once in PBS and fixed with 4% PFA for 20 min . Fixed cells were permeabilized with 0 . 1% Triton X ( Pierce ) for 4 min , and subsequently washed 3 times with PBS . Mh3864 was detected by incubation with anti-Mh3864 serum ( 1/100 in PBS supplemented with 1% BSA ) for 1 h . After washing , a fluorescently conjugated secondary antibody was added for visualization of IgG-binding , and Alexa Fluor-350 conjugated phalloidin ( Invitrogen ) was added for visualization of polymerized actin . After 1 h incubation , cells were washed 3 times in PBS , and mounted for IF-microscopy analysis . Bacteria were prepared as described for IF-microscopy , except that IgG-binding was detected with APC-conjugated secondary antibodies . Samples were run on a FACSCalibur ( BD Biosciences ) , and data was analyzed using FlowJo ( Tree Star Inc . ) . Bacterial cells were prepared as described for IF-microscopy , but we used a gold ( 6 nm ) conjugated secondary antibody for detection of bound IgG . Cells were fixed in 1/2 Karnovski's ( 2% paraformaldehyde and 2 . 5% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ) for 1 h , and then washed in 0 . 1 M sodium cacodylate buffer and post-fixed in 1% aqueous osmium textroxide for 1 h . Samples were subsequently dehydrated through a series of ethanol , followed by propylene oxide and embedded in Eponate 12 ( Ted Pella ) . Thin sections were cut on a Reichert Ultracut E , stained with 1% uranyl acetate and 0 . 1% lead citrate , and examined in a Philips CM12 electron microscope . Images were captured with a GATAN Retractable Multiscan digital camera . For quantitative analysis of localization of gold aggregates , the localization of each aggregate is represented as a ratio ( d/D ) ; the distance between pole 1 ( defined below ) and individual aggregates of gold particles ( d ) was divided by the longest distance between the two bacterial poles ( D ) . If gold particles were observed in only one polar region , that pole is pole 1 . If gold particles were observed in both polar regions , the pole with more gold particles is pole 1 . If gold particles were observed in only non-polar regions , the pole to the left in the picture is pole 1 . Each polar region includes 25% of the total bacterial length . For analysis of newly secreted Mh3864 , bacteria were trypsinized essentially as described previously [17] , [32] . In brief , mid-log cultures were washed twice with PBS , resuspended in PBS ( untreated control ) or PBS supplemented with 0 . 2 mg/ml trypsin ( Sigma ) , and incubated at 37°C for 1 h with agitation . As controls , untreated and trypsinized cells were washed twice with TBST supplemented with 0 . 02% azide ( TBSTA ) and immediately probed for Mh3864 as described above . For analysis of newly secreted Mh3864 , cells were washed twice with PBS , resuspended in 10 ml pre-warmed 7H9-medium , and grown at 32°C for ≤2 generations as measured by optical density . Finally these cells were fluorescently labeled with Ester-350 and similarly probed for Mh3864 . Bone marrow derived macrophages ( BMDM ) were obtained and cultured from 129/SVJ mice as previously described [23] . BMDMs were grown on glass cover slips and infected with GFP-expressing M . marinum at a MOI of 5 , essentially as described [23] . At 24 h post infection , samples were stained for Mh3864 and actin tails as described above . Finally , samples we mounted with ProLong antifade for microscopical analysis . The ExPASy Proteomics MotifScan-tool ( http://myhits . isb-sib . ch/cgi-bin/motif_scan ) was used to analyze the primary sequences of Mh3870 ( MMAR_5445 ) and Rv3870 , which were retrieved from MarinoList ( http://genolist . pasteur . fr/ MarinoList/ ) and TubercuList ( http://genolist . pasteur . fr/ TubercuList/ ) , respectively . | Mycobacteria represent a major human health problem globally , and there is a pressing need to identify novel processes and mechanisms including therapeutic targets . The Esx-1 secretion system is required for both Mycobacterium tuberculosis and Mycobacterium marinum to cause disease , and is absent from vaccine strains such as Mycobacterium bovis BCG . Esx-1 is functionally conserved between M . tuberculosis and the experimentally amenable M . marinum , which is increasingly used to study this secretory system . Bacterial cells are spatially highly organized; in particular , pathogenic bacteria may localize virulence-related protein secretion to specific regions within the cell envelope , a feature that is generally believed to be important for virulence . However , it has not been known whether Esx-1 is compartmentalized . Our work represents the first visualization of protein secretion in mycobacteria in general . Specifically , we show that the Esx-1 apparatus localizes to , and is active at , the bacterial poles in M . marinum . These findings suggest previously unappreciated mechanisms governing localization of protein secretion in mycobacteria , potentially including new therapeutic targets . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"cell",
"biology/cell",
"growth",
"and",
"division",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"cell",
"biology/microbial",
"growth",
"and",
"development",
"infectious",
"diseases/bacterial",
"infections",
"microbiology/microbial",
"physiology",
"and",
"metabolism",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/medical",
"microbiology",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] | 2009 | Polar Localization of Virulence-Related Esx-1 Secretion in Mycobacteria |
Brassinosteroids ( BRs ) are steroid hormones essential for plant growth and development . The BR signaling pathway has been studied in some detail , however , the functions of the BRASSINOSTEROID-SIGNALING KINASE ( BSK ) family proteins in the pathway have remained elusive . Through forward genetics , we identified five semi-dominant mutations in the BSK3 gene causing BSK3 loss-of-function and decreased BR responses . We therefore investigated the function of BSK3 , a receptor-like cytoplasmic kinase , in BR signaling and plant growth and development . We find that BSK3 is anchored to the plasma membrane via N-myristoylation , which is required for its function in BR signaling . The N-terminal kinase domain is crucial for BSK3 function , and the C-terminal three tandem TPR motifs contribute to BSK3/BSK3 homodimer and BSK3/BSK1 heterodimer formation . Interestingly , the effects of BSK3 on BR responses are dose-dependent , depending on its protein levels . Our genetic studies indicate that kinase dead BSK3K86R protein partially rescues the bsk3-1 mutant phenotypes . BSK3 directly interacts with the BSK family proteins ( BSK3 and BSK1 ) , BRI1 receptor kinase , BSU1 phosphatase , and BIN2 kinase . BIN2 phosphorylation of BSK3 enhances BSK3/BSK3 homodimer and BSK3/BSK1 heterodimer formation , BSK3/BRI1 interaction , and BSK3/BSU1 interaction . Furthermore , we find that BSK3 upregulates BSU1 transcript and protein levels to activate BR signaling . BSK3 is broadly expressed and plays an important role in BR-mediated root growth , shoot growth , and organ separation . Together , our findings suggest that BSK3 may function as a scaffold protein to regulate BR signaling . The results of our studies provide new insights into early BR signaling mechanisms .
Brassinosteroids ( BRs ) are steroid hormones that are essential for plant growth and development . BRs regulate a wide range of cellular , physiological , and developmental processes , including cell expansion , cell division , stem elongation , root development , leaf growth , organ boundary formation , vascular development , male fertility , and stomatal development [1 , 2] . BRs are mainly perceived by the plasma membrane-localized leucine-rich repeat ( LRR ) receptor kinase BRASSINOSTEROID INSENSITIVE 1 ( BRI1 ) [3 , 4] . Direct binding of BRs to the extracellular BR-binding domain composed of the island domain and LRR22 of BRI1 [5] causes the release of BRI1 KINASE INHIBITOR 1 ( BKI1 ) from BRI1 to the cytosol [6 , 7] and promotes the interaction between BRI1 and BRI1-ASSOCIATED RECEPTOR KINASE 1 ( BAK1 ) [8–10] . Structural studies suggest that BRs act as “molecular glues” to enhance BRI1 and BAK1 association [11 , 12] . BRI1 and BAK1 transphosphorylate each other to fully activate BRI1 and initiate a phosphorylation/dephosphorylation-mediated signaling cascade to regulate nuclear gene expression [13] . The activated BRI1 phosphorylates two plasma membrane-associated receptor-like cytoplasmic kinases BRASSINOSTEROID-SIGNALING KINASE 1 ( BSK1 ) and CONSTITUTIVE DIFFERENTIAL GROWTH 1 ( CDG1 ) to activate the phosphatase BRI1 SUPPRESSOR 1 ( BSU1 ) [14–17] . BSU1 subsequently inactivates the GLYCOGEN SYNTHASE KINASE 3 ( GSK3 ) -like kinase BRASSINOSTEROID INSENSITIVE 2 ( BIN2 ) by dephosphorylating BIN2 phosphotyrosine residue pTyr200 [16 , 18] . BIN2 and protein phosphatase-2A ( PP2A ) antagonistically regulate the phosphorylation status of BRASSINAZOLE-RESISTANT 1 ( BZR1 ) and BRI1-EMS-SUPPRESSOR 1 ( BES1 ) transcription factors [19–22] . Upon inhibition of BIN2 by BSU1 [16] and SCFKIB1-mediated BIN2 degradation [23] , dephosphorylated BZR1 and BES1 accumulate in the nucleus to regulate the expression of hundreds of genes [20 , 24–28] . These BR-responsive genes then control various cellular , physiological , and developmental processes . The BR signaling pathway has been studied in some detail , however , the functions of the BSK family proteins in the pathway have remained elusive . The founding members of this family are BSK1 and BSK2 , which were identified as BR-responsive proteins by two-dimensional difference gel electrophoresis and mass spectrometry [15] . The BSK protein family is composed of twelve members , which contain an N-terminal kinase domain and C-terminal tetratricopeptide repeat ( TPR ) motifs [15] . Although called BRASSINOSTEROID-SIGNALING KINASES , the available results indicate that not all members are involved in BR signaling . SHORT SUSPENSOR ( SSP/BSK12 ) activates the YODA mitogen-activated protein kinase pathway to regulate suspensor development during embryogenesis [29] , which has not been shown to be regulated by BRs . Biochemical and genetic studies have implicated numerous BSK members ( BSK1 , 2 , 3 , 4 , 5 , 6 , 8 , and 11 ) as positive regulators in BR signaling [15 , 30] . BRI1 phosphorylation of BSK1 at Ser230 promotes BSK1 interaction with BSU1 phosphatase [15 , 16] , however , the mechanism underlying BSK1-mediated BSU1 activation is not known [17] . bsk3/4/6/7/8 pentuple mutants exhibit multiple growth defects , including reduced rosette size , leaf curling , and enhanced leaf inclination [30] . However , these mutants are phenotypically very different from BR deficient and response mutants , which exhibit severe growth defects , including dark green and rounded leaves , reduced male fertility , and reduced silique growth [3 , 31–33] . Together , the functional importance of the BSK family members in BR signaling and BR-mediated plant growth and development remains unclear . To identify novel regulators in BR signaling and responses , we took a forward genetic approach to screen for mutations that cause decreased BR responses in Arabidopsis . Our genetic screen identified five semi-dominant mutations causing decreased BR responses . Through positional cloning , we determined that the mutations occurred in the BSK3 gene ( At4g00710 ) , which encodes a receptor-like cytoplasmic kinase . Our genetic , molecular , and biochemical studies demonstrate that BSK3 plays an important role in BR signaling and BR-mediated plant growth and development .
Previous genetic screens for mutations that cause decreased BR responses in Arabidopsis only successfully identified two BR signaling regulators , BRI1 and BIN2 [3 , 18 , 31 , 34 , 35] . The identified bri1 and bin2 mutants are dwarf or semi-dwarf plants that exhibit pleiotropic growth defects , including reduced rosette size , dark green and rounded leaves , and reduced male fertility . We reasoned that previous screens missed mutants that do not exhibit bri1/bin2-like shoot phenotypes . To identify mutants affecting novel loci involved in BR signaling , we carried out a root-based genetic screen for mutations that cause decreased BR responses in Arabidopsis . Wild-type Columbia ( Col ) seedlings exhibited reduced root growth when grown on 0 . 1 μM brassinolide ( BL , the most active BR ) ( S1A Fig ) . We screened ethyl methanesulfonate ( EMS ) -mutagenized ( in Col-0 ) and activation-tagged ( in Col-2 ) populations . Seedlings on 0 . 1 μM BL exhibiting longer roots were selected . We named these plants brassinosteroid resistant ( brr ) mutants . Here , we report five brr mutants ( brr1 , brr2 , and brr3 in Col-0; brr4 and brr5 in Col-2 ) ( Fig 1A ) . To determine the recessive or dominant nature of these mutations , we crossed all brr mutants into the wild-type Col-0 or Col-2 plants . Surprisingly , both heterozygous and homozygous mutants were resistant to 0 . 01 and 0 . 1 μM BL . In addition , homozygous mutants had stronger BL resistance ( Fig 1B and 1C and S1B–S1E Fig ) , indicating that all five brr mutations are semi-dominant . Although brr4 and brr5 were identified from an activation-tagged population , co-segregation analyses of BL and Basta resistance suggested that the mutations were not caused by T-DNA inserts . We used positional cloning to identify the affected gene of the brr1 mutant , which exhibited slightly reduced root growth in the absence of BL ( Fig 1F ) . This mutant is caused by a mutation in the BSK3 gene ( At4g00710 ) , which encodes a receptor-like cytoplasmic kinase [15] . The second glycine of the protein was mutated to arginine ( Fig 1D ) . To confirm that BSK3 is responsible for the mutant phenotypes , we expressed an HA-tagged BSK3 genomic DNA under the control of the native BSK3 promoter in the brr1 mutant . Two independent brr1 transgenic lines expressing BSK3-HA protein were selected ( Fig 1E ) . Light-grown brr1 BSK3-HA seedlings exhibited wild-type-like root length and hypersensitivity to 0 . 1 μM BL ( Fig 1F ) . These results demonstrate that BSK3 genomic DNA complements the brr1 mutant phenotypes , including the root growth defect and BL resistance , confirming that the BSK3G2R mutation confers the brr1 mutant phenotypes . Subsequent sequencing of the BSK3 gene in other four brr mutants ( brr2 to brr 5 ) revealed that all these mutants are caused by mutations in the BSK3 gene ( Fig 1D ) . We therefore changed the names of five brr mutants to bsk3-2 ( brr1 ) , bsk3-3 ( brr2 ) , bsk3-4 ( brr3 ) , bsk3-5 ( brr4 ) , and bsk3-6 ( brr5 ) ( Fig 1D ) . Since all five identified bsk3 mutations are semi-dominant mutations causing BSK3 loss-of-function , we were curious whether the known bsk3-1 T-DNA insertion mutant ( SALK_096500 ) [15 , 30] is also semi-dominant . The T-DNA insertion of bsk3-1 is located in the first intron in the 5’ UTR of BSK3 ( Fig 1D ) . RT-PCR did not detect any BSK3 transcripts in bsk3-1 ( Fig 1G ) , indicating that bsk3-1 is probably a null mutant . We crossed the bsk3-1 mutant into the wild-type Col-0 plants . Both heterozygous and homozygous mutant seedlings exhibited resistance to 0 . 01 and 0 . 1 μM BL . In addition , homozygous mutant seedlings exhibited stronger BL resistance ( Fig 1H and 1I ) . These results suggest that the bsk3-1 mutant is also semi-dominant , indicating a dose-dependent effect of BSK3 on BR responses . N-myristoylation is a co-translational lipid modification . The mechanisms underlying N-myristoylation are conserved among eukaryotes . Myristic acid is linked to a protein’s N-terminal glycine residue via an amide bond , which is catalyzed by an N-myristoyltransferase [36–38] . Analysis of BSK3 protein sequence using NMT-The MYR Predictor ( http://mendel . imp . ac . at/myristate/SUPLpredictor . htm ) indicated that BSK3 has a putative myristoylation sequence at its N-terminus ( Fig 2A ) , suggesting that it may be an N-myristoylated protein . To determine whether BSK3 is an N-myristoylated protein , we performed in vitro myristoylation assays . The TNT SP6 high-yield wheat germ protein expression system was used to synthesize HA-tagged BSK3 protein in the presence of [3H]myristic acid , and reaction products were analyzed by SDS-PAGE , western blot , and fluorography . Wheat germ extracts contain the N-myristoyltransferase , which can modify proteins . Consistent with the prediction by NMT-The MYR Predictor , wild-type BSK3-HA was labeled ( Fig 2B ) . The second glycine of BSK3 , a putative myristoylation site , was mutated to arginine in the bsk3-2 mutant ( Fig 2A ) . To determine whether this G2R mutation affects BSK3 myristoylation , we performed PCR-based site-directed mutagenesis to create BSK3G2R mutation and examined the myristoylation of this mutant protein . It was previously reported that the G2A mutation blocks the myristoylation of N-myristoylated proteins [39] . As a control , we included BSK3G2A-HA protein in the assays . Unlike the wild-type protein , BSK3G2A-HA and BSK3G2R-HA mutant proteins were not modified by [3H]Myristic acid ( Fig 2B ) , indicating that these two mutations block BSK3 myristoylation . N-myristoylation confers a tendency to associate with membranes [37] , indicating that BSK3 may be a membrane-associated protein . To examine BSK3 subcellular localization , we generated transgenic Arabidopsis plants expressing BSK3-mCitrine driven by the native BSK3 promoter . BSK3-mCitrine protein localized exclusively to the plasma membrane ( Fig 2C ) . To determine whether N-myristoylation is required for BSK3 plasma membrane localization , we examined the subcellular localization of BSK3G2A-mCitrine and BSK3G2R-mCitrine proteins . Unlike the wild-type protein , these two mutant proteins were mislocalized in the nucleus and cytosol ( Fig 2C ) . To further show that BSK3-mCitrine is a plasma membrane-localized protein while BSK3G2A-mCitrine and BSK3G2R-mCitrine mutant proteins are not , we performed subcellular protein fractionation . Consistent with our confocal observations , BSK3-mCitrine protein was only detected in the membrane fraction ( Fig 2D ) . By contrast , BSK3G2A-mCitrine and BSK3G2R-mCitrine mutant proteins were only detected in the soluble fraction ( Fig 2D ) . These results indicate that N-myristoylation is required for BSK3 plasma membrane localization . To determine whether BRs regulate BSK3 subcellular localization , we treated light-grown BSK3pro:BSK3-GFP seedlings with 2 μM brassinazole ( BRZ , a BR biosynthesis inhibitor ) for four days and 1 μM BL for two hours , respectively . Seedlings treated with BRZ and BL showed BSK3-GFP localization exclusively on the plasma membrane in the hypocotyls and roots ( Fig 2E ) , indicating that BRs do not regulate BSK3 plasma membrane localization . Since the effects of BSK3 on BR responses are dose-dependent , depending on BSK3 protein levels ( Fig 1H and 1I ) , we wondered whether BSK3 protein levels are regulated by BRs . Light-grown BSK3pro:BSK3-HA and BES1pro:BES1-HA seedlings were treated with 2 μM BRZ for six days and 1 μM BL for two hours , respectively . BRZ and BL treatments caused increased and reduced BES1-HA protein phosphorylation , indicating decreased and increased BR responses , respectively ( Fig 2F ) . However , BSK3-HA protein levels were not altered by BRZ and BL treatments ( Fig 2F ) , suggesting that BRs do not regulate BSK3 protein levels . All BSK family proteins contain an N-terminal kinase domain , however , contradictory results have been reported regarding whether these proteins have kinase activity in vitro . Autophosphorylation of BSK1 , 3 , 5 , 6 , 8 , and 11 were not detected in kinase assay reactions containing Mg2+ or Mn2+ [15 , 30 , 40] . However , BSK1 was shown to exhibit kinase activity , requiring Mn2+ as a divalent cation cofactor [41 , 42] . A luciferase-based kinase assay using affinity-purified BSK8-GFP protein from Arabidopsis seedlings showed that BSK8 has kinase activity [43] . Caution should be taken , however , regarding the detected BSK8 kinase activity , as kinases copurifying with BSK8-GFP may contribute to the detected kinase activity . OsBSK3 , a BSK3 ortholog in rice , was shown to exhibit weak autophosphorylation activity in kinase assay reactions containing Mg2+ or Mn2+ . However , a kinase dead OsBSK3 protein was not included as a negative control . Furthermore , the authors could not confidently claim that OsBSK3 is an active kinase since the autophosphorylation signals were too weak [44] . Together , all these results suggest that further research is needed to conclude whether the BSK family proteins are real kinases . To determine whether BSK3 has kinase activity , we performed kinase assays to examine BSK3 autophosphorylation . Kinase active GST-BRI1-KD and kinase dead GST-BRI1-KDK911E proteins were included as controls . Like BRI1 receptor kinase , all BSK family proteins contain the highly conserved lysine ( K ) residue required for binding ATP and the catalytic activity ( S2 Fig ) . We performed PCR-based site-directed mutagenesis to create kinase dead BSK3K86R mutation ( Fig 3A and S2 Fig ) . GST-BRI1-KD protein exhibited kinase activity with an exposure time of two minutes , showing stronger autophosphorylation in the presence of Mn2+ ( Fig 3B ) . However , GST-BSK3 protein did not exhibit any autophosphorylation in the presence of either Mg2+ or Mn2+ despite an exposure time of three days ( Fig 3B ) . These results indicate that unlike BRI1 receptor kinase , BSK3 does not have kinase activity under our conditions . A previous study reported that kinase dead SSP/BSK12K78R protein could fully complement the short suspensor phenotype of the ssp mutants , suggesting that SSP kinase activity may not be required for SSP function in suspensor development during embryogenesis [29] . We used the same strategy to determine whether BSK3 kinase activity , if it has kinase activity in planta , is required for BSK3 function in BR signaling . We selected two independent bsk3-1 transgenic lines expressing BSK3-HA or BSK3K86R-HA driven by the native BSK3 promoter , which exhibited similar protein levels ( Fig 3C ) . Like BSK3-HA , kinase dead BSK3K86R-HA protein fully complemented the short root phenotype of the bsk3-1 mutant ( Fig 3D ) . Unexpectedly , bsk3-1 BSK3-HA seedlings exhibited hypersensitivity to 0 . 1 μM BL in the root elongation assays , while bsk3-1 BSK3K86R-HA seedlings still exhibited resistance to 0 . 1 μM BL , although considerably weaker than bsk3-1 seedlings ( Fig 3D ) . These results indicate that kinase dead BSK3K86R-HA protein only partially rescues the bsk3-1 mutant phenotypes , suggesting that BSK3 kinase activity , or at least BSK3 binding to ATP , may be required for full BSK3 function in BR signaling . As described later , BSK3 may function as a scaffold protein in BR signaling , we could not rule out that the BSK3K86R mutation may affect the scaffold function and thereby impairs BSK3 function in BR signaling . Our genetic screen identified four mutations ( R156K , N182AQAL insertion , G226E , and G238S ) in the kinase domain of BSK3 that confer decreased BR responses , suggesting that the kinase domain is crucial for BSK3 function in BR signaling ( Figs 1D and 3A ) . To assess how three missense mutations ( R156K , G226E , and G238S ) affect BSK3 function , we used the native BSK3 promoter to express BSK3R156K-HA , BSK3G226E-HA , and BSK3G238S-HA proteins in the bsk3-1 mutant , and analyzed the ability of these mutant proteins to complement the bsk3-1 mutant . As described previously , expression of wild-type BSK3-HA protein under the control of the native BSK3 promoter complemented the bsk3-1 mutant ( Fig 3D ) . bsk3-1 plants did not exhibit obvious shoot growth defects . However , bsk3-1 transgenic plants expressing BSK3R156K-HA or BSK3G226E-HA , but not BSK3G238S-HA , exhibited slightly smaller rosette leaves ( Fig 3E ) . Western blot analyses showed that bsk3-1 BSK3R156K-HA and bsk3-1 BSK3G226E-HA plants exhibited high levels of protein expression , while bsk3-1 BSK3G238S-HA plants exhibited low levels of protein expression ( Fig 3F ) . Consistent with the slight growth defect of rosette leaves , mature bsk3-1 BSK3R156K-HA and bsk3-1 BSK3G226E-HA plants were shorter than bsk3-1 plants ( Fig 3G ) and had smaller siliques ( Fig 3H ) , indicating defective shoot and silique growth . The above results suggest that BSK3R156K and BSK3G226E mutations may have dominant-negative effects , interfering with the function of additional BSK members in BR signaling . We noticed that bsk3-1 BSK3G238S-HA plants expressed low levels of BSK3G238S-HA protein . Twenty-six independent transgenic lines were screened , and Lines 8 and 26 exhibited highest protein expression . However , BSK3G238S-HA protein levels were still much lower than those of BSK3R156K-HA and BSK3G226E-HA proteins ( Fig 3F ) , suggesting that the G238S mutation may affect BSK3 protein stability . To test this hypothesis , we analyzed bsk3-1 transgenic plants expressing BSK3-HA or BSK3G238S-HA under the control of the native BSK3 promoter . Semi-quantitative RT-PCR showed that bsk3-1 BSK3-HA lines 5 and 24 exhibited similar levels of transcripts as those of bsk3-1 BSK3G238S-HA lines 8 and 26 ( Fig 3I ) . However , western blot analyses showed that BSK3G238S-HA protein levels were much lower than those of BSK3-HA protein ( Fig 3I ) . Together , these results suggest that the G238S mutation may indeed reduce BSK3 protein stability . BSK3 was previously shown to be a substrate of BRI1 kinase in vitro [15] , however , the precise function of BSK3 in BR signaling is not known . To understand the role of BSK3 in BR signaling , we sought to identify BSK3 interactors by testing the interactions between BSK3 and the known BR signaling regulators: BRI1 receptor kinase , CDG1 kinase , BSU1 phosphatase , and BIN2 kinase . To know whether BSK3 can form a homodimer or heterodimer with the BSK family members , we also tested BSK3/BSK3 and BSK3/BSK1 protein interactions . We generated transgenic Arabidopsis plants co-expressing BSK3-HA and BRI1-GFP , CDG1-EYFP , BSU1-EYFP , BIN2-EYFP , BSK3-GFP , or BSK1-EYFP proteins . We immunoprecipitated BSK3-HA protein using an anti-HA antibody and detected the co-immunoprecipitated ( co-IPed ) GFP/EYFP fusion proteins by western blots using an anti-GFP antibody . Since a non-specific protein recognized by the anti-GFP antibody and the antibody heavy chain prevented us detecting BSU1-EYFP and BSK3-HA proteins , respectively , we could not successfully determine BSK3-HA and BSU1-EYFP protein interaction by co-IP assays . BSK3-HA co-IPed with BSK3-GFP ( Fig 4A ) , BSK1-EYFP ( Fig 4A ) , BRI1-GFP ( Fig 4B ) , BIN2-EYFP ( Fig 4C ) , but not CDG1-EYFP ( Fig 4B ) . We further confirmed these protein interactions by GST pull-down and bimolecular fluorescence complementation ( BiFC ) assays , and BSU1 was included in these assays . GST-BSK3 protein pulled down BSK3-HA and BSK1-FLAG , and GST-BRI1-KD , GST-BSU1 , and GST-BIN2 proteins pulled down BSK3-HA ( S3 Fig ) . Plasma membrane-localized yellow fluorescent signals were observed in the leaf epidermal cells when BSK3-YFPN were transiently co-expressed with BSK3-YFPC , BSK1-YFPC , BRI1-YFPC , BSU1-YFPC , or BIN2-YFPC , but not with the plasma membrane-localized receptor kinase TMK1-YFPC [45] , in Nicotiana benthamiana leaves ( Fig 4E ) . The expression of these proteins was confirmed by western blots using anti-Myc and anti-HA antibodies ( S4 Fig ) . Together , our co-IP , GST pull-down , and BiFC results demonstrate that BSK3 directly interacts with BSK3 , BSK1 , BRI1 , BSU1 , and BIN2 in the BR signaling pathway . To know whether BRs regulate BSK3 interactions with BSK3 , BSK1 , BRI1 , and BIN2 , we treated transgenic Arabidopsis seedlings co-expressing BSK3-HA and BSK3-GFP , BSK1-EYFP , BRI1-GFP , or BIN2-EYFP proteins with 1 μM BL for 30 minutes and performed co-IP assays . Consistent with previous findings [15] , BR treatment reduced BSK3-HA and BRI1-GFP protein interaction ( Fig 4D ) . However , the interactions between BSK3-HA and BSK3-GFP , BSK1-EYFP , or BIN2-EYFP proteins were not affected by BR treatment ( Fig 4D ) . These results indicate that BRs do not regulate BSK3 and BIN2 protein interaction and the formation of BSK3/BSK3 homodimers and BSK3/BSK1 heterodimers . Our co-IP , GST pull-down , and BiFC assays demonstrated that BSK3 physically interacts with the GSK3-like kinase BIN2 at the plasma membrane ( Fig 4 and S3 Fig ) . We were curious whether BSK3 is a substrate of BIN2 kinase , and therefore searched for the GSK3 consensus phosphorylation motifs ( S/T-X-X-X-S/T , X is any amino acid ) in BSK3 . BSK3 contains nine putative GSK3 consensus phosphorylation motifs ( Fig 4F ) , suggesting that BIN2 may be able to phosphorylate BSK3 . To determine whether BSK3 is a BIN2 kinase substrate , we performed kinase assays using GST-BSK3 , kinase active GST-BIN2 , and kinase dead GST-BIN2K69R proteins . A previous study showed that GST protein is not phosphorylated by BIN2 kinase , suggesting that it is not a BIN2 kinase substrate [21] . BSK3 phosphorylation was observed when GST-BSK3 was incubated with GST-BIN2 , but not with GST-BIN2K69R ( Fig 4G ) , indicating that BIN2 kinase phosphorylates BSK3 . Our results are consistent with the previous finding that BIN2 kinase phosphorylates BSK3 in vitro [30] . Together , these findings suggest that BSK3 is a substrate of BIN2 kinase . To understand the biological significance of BSK3 phosphorylation by BIN2 in BR signaling , we performed in vitro kinase assays and far-western blot assays to examine whether BIN2 phosphorylation of BSK3 may affect BSK3 interactions with BSK3 , BSK1 , BRI1 , and BSU1 . MBP-BSK3 , GST-BIN2 , and GST-BIN2K69R proteins expressed in E . coli cells were purified , and the expression levels of GST-BIN2K69R were lower than those of GST-BIN2 . Equal amounts of MBP-BSK3 proteins were incubated with GST-BIN2 or GST-BIN2K69R for kinase assays . After kinase assays , MBP-BSK3 ( 97 . 5 kDa ) and GST-BIN2 or GST-BIN2K69R ( 70 kDa ) proteins were separated by SDS-PAGE and transferred onto nitrocellulose membranes . GST-BSK3 , GST-BSK1 , GST-BRI1-KD , and GST-BSU1 proteins synthesized using the TNT SP6 high-yield wheat germ protein expression system were incubated with nitrocellulose membrane strips containing only MBP-BSK3 without GST-BIN2 or GST-BIN2K69R . We detected MBP-BSK3-bound GST fusion proteins using an anti-GST antibody . Interestingly , BIN2 phosphorylation of BSK3 enhanced MBP-BSK3 interactions with GST-BSK3 , GST-BSK1 , GST-BRI1-KD , and GST-BSU1 ( Fig 4H ) . These results suggest that BSK3 phosphorylation by BIN2 promotes BSK3/BSK3 homodimer and BSK3/BSK1 heterodimer formation , BSK3/BRI1 interaction , and BSK3/BSU1 interaction . Analysis of BSK3 protein sequence using TPRpred ( http://toolkit . tuebingen . mpg . de/tprpred ) indicated that BSK3 has three C-terminal tandem TPR motifs ( Fig 5A ) . Our genetic screen identified five mutations in BSK3 , surprisingly , no mutations were identified in the TPR motifs . We were curious whether the TPR motifs are essential for BSK3 function in BR signaling . We therefore performed PCR-based site-directed mutagenesis to create a truncated BSK3 protein without the C-terminal three tandem TPR motifs ( BSK3TPR-Δ ) ( Fig 5A ) , and examined whether this mutant protein could rescue the bsk3-1 mutant phenotypes , including the slight root growth defect and BR resistance . Two independent bsk3-1 transgenic lines expressing BSK3-HA or BSK3TPR-Δ-HA driven by the native BSK3 promoter were selected . bsk3-1 BSK3-HA line 16 and bsk3-1 BSK3TPR-Δ-HA line 10 expressed similar high protein levels , while bsk3-1 BSK3-HA line 19 and bsk3-1 BSK3TPR-Δ-HA line 11 expressed similar low protein levels ( Fig 5B ) . Like the wild-type seedlings , 0 . 1 μM BL-treated bsk3-1 BSK3-HA and bsk3-1 BSK3TPR-Δ-HA seedlings exhibited shorter roots , indicating loss of bsk3-1’s BR resistance phenotype ( Fig 5C ) . Interestingly , bsk3-1 BSK3TPR-Δ-HA seedlings exhibited less sensitivity to 0 . 1 μM BL than bsk3-1 BSK3-HA seedlings ( Fig 5C ) . Unlike line 11 , bsk3-1 BSK3TPR-Δ-HA line 10 seedlings expressed higher protein levels and fully rescued the root growth defect and BR resistance phenotype of bsk3-1 seedlings ( Fig 5C ) . These results demonstrate that BSK3TPR-Δ-HA protein fully complements the bsk3-1 mutant phenotypes , indicating that the C-terminal three tandem TPR motifs are not essential for BSK3 function in BR signaling . Interestingly , expressed at similar levels from the native BSK3 promoter , BSK3TPR-Δ-HA protein was less efficient to rescue the root growth defect and BR resistance phenotype of bsk3-1 seedlings than BSK3-HA protein ( Fig 5C ) . These results indicate that TPR deletion impairs BSK3 function , suggesting that TPR motifs are required for the full function of BSK3 in BR signaling . Previously , we demonstrated that BSK3 interacts with the BSK family members ( BSK1 and BSK3 ) , BRI1 , BSU1 , and BIN2 . We were curious whether TPR motifs are involved in these protein interactions . We therefore performed BiFC assays to test BSK3TPR-Δ interactions with BSK1TPR-Δ , BSK3TPR-Δ , BRI1 , BSU1 , and BIN2 . BSK3TPR-Δ does not interact with BSK1TPR-Δ and BSK3TPR-Δ ( Fig 5D and 5E ) , while BSK3TPR-Δ still interacts with BRI1 , BSU1 , and BIN2 ( S5 Fig ) . These results indicate that TPR deletion impairs BSK3/BSK3 homodimer and BSK3/BSK1 heterodimer formation . To understand BSK3 function in BR-mediated plant growth and developmental processes , we examined BSK3 gene expression using the GUS ( β-glucuronidase ) reporter gene driven by the native BSK3 promoter . BSK3 was expressed in the cotyledons , hypocotyls , and roots of etiolated and light-grown seedlings ( Fig 6A–6C ) . Interestingly , roots exhibited higher BSK3 expression than shoots in light-grown seedlings ( Fig 6B ) . BSK3 was also expressed in lateral roots ( Fig 6D ) , rosette leaves ( Fig 6E ) , and cauline leaves ( Fig 6F ) . In flowers , BSK3 was expressed in sepals , petals , stamens ( including filaments , anthers , and pollens ) , and the top region of pistils ( Fig 6G and 6H ) . In siliques , the top and basal regions exhibited higher BSK3 expression ( Fig 6I ) . Together , these results indicate that BSK3 is broadly expressed in a variety of organs and tissues throughout plant development , consistent with the notion that BSK3 may play an important role in BR-mediated plant growth and development . To know the effects of BSK3 gain-of-function on BR signaling and plant growth and development , we generated transgenic Arabidopsis plants that express an extra copy of BSK3 gene under the control of the native BSK3 promoter ( BSK3pro:BSK3-HA ) . We screened forty-two independent transgenic lines , and lines 6 and 38 expressing high levels of BSK3-HA protein were selected for phenotypic analyses ( Fig 7D ) . Etiolated BSK3-HA seedlings exhibited slightly increased hypocotyl growth and resistance to 2 μM BRZ ( Fig 7A and 7B ) . Light-grown BSK3-HA seedlings did not exhibit obvious root growth phenotype , while bes1-1D seedlings exhibited reduced root growth ( Fig 7C ) . bes1-1D is a new bes1 mutant that we identified in the Col-0 background , which contains the same P233L mutation as the bes1-D mutant in the Enkheim-2 ( En-2 ) background [20] . BSK3-HA seedlings exhibited reduced BES1 protein phosphorylation , although considerably weaker than bes1-1D seedlings ( Fig 7D ) , indicating increased BR signaling . Like bzr1-1D and bes1-1D , BSK3-HA plants exhibited cauline leaf and inflorescence stem fusion , causing the stem bending toward the axillary branch and cauline leaf ( Fig 7E ) . Together , these results suggest that BSK3-HA plants exhibit increased BR responses and defective shoot organ separation . To know whether increased BSK3 expression could suppress the growth defects of bri1-801 ( GABI_227B07 ) and bin2-1 mutants , we crossed the BSK3pro:BSK3-HA line 38 transgene into these mutants . Western blots using an anti-BRI1 antibody were not able to detect the full-length BRI1 protein in bri1-801 ( S7B Fig ) . If a truncated BRI1 protein is produced , this mutant protein would lack the region after LRR19 , including the BR-binding island domain , transmembrane domain , and kinase domain [46] . Therefore , this mutant BRI1 protein cannot perceive BRs to initiate BR signaling . Like the bri1-116 null mutant [4 , 24] , bri1-801 plants exhibited severe growth defects ( S7C–S7F Fig ) . All these results suggest that bri1-801 is a null mutant . Consistent with the previous findings that BSK3 overexpression from the 35S promoter partially suppresses the dwarf phenotype of bri1-116 [15] , BSK3-HA expression from the native BSK3 promoter also partially suppressed the dwarf phenotype of bri1-801 ( S7C and S7F Fig ) . In addition , our results further revealed that BSK3-HA expression partially suppressed all examined aspects of the growth defects of bri1-801 , including root growth , shoot growth , and male fertility ( S7C–S7F Fig ) . These results indicate that BSK3 can activate BR signaling without a functional BRI1 receptor , and it is an important player that regulates BRI1-mediated plant growth and developmental processes . The homozygous bin2-1 mutant exhibits severe bri1-like growth defects , such as dwarf and male sterility [34] . A previous study reported that BSK3 overexpression from the 35S promoter could not suppress the dwarf phenotype of bin2-1 [15] . However , BSK3-HA expression from the native BSK3 promoter partially suppressed the growth defects of bin2-1 . Etiolated bin2-1 BSK3-HA seedlings exhibited longer hypocotyls than those of bin2-1 seedlings ( Fig 7F and 7G ) , and mature bin2-1 BSK3-HA plants were bigger than bin2-1 plants ( Fig 7F and 7H ) . Although smaller than those of wild-type , the siliques of bin2-1 BSK3-HA plants were larger than those of bin2-1 plants and could set seeds ( Fig 7H ) . Our findings demonstrate that BSK3-HA expression can partially rescue the growth defects of the bin2-1 mutant , including shoot growth and male fertility . When we tried to test BSK3 and BSU1 protein interaction in Arabidopsis by co-IP , we crossed the BSK3pro:BSK3-HA line 38 transgene into 35Spro:BSU1-EYFP plants . BSU1-EYFP overexpression plants did not exhibit obvious growth phenotypes , looking like the wild-type plants ( Fig 8A ) . However , we noticed that F1 plants co-expressing BSK3-HA and BSU1-EYFP exhibited bes1-1D-like long and twisted rosette leaves ( Fig 8A ) [20] , indicating dramatically increased BR responses . To examine BSK3-HA and BSU1-EYFP protein expression , we performed western blots using anti-HA and anti-GFP antibodies , respectively . Interestingly , BSU1-EYFP protein levels were significantly increased in BSK3-HA BSU1-EYFP plants relative to those of BSU1-EYFP plants ( Fig 8B ) . To further determine whether increased BSU1-EYFP protein levels are caused by increased BSU1-EYFP transcripts , we performed semi-quantitative RT-PCR using primers specific for BSU1-EYFP transcripts . BSU1-EYFP transcript levels were significantly increased in BSK3-HA BSU1-EYFP plants than those of BSU1-EYFP plants ( Fig 8B ) . Since BSU1-EYFP transcription is under the control of the 35S promoter , increased BSU1-EYFP transcript levels in BSK3-HA BSU1-EYFP plants suggest that BSK3 is involved in post-transcriptional regulation of BSU1-EYFP transcript levels . Our findings revealed that BSK3 is involved in upregulating BSU1 protein levels to activate BR signaling . To determine whether BRs can upregulate BSU1 protein levels , we treated light-grown BSU1-EYFP seedlings with 0 . 01 and 0 . 1 μM BL and examined BSU1-EYFP protein levels by western blots . BL-treated BSU1-EYFP seedlings exhibited increased hypocotyl growth ( Fig 8C ) , indicating increased BR responses . Consistent with increased BR responses , BL-treated BSU1-EYFP seedlings exhibited reduced BES1 phosphorylation and increased BSU1-EYFP protein levels ( Fig 8D ) . These results demonstrate that BRs upregulate BSU1 protein levels to activate BR signaling .
Since their discovery in 2008 [15] , the functions of the BSK family proteins in the BR signaling pathway have remained elusive . In this study , we focus on BSK3 and elucidate its function in BR signaling and plant growth and development . Our genetic screen identified a G2R missense mutation in the bsk3-2 mutant , causing BSK3 loss of function and decreased BR responses ( Fig 1A–1D ) . Interestingly , the G2R mutation blocked BSK3 myristoylation and plasma membrane localization ( Fig 2B–2D ) . These results suggest that plasma membrane localization is required for BSK3 function in BR signaling . In addition to the G2R mutation , our genetic screen also identified three additional missense mutations ( R156K/bsk3-3 , G226E/bsk3-4 , and G238S/bsk3-6 ) in BSK3 kinase domain , causing BSK3 loss-of-function and decreased BR responses ( Figs 1D and 3A ) . The arginine residue corresponding to BSK3 R156 is absolutely conserved in all BSK family members , while the glycine residue corresponding to BSK3 G226 is also highly conserved in all BSK family members except BSK11 ( S2 Fig ) . Expression of BSK3R156K-HA and BSK3G226E-HA proteins under the control of the native BSK3 promoter in the bsk3-1 mutant conferred defective shoot growth , including smaller rosette leaves ( Fig 3E ) , reduced plant height ( Fig 3G ) , and smaller siliques ( Fig 3H ) . These results suggest that BSK3R156K-HA and BSK3G226E-HA proteins may have dominant-negative effects , which interfere with the function of additional BSK family members . These mutant proteins may be non-functional and sequester BSK interactors , causing BSK loss-of-function . Unlike R156K and G226E , the G238S mutation appeared to destabilize BSK3 protein to reduce its levels in the bsk3-6 mutant , causing BSK3 loss-of-function ( Fig 3I ) . The glycine residue corresponding to BSK3 G238 is absolutely conserved in all BSK family members ( S2 Fig ) . It will be interesting to know whether this glycine regulates the protein stability of other BSK family members . Together , our results demonstrate that the kinase domain is crucial for BSK3 function in BR signaling . The TPR domain is a 34 amino acid structural motif involved in mediating protein-protein interaction and assembling a multiprotein complex , which has been found in a wide variety of proteins in all organisms [47–49] . Previous studies have shown that TPR motifs are crucial for BSK1 and SSP/BSK12 functions in regulating plant defense responses and suspensor development during embryogenesis , respectively [29 , 41] . A recessive R443Q missense mutation in the TPR motifs confers BSK1 loss-of-function and defective defense responses [41] . TPR deletion completely inactivates SSP/BSK12 , causing defective suspensor development during embryogenesis [29] . More recently , TPR motifs were reported to negatively regulate OsBSK3 activity in BR signaling , as OsBSK3TPR-Δ-GFP overexpression from the 35S promoter activates BR signaling more efficiently than OsBSK3-GFP overexpression [44] . We demonstrate that the C-terminal three tandem TPR motifs are not essential for BSK3 function in BR signaling . However , TPR deletion impairs BSK3 function , suggesting that TPR motifs are required for the full function of BSK3 in BR signaling ( Fig 5C ) . Therefore , different from the negative role of TPR motifs in the control of OsBSK3 activity in BR signaling [44] , our results do not support a negative role of TPR motifs in regulating BSK3 activity in BR signaling . Interestingly , TPR deletion impairs BSK3/BSK3 interaction and BSK3/BSK1 interaction ( Fig 5D ) , suggesting that TPR motifs contribute to BSK homodimer/heterodimer formation . GSK3 kinases are highly conserved in eukaryotes and regulate diverse physiological and developmental processes [50 , 51] . In the BR signaling pathway , the GSK3-like kinase BIN2 phosphorylates BZR1 and BES1 transcription factors to inhibit BR signaling via multiple mechanisms , causing proteasomal degradation of BZR1 and BES1 , inhibiting their DNA binding , and promoting their binding to 14-3-3 proteins to cause cytoplasmic retention of BZR1 and BES1 [19 , 21 , 52 , 53] . We found that BSK3 directly interacted with BIN2 in Arabidopsis , and their interaction did not appear to be regulated by BRs ( Fig 4C and 4D ) . Furthermore , BSK3 contained nine putative GSK3 consensus phosphorylation motifs and was a BIN2 kinase substrate ( Fig 4F and 4G ) [30] . Interestingly , BIN2 phosphorylation of BSK3 enhanced BSK3 interactions with BSK1 , BSK3 , BRI1 , and BSU1 ( Fig 4H ) . These results suggest that BIN2 may have a positive role in BR signaling , promoting BSK homodimer/heterodimer formation , BSK3/BRI1 interaction , and BSK3/BSU1 interaction to enhance BR signaling . A similar positive role of GSK3 kinase in Wnt signaling was reported . GSK3 kinase phosphorylates and activates the Wnt co-receptor low-density lipoprotein receptor-related protein 6 ( LRP6 ) to activate Wnt signaling [54] . The BSU1 protein family is composed of four members ( BSU1 , BSU1-LIKE 1/BSL1 , BSL2 , and BSL3 ) and positively regulate BR signaling [14 , 16 , 55] . BSU1 phosphatase inactivates BIN2 kinase activity by dephosphorylating the phosphotyrosine residue pTyr200 of BIN2 to activate BR signaling [16] . We and others have shown that BSK3 and BSK1 physically interact with BSU1 and maybe other BSU1 family members ( BSL1 , BSL2 , and BSL3 ) to regulate BR signaling [16 , 55] . However , the molecular mechanisms underlying BSK-mediated BSU1 activation is still not known . We found that BSK3-HA expression driven by the native BSK3 promoter upregulated BSU1-EYFP transcript levels via a post-transcriptional mechanism to increase BSU1 protein levels and thereby activate BR signaling ( Fig 8B ) . These results reveal that an important mechanism to activate BR signaling is BSK3-mediated upregulation of BSU1 transcript and protein levels . The detailed mechanisms require further investigation . Our genetic analyses showed that all five bsk3 loss-of-function mutants that we identified are caused by semidominant mutations ( Fig 1 and S1 Fig ) . Interestingly , the bsk3-1 T-DNA loss-of-function mutant is also semidominant . Homozygous bsk3-1 seedlings exhibited stronger BR resistance than heterozygous bsk3-1 seedlings ( Fig 1H and 1I ) , suggesting a dose-dependent effect of BSK3 protein on BR responses . Expression of a kinase dead BSK3K86R-HA protein under the control of the native BSK3 promoter fully rescued the root growth defect and partially rescued the BR resistance phenotype of the bsk3-1 mutant , suggesting that this mutant protein can still activate BR signaling to promote root growth ( Fig 3D ) . These results provide experimental evidence to support that BSK3 may function as a scaffold protein to positively regulate BR signaling . Scaffold proteins are signal-organizing proteins that regulate a variety of signal transduction pathways . Their basic functions include assembling their interactors into specific protein complexes and localizing their interactors to specific subcellular compartments [56 , 57] . As a scaffold protein in the BR signaling pathway , BSK3 may function as monomers , homodimers , and/or heterodimers with other BSK family members . BSK3 may sequester BSU1 phosphatase and BIN2 kinase at the inner face of the plasma membrane to enhance their interaction [16] . BSK3 may also enhance the oligomerization of the BSU1 family proteins [58] . In either case , BSU1 phosphatase may dephosphorylate the phosphotyrosine residue pTyr200 of BIN2 more efficiently to inactivate BIN2 kinase and thereby activate BR signaling . Despite extensive efforts , we were not able to detect BSK3 autophosphorylation in kinase assay reactions containing either Mg2+ or Mn2+ ( Fig 3B ) , indicating that unlike BRI1 receptor kinase , BSK3 does not have kinase activity under our conditions . However , these results could not rule out that BSK3 may have kinase activity in planta . Supporting this hypothesis , the kinase dead K86R mutation impairs BSK3 function in BR signaling . Expressed at similar levels , BSK3K86R-HA protein was less efficient to rescue the BR resistance phenotype of bsk3-1 seedlings than BSK3-HA protein ( Fig 3C and 3D ) , suggesting that BSK3 kinase activity may be required for its full function in BR signaling . The mechanisms underlying BSK3 kinase activation in planta are not known . Based on the structural studies of BSK8 kinase domain [40] , one possibility is that BSK3 binding to other proteins may reverse the catalytically inactive auto-inhibition state of BSK3 to a catalytically active state . Alternatively , BSK3 phosphorylation by BRI1 receptor kinase may activate BSK3 kinase activity . This hypothesis is supported by the finding that OsBSK3 phosphorylation by OsBRI1 receptor kinase appears to enhance OsBSK3 autophosphorylation in vitro [44] . Together , our findings suggest that BSK3 may function as a scaffold protein with possible kinase activity to regulate BR signaling . Future research to demonstrate BSK3 kinase activity , investigate BSK3 kinase activation mechanisms , and identify BSK3 physiological substrates in the BR signaling pathway will advance the understanding of BR signaling mechanisms . Alternatively , our results could not rule out that BSK3 may not function as a kinase . However , full BSK3 function in BR signaling requires ATP binding , which may modulate the scaffold function of BSK3 . Supporting this hypothesis , some pseudokinases have been shown to bind ATP , which may have a structural or functional role in regulating pseudokinase activity in the absence of catalytic activity [59] . The BSK protein family is composed of twelve members . Previous studies using a loss-of-function approach to understand their role in BR-mediated plant growth and development were not successful [15 , 30] . Despite reduced growth , bsk3/4/6/7/8 pentuple mutants do not exhibit dark green and rounded leaves , male sterility , and reduced silique growth , which are typical growth phenotypes of BR deficient and response mutants [3 , 30–33] . We performed phenotypic analyses on 10 bsk T-DNA insertion mutants ( bsk1 , 2 , 3 , 4 , 5 , 6 , 8 , 10 , 11 , and 12 ) , including growth phenotypes and responses to 0 . 01 and 0 . 1 μM BL ( S1 Table ) . None of these mutants exhibited obvious growth phenotypes and BR resistance except that the bsk3-1 mutant exhibited slightly reduced root growth and BR resistance ( Fig 1I and S6 Fig ) . Supporting an important function of BSK3 in root growth , BSK3 was highly expressed in seedling roots ( Fig 6B ) . In addition , all five bsk3 mutants that we identified exhibited slightly reduced root growth ( S6A and S6B Fig ) . Our BSK3 gain-of-function approach revealed that in addition to root growth , BSK3 is also important for various shoot growth and developmental processes . BSK3-HA expression driven by the native BSK3 promoter conferred increased seedling hypocotyl growth and cauline leaf and inflorescence stem fusion ( Fig 7A , 7B and 7E ) . In addition , BSK3-HA expression partially suppressed the growth defects of bri1-801 and bin2-1 mutants , including root growth , shoot growth , and male fertility ( Fig 7F–7H and S7C–S7F Fig ) . Interestingly , BSK3R156K-HA or BSK3G226E-HA expression under the control of the native BSK3 promoter in the bsk3-1 mutant conferred reduced rosette leaf growth , plant height , and silique growth ( Fig 3E–3H ) . Together , our findings demonstrate that BSK3 plays an important role in BR-mediated plant growth and development , including root growth , shoot growth , and organ separation . Future research to generate mutant plants loss of all twelve BSK genes may provide further evidence in support of the crucial role of the BSK family proteins in BR-mediated plant growth and development .
All Arabidopsis thaliana mutants and transgenic lines were in the Columbia ( Col ) ecotype . Plants were grown under long-day conditions ( 16 h light/8 h dark ) at 22 oC . Seeds were surface sterilized in a solution containing 30% bleach and 0 . 04% triton X-100 for 15 minutes , washed 3 times in sterile water , and cold treated for 3 days at 4 oC to synchronize germination . Seedlings were grown on ½ LS ( Linsmaier and Skoog medium , Caisson Labs ) plates containing 1% sucrose and 0 . 8% agar . To make a BSK3pro:GUS construct , BSK3 promoter was cloned into pENTR/D-TOPO using the pENTR directional TOPO cloning kit ( Invitrogen ) . BSK3 promoter was recombined into pGWB203 ( GUS ) [60] using the Gateway LR clonase II enzyme mix ( Invitrogen ) . To make BSK3pro:BSK3WT/K86R/R156K/G226E/G238S-HA , BSK3pro:BSK3-GFP , and BES1pro:BES1-HA constructs , BSK3 or BES1 genomic DNA containing the promoter and coding sequence without the stop codon was cloned into pENTR/D-TOPO . Site-directed mutagenesis PCRs were performed to create BSK3K86R/R156K/G226E/G238S mutations . To make a BSK3pro:BSK3TPR-Δ-HA construct , BSK3 genomic DNA containing the promoter and partial coding sequence that does not encode three TPR motifs was cloned into pENTR/D-TOPO . All above inserts were recombined into pEarleyGate 301 ( HA ) [61] or pGWB204 ( GFP ) [60] . To make BSK3pro:BSK3WT/G2A/G2R-mCitrine constructs , BSK3 promoter , full-length cDNA without the stop codon containing the WT/G2A/G2R mutations , and the mCitrine coding sequence were cloned into pDONR-P4P1R , pDONR221 , and pDONR-B2RB3 , respectively . These three DNA fragments were assembled into pB7m34GW [62] using the Gateway LR clonase II Plus enzyme mix ( Invitrogen ) . To make BSK1 , CDG1 , BSU1 , and BIN2 overexpression constructs , the full-length cDNA without the stop codon of each gene was cloned into pENTR/D-TOPO . All inserts were recombined into pK7YWG2 ( EYFP ) [62] . All binary vectors were introduced into Agrobacterium tumefaciens strain GV3101 ( helper plasmid pMP90 ) by electroporation . The floral dip method was used to transform Arabidopsis plants [63] . Transgenic plants were selected on ½ LS agar plates containing 0 . 01% Basta ( Finale herbicide , Bayer CropScience ) , hygromycin ( 25 μg/ml ) , or 50 μg/ml kanamycin without sucrose . EMS-mutagenized M2 seeds ( Catalog nos . M2E-02-05 and M2E-01A-07 , ~ 448100 M2 seeds from ~ 56012 M1 plants ) and activation-tagged lines ( Stock no . CS31100 , ~ 62000 lines ) were purchased from Lehle Seeds and Arabidopsis Biological Resource Center , respectively . Sterilized seeds were grown on ½ LS agar plates containing 0 . 1 μM brassinolide ( BL ) ( Daiichi Fine Chemical ) . Seven-day-old light-grown BL resistant seedlings exhibiting long roots were selected . All candidate mutants were retested for BL resistance in the next generation . The semi-dominant brr1 mutant was crossed into the Landsberg erecta ( Ler ) ecotype to create a mapping population . DNA extractions were performed using a CTAB method [64] from BL sensitive F2 plants , and SSLP ( simple sequence length polymorphism ) , INDEL ( insertion-deletion polymorphism ) , and CAPS ( cleaved amplified polymorphic sequence ) markers were used to map the BSK3 mutation in the brr1 mutant by PCRs . Total protein extracts were prepared from Arabidopsis seedlings using an extraction buffer containing 50 mM Tris·HCl ( pH 7 . 5 ) , 150 mM NaCl , and 0 . 5% Igepal CA-630 . Microsomal proteins were prepared from Arabidopsis seedlings or Nicotiana benthamiana leaves as described previously [65] . The complete EDTA-free protease inhibitor cocktail ( Roche Applied Science ) was added to all buffers . All centrifugation steps were carried out at 4 oC . For protein fractionation to isolate microsomal proteins , total protein extracts were centrifuged for 30 minutes at 5000 × g , and supernatants were then centrifuged for 1 hour at 100000 × g . Co-IP and western blots were performed as previously described with slight modification [66] . Protein G agarose beads ( Roche Applied Science ) were used to collect the immune complexes . The tris-buffered saline buffer ( TBST , 0 . 05% tween 20 , pH7 . 6 ) was used for western blots . Proteins were detected using the SuperSignal West Pico or Dura chemiluminescent substrates ( Thermo Scientific ) . Far-western blot assays were performed to examine BSK3 interactions with BSK3 , BSK1 , BRI1 , and BSU1 . MBP-BSK3 , GST-BIN2 , and GST-BIN2K69R fusion proteins were expressed in E . coli BL21 ( DE3 ) cells and purified using amylose resin ( New England Biolabs ) and glutathione sepharose 4B beads ( GE Healthcare Life Sciences ) , respectively , according to the manufacturer's instructions . MBP-BSK3 was incubated with GST-BIN2 or GST-BIN2K69R in 100 μl kinase buffer ( 20 mM Tris-HCl [pH 7 . 5] , 10 mM MgCl2 , 5 mM dithiothreitol , and 100 μM ATP ) for 2 hours at 30 oC . After kinase assays , proteins were analyzed by SDS-PAGE and transferred onto nitrocellulose membranes . Nitrocellulose membranes were stained with ponceau S to detect MBP-BSK3 and GST-BIN2 or GST-BIN2K69R proteins . Nitrocellulose membrane strips containing only MBP-BSK3 without GST-BIN2 or GST-BIN2K69R were incubated with 100 μl of GST fusion proteins overnight at 4 oC . GST-BSK3 , GST-BSK1 , GST-BRI1-KD , and GST-BSU1 fusion proteins were synthesized using the TNT SP6 high-yield wheat germ protein expression system ( Promega ) . GST fusion proteins bound to MBP-BSK3 were detected by an HRP-conjugated anti-GST antibody ( GE Healthcare Life Sciences ) . RNAs were prepared using the Spectrum™ plant total RNA kit ( Sigma ) . An on-column DNase I digest set ( Sigma ) was used to remove contaminating DNA . Complementary DNAs ( cDNAs ) were synthesized using the SuperScript III first-strand synthesis system ( Invitrogen ) . RT-PCR reactions were carried out using gene specific primers , and PCR products were analyzed by agarose gel electrophoresis . BSK3 full-length cDNA without the stop codon was cloned into pTNT ( HA ) to generate a C-terminal HA epitope-tagged BSK3 construct . Site-directed mutagenesis PCRs were performed to create BSK3G2A/G2R mutations . Proteins were synthesized using the TNT SP6 high-yield wheat germ protein expression system . [3H]Myristic acid ( tetradecanoic acid ) ( PerkinElmer ) was added to the reactions at a concentration of 0 . 5 μCi/μl for radiolabelling . Reaction products were analyzed by SDS-PAGE , western blot , and fluorography . BIN2 or BSK3 full-length cDNA was cloned into pENTR/D-TOPO . Similarly , BRI1 partial cDNA encoding the kinase domain containing a stop codon ( BRI1-KD ) was cloned into in the same vector . Site-directed mutagenesis PCRs were performed to create BSK3K86R , BIN2K69R , and BRI1-KDK911E kinase dead mutations . All DNA fragments were recombined into pTNT ( GST ) or pDEST15 ( GST ) to generate N-terminal GST-tagged fusion protein constructs . GST fusion proteins were synthesized using the TNT SP6 high-yield wheat germ protein expression system or expressed in E . coli BL21 ( DE3 ) cells induced by 1 mM IPTG for 4 hours at 30 oC . All GST fusion proteins were purified using glutathione sepharose 4B beads according to the manufacturer's instructions . Purified GST fusion proteins were incubated in 50 μl kinase buffer ( 20 mM Tris-HCl [pH 7 . 5] , 10 mM MgCl2 or MnCl2 , 5 mM dithiothreitol , and 100 μM ATP ) containing 10 μCi [γ-32P]ATP ( PerkinElmer ) for 1 hour at 30 oC or 2 hours at room temperature . Kinase reactions were terminated by adding 20 μL 4x SDS-PAGE sample buffer and boiled for 5 minutes . Reaction products were analyzed by SDS-PAGE and fluorography . BSU1 full-length cDNA was cloned into pENTR/D-TOPO and recombined into pTNT ( GST ) to generate an N-terminal GST-tagged fusion protein construct . BSK1 full-length cDNA without the stop codon was cloned into pENTR/D-TOPO and recombined into pTNT ( FLAG ) to generate a C-terminal FLAG-tagged fusion protein construct . GST fusion proteins were synthesized using the TNT SP6 high-yield wheat germ protein expression system and purified using glutathione sepharose 4B beads . GST pull-down assays were performed in the PBS buffer ( pH 7 . 4 ) containing 1 mM dithiothreitol and 0 . 1 to 1% igepal CA-630 at 4 oC . After 2 hours of incubation of GST fusion proteins and HA- or FLAG-tagged proteins , beads were washed 4 times with the same buffer . Finally , Beads were resuspended in 1x SDS-PAGE sample buffer and boiled for 5 minutes . The supernatants were analyzed by SDS-PAGE and western blots using anti-GST , anti-HA , and anti-FLAG antibodies . The full-length or partial cDNA of BSK3 , BSK3TPR-Δ , BSK1 , BSK1TPR-Δ , BRI1 , BSU1 , BIN2 , or TMK1 without the stop codons in pENTR/D-TOPO was recombined into pSPYNE-35S and pSPYCE-35S [67] to generate BiFC expression constructs . These constructs were introduced into Agrobacterium tumefaciens strain GV3101 ( helper plasmid pMP90 ) by electroporation . BiFC assays were performed in an Nicotiana benthamiana transient expression system as previously described [68] . Confocal microscopy was performed with the Leica DM IRE2 inverted microscope . GUS staining of plant tissues was performed at 37 oC in a solution containing 100 mM sodium phosphate ( pH 7 . 0 ) , 10 mM EDTA , 0 . 5 mM K4Fe[CN]6 , 0 . 5 mM K3Fe[CN]6 , 0 . 1% triton X-100 , and 1 mM X-Gluc ( 5-Bromo-4-chloro-3-indoxyl-beta-D-glucuronide cyclohexylammonium salt ) ( Gold Biotechnology ) . GUS-stained tissues were incubated in 70% ethanol to remove chlorophyll and cleared in the Hoyer's solution . GUS expression patterns were imaged with a Leica MZ FLIII fluorescent dissecting microscope using the imaging software Leica Application Suite V4 . 0 . All statistical analyses were carried out by analysis of variance ( ANOVA ) with the JMP Pro 13 . 1 software ( SAS Institute ) . Tukey’s HSD ( honestly significant difference ) test results were grouped by letters , indicating measurements that were not significantly different at 95% confidence . Different letters indicate significant differences ( P < 0 . 05 ) . Arabidopsis Genome Initiative locus identifiers for the genes employed in this study are as follows: BSK1 ( At4g35230 ) , BSK2 ( At5g46570 ) , BSK3 ( At4g00710 ) , BSK4 ( At1g01740 ) , BSK5 ( At5g59010 ) , BSK6 ( At3g54030 ) , BSK7 ( At1g63500 ) , BSK8 ( At5g41260 ) , BSK9 ( At3g09240 ) , BSK10 ( At5g01060 ) , BSK11 ( At1g50990 ) , BSK12/SSP ( At2g17090 ) , BRI1 ( At4g39400 ) , BSU1 ( At1g03445 ) , CDG1 ( At3g26940 ) , BES1 ( At1g19350 ) , BIN2 ( At4g18710 ) , TMK1 ( At1g66150 ) , and ACTIN 2 ( At3g18780 ) . | Steroid hormones exist in both animals and plants . Brassinosteroids ( BRs ) are steroid hormones that regulate numerous growth and developmental processes throughout the plant life cycle . Discovered in 2008 , the BRASSINOSTEROID-SIGNALING KINASE ( BSK ) protein family is composed of twelve members . However , the functions of these receptor-like cytoplasmic kinases in the BR signaling pathway are poorly understood . Here , we focus on BSK3 and investigate its function in BR signaling and plant growth and development . Our genetic and biochemical studies suggest that BSK3 may function as a scaffold protein to regulate BR signaling . BSK3 is important for BR-mediated root growth , shoot growth , and organ separation . BSK3 activates BR signaling via upregulating BSU1 transcript levels , and BIN2 phosphorylation of BSK3 influences BSK3 interactions with other signaling components , revealing new layers of regulation in BR signaling . Together , our findings elucidate the function of BSK3 in BR signaling and plant growth and development , and provide new insights into early BR signaling mechanisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"plant",
"anatomy",
"plant",
"growth",
"and",
"development",
"membrane",
"proteins",
"developmental",
"biology",
"protein",
"expression",
"plant",
"science",
"seedlings",
"molecular",
"biology",
"techniques",
"plants",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"protein",
"kinase",
"signaling",
"cascade",
"leaves",
"molecular",
"biology",
"cell",
"membranes",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"gene",
"expression",
"and",
"vector",
"techniques",
"signal",
"transduction",
"eukaryota",
"cell",
"biology",
"root",
"growth",
"phenotypes",
"genetics",
"biology",
"and",
"life",
"sciences",
"cell",
"signaling",
"organisms",
"signaling",
"cascades"
] | 2019 | BRASSINOSTEROID-SIGNALING KINASE 3, a plasma membrane-associated scaffold protein involved in early brassinosteroid signaling |
With the expansion of soil transmitted helminth ( STH ) intervention efforts and the corresponding decline in infection prevalence , there is an increased need for sensitive and specific STH diagnostic assays . Previously , through next generation sequencing ( NGS ) -based identification and targeting of non-coding , high copy-number repetitive DNA sequences , we described the development of a panel of improved quantitative real-time PCR ( qPCR ) -based assays for the detection of Necator americanus , Ancylostoma duodenale , Ancylostoma ceylanicum , Trichuris trichiura , and Strongyloides stercoralis . However , due to the phenomenon of chromosome diminution , a similar assay based on high copy-number repetitive DNA was not developed for the detection of Ascaris lumbricoides . Recently , the publication of a reference-level germline genome sequence for A . lumbricoides has facilitated our development of an improved assay for this human pathogen of vast global importance . Repurposing raw DNA sequence reads from a previously published Illumina-generated , NGS-based A . lumbricoides germline genome sequencing project , we performed a cluster-based repeat analysis utilizing RepeatExplorer2 software . This analysis identified the most prevalent repetitive DNA element of the A . lumbricoides germline genome ( AGR , Ascaris germline repeat ) , which was then used to develop an improved qPCR assay . During experimental validation , this assay demonstrated a fold increase in sensitivity of ~3 , 100 , as determined by relative Cq values , when compared with an assay utilizing a previously published , frequently employed , ribosomal internal transcribed spacer ( ITS ) DNA target . A comparative analysis of 2 , 784 field-collected samples was then performed , successfully verifying this improved sensitivity . Through analysis of the germline genome sequence of A . lumbricoides , a vastly improved qPCR assay has been developed . This assay , utilizing a high copy-number repeat target found in eggs and embryos ( the AGR repeat ) , will improve prevalence estimates that are fundamental to the programmatic decision-making process , while simultaneously strengthening mathematical models used to examine STH infection rates . Furthermore , through the identification of an optimal target for PCR , future assay development efforts will also benefit , as the identity of the optimized repeat DNA target is likely to remain unchanged despite continued improvement in PCR-based diagnostic technologies .
Believed responsible for more than 800 million global infections , Ascaris lumbricoides is the most prevalent of the human-infecting soil transmitted helminths ( STH ) [1–2] . As recently as 2017 , infections with this parasite were believed to result in approximaely 861 , 000 disability adjusted life years [3] , generating nearly 45% of the global years lived with disability attributable to the overall burden of STH infections [3] . Due to an improved understanding of the scope of this disease burden , there is now an increased recognition of the global health impact of A . lumbricoides and the other STH infections . Such awareness has resulted in the expansion of infection and risk mapping efforts [4–9] and operational research studies intended to improve , expand , and more fully understand the impacts associated with interventions [10–15] . Similarly , due to exponential improvements in approaches to mathematical modelling , the roles played by these valuable tools for shaping and informing the STH programmatic decision making process continue to increase [5–6 , 16–18] . Fundamentally , such operational research efforts and modelling strategies rely heavily upon the availability of accurate data . Such reliance is particularly critical following interventions that have resulted in declining prevalence , drawing greater attention to the ramifications of employing insensitive diagnostic methods such as Kato-Katz [19] . Therefore , sensitive and specific diagnostic tools facilitating the collection of accurate data are increasingly critical for the proper interpretation of findings and the veracity of resulting conclusions . Previously , we described a pipeline for the identification of high-copy number repetitive DNA elements for use as semi-quantitative real-time PCR ( qPCR ) targets for the detection of various STH species [20–21] . These targets , identified utilizing next-generation sequencing ( NGS ) -based analysis tools , have facilitated improved sensitivity and specificity of detection , leading to their adoption in various diagnostic efforts and operational research ( OR ) studies such as the DeWorm3 cluster randomized trials [11 , 14 , 22] . Despite the availability of such tools , to date , the qPCR-based detection of A . lumbricoides has depended upon less optimal targets , such as ribosomal internal transcribed spacer ( ITS ) sequences [20 , 23–24] . This shortcoming is rooted in the unique process of chromosome diminution , whereby some species , including certain members of the order Ascaridida , undergo programmed elimination of select and reproducible regions of their gDNA during development [25–26] . In the case of A . lumbricoides , diminution occurs between the third and seventh embryonic divisions [27] , and an estimated 13% of the haploid germline genome is eliminated by this process [25] , including the most abundant of the genome’s tandemly repeated sequences [26] . Such elimination of highly repetitive , non-coding sequences during embryonic development renders ribosomal repeats the highest copy number gDNA sequences remaining in the genomes of larval and adult Ascaris worms . As pure gDNA is more easily obtained from adult worms , initial analyses using our pipeline utilized adult DNA extracts , and therefore failed to identify repeats present at higher copy number than the ribosomal ITS sequences [20] . However , STH diagnosis is dependent upon the detection of DNA from eggs/early embryos extracted from the stool of infected individuals . Thus , identifying an optimal qPCR target requires examination of egg-derived DNA , possessing pre-diminution gDNA sequences . Acknowledging this shortcoming in the currently available PCR diagnostic toolkit , we now describe the development of an Ascaris germline assay utilizing a highly repetitive DNA element whose copy number is reduced by an estimated 99% in the post-diminution genome of larval and adult worms [26] . This 120 bp target , hereafter referred to as the Ascaris germline repeat ( AGR ) , was previously estimated to constitute approximately 8 . 9% of the Ascaris germline genome [26] , and further analysis of germline sequence reads utilizing RepeatExplorer2 , a Galaxy-based computational tool [28] supports the prediction that this tandem repeat represents the most abundant germline gDNA sequence . The incorporation of a new PCR-based assay utilizing this improved target into our previously described STH diagnostic pipeline [20–21] , represents a significant diagnostic improvement with the capacity to aid future programmatic efforts .
The use of human samples in this study was approved by the reviewing body at the International Centre for Diarrhoeal Disease Research , Bangladesh ( protocol # PR-14105 ) and by the University of California at Berkeley Committee for Protection of Human Subjects ( protocol # 2014-08-6658 ) . Three independently prepared , paired-end DNA libraries of raw Illumina sequencing reads , previously utilized for the assembly of a reference-quality A . lumbricoides germline genome [26] , were repurposed for use in this study ( Sequence Read Archive [SRA] BioProject number PRJNA511996 ) . Prior to SRA upload , all reads were trimmed to a uniform length of 93 bp and a full description of sequencing and filtering methodologies has been described elsewhere [26] . Utilizing a randomly selected subset of 500 , 000 reads , each paired-end library was analyzed using RepeatExplorer2 , a Galaxy-based analysis tool for the identification of repetitive DNA elements [28] . These analyses were used to identify the highest copy number genomic DNA sequences , which were selected as PCR targets for further analysis ( Fig 1 ) . All RepeatExplorer analyses were performed using default settings , without advanced options , and with the “Select Queue” set to “Basic and Fast” . Utilizing default parameters for PrimerQuest Tool software ( Integrated DNA Technologies , Coralville , IA ) , a candidate primer-probe pairing was designed that targeted our identified DNA repeat sequence . Primer-BLAST , available from the National Center for Biotechnology Information ( NCBI ) website ( www . ncbi . nlm . nih . gov ) , was employed to determine whether or not our candidate primers matched off-target template sequences found within the RefSeq Representative Genome Database , and NCBI’s nucleotide collection database . Following analysis , primers and probe were synthesized by Integrated DNA Technologies . Probe chemistry included labeling with a 6-FAM fluorophore at the 5’ end , and double quenching with ZEN ( internal ) and 3IABkFQ ( 3’ end ) chemistries ( Table 1 ) . Assay validation and optimization experiments were performed as previously described [20–21] . Briefly , utilizing 200 pg of pure A . lumbricoides gDNA isolated from an adult female worm as template , optimal primer concentrations were determined by titrating forward and reverse primers in independent 7 μL reactions containing 3 . 5 μL of TaqPath ProAmp Master Mix ( ThermoFisher Scientific , Waltham , MA ) . Employing doubling dilutions , primers were tested at concentrations ranging from 1000 nM to 62 . 5 nM , with forward and reverse primer concentrations tested in all possible dilution combinations . Optimal AGR primer concentrations were then utilized in reactions intended to verify assay specificity , whereby 2 ng of purified genomic DNA isolated from adult Necator americanus , adult Ancylostoma duodenale , adult Ancylostoma ceylanicum , adult Trichuris trichiura , Strongyloides stercoralis L1 larvae , adult Schistosoma mansoni , adult Anisakis typica , adult Baylisascaris procyonis , and adult Parascaris univalens , were used as template in separate reactions . Additionally , testing against human DNA , gDNA from Candida albicans ( strain L26 ) ( BEI Resources , Manassas , VA ) , DNA from the common gut bacteria Escherichia coli , and gDNA from a “mock” microbial community ( v5 . 2H ) ( BEI Resources ) also occurred . As a final validation , a panel of 20 infection-naïve , commercially available human stool samples were obtained for testing ( BioIVT , Westbury , NY ) . DNA extraction was performed as previously described [29] and each extract was then tested for the presence of Ascaris signal . Utilizing our AGR qPCR assay primers , pure A . lumbricoides gDNA was amplified by conventional PCR . Reactions in 25 μL volumes , containing 12 . 5 μL of Q5 Hot Start High-Fidelity 2X Master Mix ( New England Biolabs , Ipswich , MA ) and 500 nM concentrations of each primer were amplified with an initial 30 second incubation at 98°C; followed by 35 cycles of 98°C for 10 seconds , 63°C for 30 seconds , and 72°C for 30 seconds; and a final 2 minute extension step at 72°C . Following cycling , PCR products were cloned into the pCR-Blunt II-TOPO vector ( ThermoFisher Scientific ) in accordance with the manufacturer’s suggested protocol , and NEB Express Competent E . coli ( New England Biolabs ) were transformed with 3 μL of the ligated plasmid . Transformed competent cells were then plated on LB-kanamycin plates , and grown at 37°C overnight . Colonies were picked , and colony PCR was performed in 25 μL reactions containing 12 . 5 μL of One Taq 2x Master Mix ( New England Biolabs ) , with 500 nM M13 forward and reverse primers . Cycling began with an initial 30 second denaturation at 94°C , followed by 35 reaction cycles of 94°C for 15 seconds , 44°C for 30 seconds , and 68°C for 90 seconds; and a final extension for 5 minutes at 68°C . Reaction products were sequenced , and a plasmid clone containing a single copy of the correct AGR repeat element was selected for use as a positive control in all future experiments . In order to determine assay efficiency , a panel of 10-fold plasmid serial dilutions was generated . Dilutions ranged from 100 pg/μL to 100 ag/μL . Because the control plasmid is 3 , 595 bp in size , and the average mass of a single nucleotide base pair is estimated to be 650 Da , 100 ag of plasmid was estimated to correspond to approximately 50 copies of the plasmid . Utilizing this information , approximate copy numbers were calculated for each concentration within the serial dilution series . Optimized reaction conditions were then employed to perform 11 or 12 reaction replicates for each dilution . Mean Cq values were calculated for reactions performed on each concentration of template , and a reaction efficiency was calculated . To determine assay detection limits , a panel of banked DNA extracts , previously isolated ( as described elsewhere [29] ) from 50 mg naïve stool samples that had been spiked with known numbers of A . lumbricoides eggs , was tested for the presence of detectable levels of A . lumbricoides target DNA . These samples were prepared and extracted as part of an ongoing , unrelated study in which the identification and isolation of eggs utilized for spiking was performed using the McMaster egg counting technique as previously described [30] . Eggs were carefully removed from their parent samples under the microscope , briefly rinsed in nuclease-free water , and then added to the naïve stool . Following the addition of eggs , DNA was extracted from spiked aliquots as previously described [29] . All testing occurred in duplicate , and was performed using the experimentally-determined optimal AGR assay conditions . To facilitate inter-assay comparison , samples were similarly tested using a previously described assay that targets a ribosomal ITS2 sequence [20] . In total , 19 samples were tested . Four samples were spiked with 40 eggs , four with 10 eggs , four with 5 eggs , and four with 2 eggs . An additional three samples containing DNA from a single egg completed the panel .
RepeatExplorer2 analysis software was employed to identify genomic DNA elements of putatively greatest copy number from sequence data derived from three , previously prepared , paired-end libraries of egg-derived A . lumbricoides DNA [26] . For each library , the RepeatExplorer-generated cluster containing the largest number of DNA sequence reads contained a 120 bp satellite element , previously identified as the most numerous within the A . lumbricoides germline genome [26] . Similarly , for each analyzed library , a supercluster comprised of multiple clusters each mapping to this repeat was predicted to represent between 7 . 9% and 16 . 3% of the germline genome . While it is important to note that such superclusters contain additional sequence fragments ( flanking regions , etc . ) , when considered as rough representations of a sequence’s genome percentage , these estimates are consistent with the 8 . 9% prediction made by Wang , et al [26] . These results strongly suggest that this repetitive sequence is the most prevalent repeat DNA element within the germline genome of A . lumbricoides . Utilizing this 120 bp AGR sequence , PrimerQuest Tool software was employed to design a candidate primer-probe set and Primer-BLAST analysis of this set returned only Ascaris-derived product predictions , minimizing the likelihood of experimental off-target PCR amplification . As previously described , a titration of doubling dilutions of primer candidates was employed to determine optimal primer concentrations [20] . As determined by mean Cq value , optimal concentrations were determined to be 125 nM for the forward primer and 500 nM for the reverse primer . Utilizing these primer concentrations , assay specificity was verified: 2 ng of template failed to produce off-target amplification for any of the species or samples tested . Similarly , testing of all DNA extracts from the infection-naïve stool panel failed to produce Ascaris signal , indicating that cross-reactivity with common elements of the gut flora is unlikely to occur . By testing a titration of our generated control plasmid ( S1 Fig ) , assay efficiency was determined . Utilizing plasmid size to determine target copy number per titration , a standard curve was generated by plotting target copy # vs . mean Cq value ( Fig 2 ) . The slope of this curve was determined to be -3 . 3216 , with a reaction efficiency of 100 . 1% and an amplification factor of 2 . 00 . Utilizing DNA extracts obtained from naïve stool samples spiked with known numbers of A . lumbricoides eggs , assay detection limits were determined . Results using the new AGR assay indicated that target detection was possible from all stool samples spiked with all tested concentrations of eggs ranging from 40 eggs to a single egg ( Table 2 ) . In contrast , results obtained when testing with the ITS-targeting assay failed to allow for consistent detection at both 1 and 2 egg concentrations ( Table 2 ) . Comparing results obtained using the newly described qPCR AGR assay with those generated through testing with a previously described , ribosomal ITS-targeting qPCR assay [20] , an analysis of 2 , 784 human stool DNA extracts was performed . 349 samples were determined to be positive using the ITS-targeting assay , while 643 samples were determined to be positive utilizing the newly described qPCR AGR assay . Of the 349 ITS-assay positives , only two were negative when tested by the new AGR assay . In contrast , of the 643 samples determined to be positive by the AGR assay , 296 were negative when tested by the ribosomal ITS assay ( Table 3 ) . This led to a sensitivity of 99 . 69% for the AGR assay , and an ITS-targeting assay sensitivity of 54 . 11% . Minimum , maximum , median , and quartile values for the ITS-assay-positive sample population , the AGR-assay-positive sample population , and the AGR-assay-positive , ITS-assay-negative sample population are shown in Fig 3 . In an attempt to quantify the improvement in reaction sensitivity offered by the new AGR assay , an average reduction in mean Cq value was calculated for all samples which tested positive by both experimental assays , excluding a single sample which produced a lower Cq value when tested using the ribosomal ITS assay ( n = 346 ) . To calculate this average reduction in mean Cq , the difference in mean Cq values for each co-positive sample was determined by subtracting the mean Cq value for the ribosomal ITS-targeting assay from the mean Cq value for the AGR assay . The average of these differences was then determined to be 11 . 51 cycles ( range of 0 . 55–14 . 99 ) ( Fig 4 ) . This average change in Cq value corresponds to a fold increase in target number between the two qPCR assays of ~3 , 100 , which resulted in the detection of Ascaris DNA in nearly twice as many stool samples .
With the expansion of treatment efforts and the resulting declines in infection prevalence and intensity , the sensitivity and specificity of STH diagnostic methods are increasingly important . Post-treatment surveys and population surveillance efforts are only as precise as the tools used to perform them and inconsistent tools may result in mismeasurement or misinterpretation of intervention impact . [31] . As such , diagnostic accuracy is critical for making assessments , and a given study’s programmatic value is inherently tied to diagnostic capability . As OR efforts increasingly work towards the definition of transmission breakpoints , important decisions will be made based upon diagnostically determined prevalence levels under settings of declining parasite burden and decreasing infection intensities . The importance of diagnostic accuracy is embodied by the criteria governing the DeWorm3 cluster randomized trials , which state that “transmission interruption in a cluster will be defined as achieving a prevalence of each STH species of ≤2% …by qPCR 24 months after the final round of MDA” [11] . However , the attainment of a 2% prevalence rate is inherently linked to the test used for prevalence determination . Therefore , understanding diagnostic performance is critical for proper decision making , and maximizing diagnostic sensitivity increases confidence when breakpoint thresholds are attained , minimizing the odds of future recrudescence . Previously , Easton , et al . , described the theoretical limits of detection for both Kato-Katz and PCR as a function of the sample volume used for diagnosis [31] . As such , a 50 mg stool sample , analyzed by PCR has a theoretical limit of detection of 20 eggs per gram , should a single egg be present within the analyzed aliquot . However , Easton and colleagues also point out that with sufficient sensitivity , shed DNA , or DNA resulting from egg degradation could also be detected , allowing for further improvement over microscopy-based techniques that are dependent upon the presence of intact eggs within the sample aliquot tested [31] . While such levels of sensitivity may appear to have reduced importance when one considers that a single adult female Ascaris worm has been estimated to shed as many as 200 , 000 eggs per day [32] , egg shedding varies considerably from person to person , and factors such as individual host immunity , geography , age of worm , worm burden , and intervention history can drastically alter patterns of egg production [33] . By selecting a molecular target with dramatically improved copy number , the capacity to detect pathogen signal is greatly improved , theoretically pushing limits of detection to previously impossible levels ( Table 4 ) . Recognizing the need for optimal sensitivity in molecular diagnosis , we previously described the identification of improved qPCR targets for the detection of a number of human-infecting soil transmitted helminths [20–21] . However , due to the unusual phenomenon of chromosome diminution , whereby repeat-enriched portions of the genomic DNA are eliminated between the third and seventh cellular divisions , we were unable to identify an appropriate , novel , high copy-number repeat DNA element within the adult genome of A . lumbricoides . Recently , due to the publication of a reference-quality germline genome sequence for A . lumbricoides [26] , we have been able to overcome this challenge with the selection of a highly repetitive DNA target that yields vastly superior sensitivity over previously utilized target DNA sequences . Present in both the A . lumbricoides and A . suum germline genomes , this target facilitates improved diagnostic detection of all human Ascaris infections . Representing an estimated 8 . 9% of the germline genome , yet only 120 bp in length , it is not surprising that the DNA target utilized by our new AGR assay facilitated a dramatic decrease in Cq values when compared to qPCR tests based on ribosomal DNA targets . With an estimated genome size of 334 Mb [26] , nearly 2 . 5 x 105 copies of this AGR element are believed to exist per haploid A . lumbricoides genome . This is in sharp contrast to the estimated 42 copies of ribosomal DNA present in Ascaris [34] . Interestingly , assuming similar reaction efficiencies , these copy numbers would suggest a Cq difference of just over 12 , in near agreement with the 11 . 51 mean cycle difference which was determined during the experimental testing of field samples described here . Such drastic improvement in sensitivity should facilitate detection of Ascaris DNA at levels well below the quantity which is recoverable from a single egg , a hypothesis further supported by our spiking experiment results ( Table 2 ) . The validity of this sensitivity increase was reinforced by the results of the extensive specificity testing which we performed , providing strong evidence that the increased rates of positivity do not result from non-specific , off-target amplification . It should be noted that a shortcoming of the performed spiking experiment was a failure to utilize an IAC during the DNA extraction procedure . However , as results for spiked sample testing existed for both the AGR assay and the ITS-targeting assay , an assessment of comparative sensitivity remained possible . While it is unfortunate that this failure to include an IAC prevented the drawing of meaningful correlations between Cq values and EPG levels , it is worth mentioning that large OR efforts , such as the DeWorm3 cluster randomized trials , aim to assess transmission break points based solely upon infection prevalence , irrespective of infection intensity [11] . In addition to their direct OR and surveillance functions , sensitive and specific diagnostic tools allow the research community to amass large bodies of accurate data , essential for the expansion and development of novel ideas and methodologies . The increased reliance on such data is seen in the incremental advancement of modeling efforts , a group of tools playing an ever-increasing role in both research and programmatic communities . Similarly , innovative ideas , such as the possibility of utilizing environmental sampling for STH surveillance [35] have historically been hampered by insufficient diagnostic options . However , with a resurging interest in these alternative methodologies [36–37] , the availability of more sensitive tools will be critical , as such samples will likely rely upon larger sample masses , resulting in the dilution of molecular signal . Furthermore , while it is likely that future technologies will eventually render the current methods of qPCR-based diagnostics obsolete , prevalent targets will remain prevalent and may prove useful as new technologies come online . As such , the discovery of optimal targets should have a lasting impact on the field of infectious disease diagnostics . While detection of parasite DNA target at sub-single egg concentrations greatly improves the sensitivity of detection , expanded sensitivity can also result in a potential complication . The issue is that higher copy-number DNA targets , coupled with excellent qPCR efficiencies , render assays increasingly susceptible to the possibility of sample-to-sample contamination . Such concerns are especially valid when the transfer of the technology to endemic countries is a priority . Deployment of such assays to varied laboratory environments can lead to an increased risk of false positive and false negative results . Accordingly , highly sensitive qPCR assays require added attention to detail , and highlight the need for a renewed programmatic focus on proper training and project oversight . Equally important , appropriate quality assurance and quality control practices must be implemented , as must the use of consistent and standardized procedures and controls . Recognizing this need , options for external laboratory quality assessment are growing , and participation in assessment programs such as the Helminth External Molecular Quality Assessment Scheme offered by the Dutch Foundation for Quality Assessment in Medical Laboratories ( SKML ) should be considered whenever possible . Submitting to such external evaluations will help to ensure the accuracy of results and the inter-lab comparability of data . Although infrequently voiced , an additional concern stems from the sometimes stated belief that optimization of a diagnostic assay can theoretically lead to the development of a test that is “too sensitive” . The argument has been made that the detection of sub-cellular levels of DNA from cellular debris may result in the false attribution of a “positive” status to individuals who are not actually harboring active infection [38] . Similarly , for certain pathogens , detection of an individual microorganism may lead to diagnostic “positivity” under non-pathogenic concentrations [39–40] . However , such concerns are more relevant in the context of the clinical diagnosis of an individual patient . It is certainly true that sub-infectious levels of a pathogen may not pose a significant risk to the individual patient . Yet when used in a surveillance capacity , even sub-clinical levels of pathogen , or pathogen-derived material , are indicative of pathogen presence within the population . Oftentimes , such sub-clinical levels of pathogen may still pose a transmission risk within the community , facilitating persistence or providing an early indication of possible infection recrudescence [41–42] . As such , when used for surveillance purposes , maximizing sensitivity should always be the diagnostic goal . However , it is equally important to remember that presence of pathogen signal is not necessarily an indicator of the potential for transmission . Factors such as single sex infections and expulsion of pathogen material can result in sample positivity despite failing to pose a transmission risk . For this reason it is critical that assay results be interpreted in the context of the study environment . Should the aims of a particular study dictate that only more heavily infected samples be of interest , a Cq value cutoff could be imposed , allowing the investigators to effectively filter out “light” , potentially sub single-egg positive results without requiring changes to the testing procedure . By targeting a highly repetitive element of the germline genome , the AGR qPCR assay described here has the capacity to greatly improve the sensitivity of detection of human Ascaris infections . This improvement should aid both operational research and programmatic efforts , increasing the accuracy of diagnostic results and facilitating better-informed decision making processes . Given the vast global prevalence of human Ascaris infection , the addition of this novel assay to the list of available molecular tools is of considerable significance . | With an at-risk population in the billions , Ascaris lumbricoides is a pathogen of great global importance . In recent years , efforts to control the spread of this parasitic helminth have expanded , resulting in declining infection rates and worm burdens in some regions . While immeasurably important for global health , these declines have also served to expose the shortcomings of traditional diagnostic methods , as low-levels of pathogen generate a need for more sensitive tools , and microscopy-based techniques are proving ill-suited to the task at hand . Thankfully , improved sensitivity can be achieved through the careful selection of optimal repetitive DNA targets for PCR . However , previous attempts to identify such targets in A . lumbricoides were unsuccessful , largely due to chromosome diminution , an unusual phenomenon occurring in the Ascaridida , whereby large portions of the germline genome are reproducibly eliminated during early development , resulting in their absence in larvae or adult worms . As the stool-based molecular diagnosis of A . lumbricoides infection is primarily dependent upon the identification of egg-derived DNA , utilizing genomic DNA from adult worms for molecular target selection eliminates germline candidates and results in suboptimal target sequence choices . Recently , the publication of a pre-diminution germline genome of A . lumbricoides has provided us with an opportunity to re-evaluate target selection , facilitating the development of a novel quantitative real-time PCR assay with greatly improved sensitivity ( ~3100-fold as determined by relative Cq value ) over previously developed assays that were based on ribosomal repeat DNA sequences with lower copy numbers . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"invertebrates",
"helminths",
"animals",
"invertebrate",
"genomics",
"ascaris",
"ascaris",
"lumbricoides",
"genome",
"analysis",
"molecular",
"biology",
"techniques",
"extraction",
"techniques",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"artificial",
"gene",
"amplification",
"and",
"extension",
"bioinformatics",
"repeated",
"sequences",
"molecular",
"biology",
"animal",
"genomics",
"dna",
"extraction",
"eukaryota",
"dna",
"sequence",
"analysis",
"polymerase",
"chain",
"reaction",
"database",
"and",
"informatics",
"methods",
"genetics",
"nematoda",
"biology",
"and",
"life",
"sciences",
"genomics",
"computational",
"biology",
"organisms"
] | 2019 | Targeting a highly repeated germline DNA sequence for improved real-time PCR-based detection of Ascaris infection in human stool |
Although mitochondrial dysfunction is often accompanied by excessive reactive oxygen species ( ROS ) production , we previously showed that an increase in random somatic mtDNA mutations does not result in increased oxidative stress . Normal levels of ROS and oxidative stress could also be a result of an active compensatory mechanism such as a mild increase in proton leak . Uncoupling protein 2 ( UCP2 ) was proposed to play such a role in many physiological situations . However , we show that upregulation of UCP2 in mtDNA mutator mice is not associated with altered proton leak kinetics or ROS production , challenging the current view on the role of UCP2 in energy metabolism . Instead , our results argue that high UCP2 levels allow better utilization of fatty acid oxidation resulting in a beneficial effect on mitochondrial function in heart , postponing systemic lactic acidosis and resulting in longer lifespan in these mice . This study proposes a novel mechanism for an adaptive response to mitochondrial cardiomyopathy that links changes in metabolism to amelioration of respiratory chain deficiency and longer lifespan .
Mitochondria are organelles found in almost every eukaryotic cell . They produce the bulk of cellular energy in the form of ATP , which is required for numerous processes in the cell . Still , mitochondrial energy production comes with a cost , the generation of reactive oxygen species ( ROS ) that is linked to the development of different pathologies and is proposed to contribute to aging [1] . The mitochondrial theory of aging proposes that an age-driven accumulation of mtDNA mutations will compromise electron transport leading to an increase in ROS production [1] . We challenged this theory by showing that increased levels of random mtDNA mutations lead to the development of premature aging phenotypes in mtDNA mutator mice , without affecting ROS production or increasing oxidative stress [2] , [3] . Our result argues against a direct link between mtDNA mutations and increased ROS production , but can also point out to the consequence of an active compensatory mechanism . A mild uncoupling of oxidative phosphorylation , leading to decreased mitochondrial ATP production could be sufficient to reduce ROS generation in the cell [4] . The “uncoupling to survive” theory further suggests that mitochondrial inefficiency mediated by partial uncoupling could have a beneficial effect on aging [4] . Uncoupling proteins ( UCPs ) are proposed to have a central role in this process ( for review see [5] ) . The mitochondrial uncoupling proteins ( UCPs ) are located in the mitochondrial inner membrane where they could act as regulated protonophores . The term “uncoupling protein” was originally used for UCP1 , a brown fat specific proton carrier that dissipates the proton gradient as heat [6] . It was anticipated that UCP2 and UCP3 lead to a moderate uncoupling that is believed to modulate the ATP/ADP ratio for signalling purposes , but their precise function in normal cellular physiology is still unclear [7] . These proteins are found in much lesser amounts than UCP1 and might be involved in the proton conductance only upon activation leading to the conclusion that they are not involved in the adaptive response to cold [7] . Emerging evidence suggests that UCP2 plays a positive physiological role by regulating mitochondrial biogenesis , substrate utilization , and ROS elimination; thereby , provides neuroprotective [8] and possibly anti-aging effects [9] . Nevertheless , several studies indicated that UCP2 could have deleterious effects on cellular function , like in the development of insulin resistance and the pathogenesis of type 2 diabetes mellitus [10] . In this study we detected increased amounts of UCP2 in multiple tissues of mtDNA mutator mice . We show that UCP2 has a protective role against molecular changes leading to a progressive cardiomyopathy and complete loss of this protein leads to further shortening of the lifespan in mtDNA mutator mice . However , UCP2 exercises its beneficial effect without affecting ROS production or modulating proton leak kinetics in heart . Instead , we provide evidence for a novel mechanism by which increased UCP2 levels modulate cardiac metabolism to maintain proficient energy production , challenged by progressive respiratory deficiency .
One of the first symptoms of premature aging in mtDNA mutator mice is cessation in weight gain around 18–25 weeks of age , followed by a progressive loss of body mass with increasing age [2] . Indeed , we detected a significant difference in the body weight in male and female mtDNA mutator mice already at 18 weeks of age that was even more pronounced at 25–27 weeks of age ( Figure 1A ) . Both individually and group-housed mtDNA mutator mice maintained on a regular chow diet showed normal daily food intake at different time points that included measuring food consumption over periods of several weeks ( 2–3 weeks ) ( Figure 1B ) . Although we cannot completely exclude the possibility that mtDNA mutator mice had decreased food intake at some point between these measurements , we believe this to be unlikely . During our early screening for changes in gene expression , we noticed that the expression of Ucp2 is increased in some tissues of mtDNA mutator mice , most prominently in heart ( Figure 1C and 1D ) . The increase in Ucp2 mRNA levels was mirrored by an increase in the UCP2 , but not UCP3 , protein levels in heart and spleen ( Figure 1E ) . Interestingly , we failed to observe an increase in Ucp2 transcript levels in spleen by quantitative real-time PCR , although we observed clear upregulation of protein levels ( Figure 1D and 1E , respectively ) . This could be the consequence of increased UCP2 stability in spleen . Inefficient energy use caused by the uncoupling of mitochondrial respiration could lead to the observed imbalance between food intake and energy expenditure in mtDNA mutator mice . An increased energy expenditure and lower body mass were observed in mice overexpressing human UCP3 protein specifically in skeletal muscle [11] . In order to assess whether the UCP2 upregulation leads to an increased proton leak , we measured proton motive force ( Δp ) in both liver and spleen mitochondria ( Figure 1F ) . These two tissues represent the real opposites when it comes to the UCP2 levels: while splenocytes contain the highest level of UCP2 , the transcripts detected in liver preps originate from the Kupffer cells , which are resident liver macrophages , as it was shown that hepatocytes do not express UCP2 [12] . Despite the upregulation of UCP2 levels in mtDNA mutator mitochondria isolated from spleen , we could not observe a difference in the Δp between the two genotypes indicating that UCP2 does not contribute significantly to the proton leak in these conditions ( Figure 1F ) . We also show that mitochondrial matricial volume , on which Δp is dependent , was not significantly changed ( Figure 1F ) . Our results demonstrate that liver mitochondria have a higher proton motive force than spleen mitochondria ( Figure 1F ) , in agreement with a previous report showing that spleen mitochondria have the highest and liver the lowest proton leak [13] . To further address the specific role of UCP2 in mitochondrial dysfunction caused by an increased mtDNA mutation load , we have crossed mtDNA mutator mice with UCP2-deficient mice ( KO ) , producing PolgAmut/mut; Ucp2−/− double mutant mice ( hereafter DM mice ) . The mean lifespan of mtDNA mutator mice was on average 14% shorter in the absence of UCP2 ( DM – 39 . 1±4 . 4 weeks v . mtDNA mutator 46 . 4±5 . 3 weeks ) , indicating that UCP2 overexpression provides some form of beneficial adaptive change ( Figure 1G ) . It was previously shown that UCP2-deficient mice have a significantly shorter lifespan than wild type controls [14] , therefore , at this point it is difficult to distinguish if the shorter lifespan reflects a specific interaction or just an additive effect of UCP2 deficiency in mtDNA mutator mice . Recent advancements in the understanding of cellular glucose and lipid metabolism identified UCP2 as a critical regulator of cellular fuel utilization and whole body glucose and lipid metabolism ( for review see [15] ) . Therefore , we investigated the impact of UCP2 depletion on energy metabolism in mtDNA mutator mice . We observed a significant reduction in body weight in both , mtDNA mutator and DM mice at 20 weeks of age ( Figure S1A – S1C ) . Interestingly , the oxygen consumption , energy expenditure and respiratory exchange ratio ( RER ) were increased only in DM mice ( Figure S1D - S1G ) . This was accompanied by a twofold decrease in blood glucose levels and a high increase in circulating lactate levels exclusively in DM mice ( Figure 2A ) . Taken together these data suggest that the loss of UCP2 promotes preferential usage of glucose as energy source in DM mice . Indeed , UCP2 has previously been shown to promote mitochondrial FAO while limiting mitochondrial catabolism of pyruvate originating from glucose [16] . In agreement with this , mtDNA mutator mice failed to increase circulating free fatty acid ( FFA ) levels at 40 weeks of age probably owing to exhaustion of lipid stores as a result of increased FAO enabled by high UCP2 upregulation ( Figure 2B ) . UCP2 was shown to be a negative regulator of glucose-stimulated insulin secretion ( GSIS ) through control of ROS production that plays an important signaling role in insulin secretion in pancreatic β-cells [10] , [17] . We detected a small but significant decrease in serum insulin levels in both mtDNA mutator and DM animals at 30 weeks of age ( Figure 2C ) . Although an initial report using mice on a mixed background suggested that UCP2-deficient mice have higher serum insulin levels [10] , it was recently shown that UCP2-deficient pancreatic β cells have reduced GSIS [17] . Our data further support this finding , as we have found normal serum insulin levels in KO mice ( Figure 2C ) . Consistent with this , glucose tolerance was similar in all different groups , although DM animals started with lower fasting glucose levels ( Figure 2D ) . Similarly , insulin sensitivity was comparable between all groups of animals ( Figure 2E ) . Hence , our data indicate that UCP2 depletion in mtDNA mutator mice does not significantly change systemic glucose metabolism . Analyses of other metabolic markers revealed that mtDNA mutator and DM mice show similar changes in the levels of different hormones involved in energy balance . We detected decreased levels of serum leptin , a central satiety agent ( Figure 2F ) , while levels of ghrelin , a hunger-stimulating hormone were upregulated in both mtDNA mutator and DM mice ( Figure 2G ) . It was previously shown that ghrelin acts by inducing UCP2-dependent changes of hypothalamic mitochondrial proliferation and respiration that are critical for the activation of different neurons involved in signaling of ghrelin-induced food intake [18] . Despite an increase in circulating ghrelin levels , we have not detected changes in UCP2 transcript levels in hypothalamus of mtDNA mutator mice ( Figure 1D ) . It is possible that chronic induction of ghrelin does not affect UCP2 expression in hypothalamus , or the observed increase in circulating ghrelin levels is too low to induce this kind of change . Therefore , we believe that the changes induced by mitochondrial dysfunction do not affect the hypothalamic circuitry and feeding behaviour differently in mtDNA mutator and DM mice . We have also found higher levels of glucagon and glucagon-like peptide 1 ( GLP-1 ) ( Figure 2H and 2I ) that increase during prolonged periods of fasting and usually show inverted relation to serum insulin levels [19] . A synergism of low insulin and relative or absolute elevation of glucagon levels is viewed as a hormonal mechanism controlling the rate of hepatic substrate extraction for gluconeogenesis [19] . Finally , we also detected decreased levels of Resistin/RETN ( Figure 2L ) , an adipose-secreted hormone linked to obesity and insulin resistance in rodents [20] . It was previously shown that the Resistin expression from adipose tissue and its serum levels are reduced in fasted mice [20] . We have not observed changes in the level of glucose-dependent insulinotropic polypeptide ( GIP ) ( Figure 2J ) , indicating normal intestinal nutrient absorption [21] , or plasminogen activator inhibitor-1 ( PAI-1 ) ( Figure 2K ) that is linked to glucose intolerance and inflammation [22] . Taken together , these data show that mitochondrial dysfunction caused by increased mtDNA mutations induces a systemic fasting-like situation that is not significantly changed by UCP2 deficiency . Puzzled by our initial observation of decreased mean and maximal lifespan in DM mice , we proceeded to look more closely into changes caused by UCP2 depletion in heart , as this was the tissue with the strongest upregulation of UCP2 levels ( Figure 1D ) and dilated cardiomyopathy was recognized as one of the most prominent changes in mtDNA mutator mice [2] . Ultrastructural analyses of DM hearts revealed the accumulation of unusually shaped mitochondria and lipid droplets accompanied by an increase in mitochondrial mass already at 25 weeks of age ( Figure 3A and 3B ) . We also observed a milder increase in mitochondrial mass in mtDNA mutator cardiomyocytes ( Figure 3B ) . Upregulation of mitochondrial mass is a common compensatory mechanism opposing decreased mitochondrial function and is detected in different animal models and patients with mitochondrial diseases [23] , [24] . Enzyme histochemical staining ( COX-SDH ) of heart sections revealed a higher incidence of mitochondrial deficiency in cardiomyocytes of DM mice at this age ( Figure 3A ) . This suggests that high levels of UCP2 protect mtDNA mutator hearts from early respiratory deficiency and postpone pathological changes to a later age [2] . In agreement with this , we observed that levels of the natriuretic peptide precursor type A ( Nppa ) , one of most commonly used marker of cardiac hypertrophy tripled in mtDNA mutator hearts after UCP2 depletion highlighting a progression of mitochondrial dysfunction in DM mice ( Figure 3C ) . Recently , it was shown that respiratory chain deficiency induces a mitochondrial stress response , marked by increased levels of FGF21 ( Fibroblast growth factor 21 ) , that directly correlate with the severity of mitochondrial dysfunction [25] . Expression of Fgf21 in heart was also shown to have an important cardioprotective role [26] . Our latest results indicate that FGF21 could act as an initial signal that senses disrupted mitochondrial proteostasis in heart and activate different stress responses independent of respiratory chain deficiency [27] . Now , we observed an upregulation of Fgf21 transcripts that was by an order of magnitude higher after UCP2 depletion in mtDNA mutator mice , signifying a more severe problem in DM hearts ( Figure 3D ) . FGF21 is a cytokine that acts through autocrine or paracrine signaling and its main sources are thought to be liver , skeletal muscle and adipocytes [28] . Therefore , we also analyzed expression levels of Fgf21 in liver and skeletal muscle . Mirroring the situation in heart , we found an upregulation of Fgf21 levels in both liver and skeletal muscle exclusively in DM animals ( Figure 3E ) . Upregulation of Fgf21 levels in skeletal muscle is also detected in mtDNA mutator mice , but not before 37–40 weeks of age [29] . These results further support our conclusion that mtDNA mutator animals are under higher stress upon UCP2 depletion . We also analysed cardiac function of mtDNA mutator mice before and after UCP2 depletion in vivo by high-resolution MRI ( magnetic resonance imaging ) in 18- to 20-week-old mice . Although we observed only mild changes in functional cardiac parameters , they were prevalent in DM mice ( Figure S2 ) . Next we characterized the respiratory chain function in hearts of DM mice because of the findings of focal cytochrome c oxidase deficiency and increased mitochondrial mass in some cardiomyocytes ( Figure 3 ) . Our results show an additional decrease in complex I and IV respiratory chain enzyme activities in DM mice ( Figure S3A ) . Even activity of complex II , the only mitochondrial respiratory chain ( MRC ) complex not affected in mtDNA mutator mice , was decreased , suggesting the existence of a general ( toxic ) effect of UCP2 deficiency on all respiratory chain complexes ( Figure S3A ) . This was accompanied by a further 12–20% decrease in the mitochondrial ATP production rates ( MAPR ) in DM heart mitochondria incubated with different substrates ( Figure S3B ) . In order to understand why the loss of UCP2 leads to higher respiratory deficiency in mtDNA mutator mice , we examined the proton leak kinetics in heart mitochondria ( Figure 4A ) . Our analysis showed that both resting oxygen consumption and membrane potential were normal in mtDNA mutator mice regardless of the presence of UCP2 ( Figure 4A ) , indicating that UCP2 or increased levels of mtDNA mutations [2] do not affect proton leak kinetics in heart mitochondria of mtDNA mutator mice . Mild uncoupling was proposed to significantly decrease ROS production in mitochondria [4] . Our initial hypothesis was that an eminent UCP2 overexpression has a protective role against a deleterious increase in ROS production . Therefore , we next determined net H2O2 production in isolated mitochondria . MtDNA mutator mitochondria , regardless of the presence or absence of UCP2 , produced significantly less ROS than the wild type , when energized by succinate , and normal ROS levels , when we used mixed substrates that allow a maximal rate of mitochondrial respiration ( Figure 4B ) . Increased reactive oxygen species ( ROS ) production often leads to compensatory upregulation of antioxidant responses in the cell . However , we found no evidence for the activation of oxidative stress responses , measured by the expression levels of different mitochondrial ROS scavenging enzymes ( Figure S4A ) and SOD2 protein levels ( Figure 4C ) in mtDNA mutator or DM mice at 25 and 40 weeks of age , consistent with our previous findings [3] . In addition , we did not detect increased oxidative stress in mtDNA mutator animals regardless of the presence of UCP2 , as shown by normal levels of protein carbonyls ( Figure S4B ) and 4-hydroxy-2-nonenal ( 4-HNE ) ( Figure 4D ) . The ROS production in intact cells was measured in mouse embryonic fibroblasts that presented a high increase in Ucp2 levels ( Figure 4E ) , as our multiple efforts to isolate adult cardiomyocytes , especially from mtDNA mutator and DM animals , failed . We found a small decrease in ROS production in mtDNA mutator MEFs and a much stronger reduction in both UCP2 KO and DM cells ( Figure 4F ) . Taken together , these results strongly indicate that UCP2 depletion in mtDNA mutator mice does not lead to an increase in ROS production or oxidative damage , and UCP2 upregulation is not a compensatory mechanism against increased oxidative stress . We next looked for an alternative role that UCP2 could play in mtDNA mutator hearts . Under normal conditions , the heart generates ATP by the consumption of energy substrates , mainly fatty acids ( roughly 70% ) with glucose and lactate contributing to the rest [30] . The rate of mitochondrial FAO is dependent on the level of fatty acids transported into mitochondria by Carnitine Palmitoyl Transferase 1 ( muscle ) - CPT1B , a mitochondrial enzyme associated with the outer mitochondrial membrane that mediates the transport of long-chain fatty acids across the membrane by binding them to carnitine . We detected a two to threefold increase in CPT1B levels in both mtDNA mutator and DM hearts pointing to an increased FFA uptake into heart mitochondria of both mutants ( Figure 5A–B ) . The levels of the insulin-regulated glucose transporter , GLUT4 , was two times lower in mtDNA mutator hearts , and was normalized upon UCP2 depletion ( Figure 5A–B ) . The levels of GLUT1 , a constitutively expressed glucose transporter , were not changed in mtDNA mutator or DM mitochondria ( Figure 5A–B ) . These results demonstrate that mitochondrial dysfunction in mtDNA mutator mice increases the FFA uptake into heart mitochondria , while decreasing glucose uptake . Simultaneously , UCP2 upregulation allows higher or more efficient fatty acid oxidation . In the case of UCP2 deficiency combined with mitochondrial dysfunction , increased fatty acid uptake results in increased lipid accumulation inside cardiomyocytes , as observed in DM mice ( Figure 3A ) . The defect in lipid handling in DM mice was further supported by changes in the expression of several genes involved in FFA transport and FAO in mitochondria like: Cpt2 ( Carnitine palmitoyl transferase 2 ) , Cact ( Carnitine-acylcarnitine translocase ) , Acot10 ( Acyl-CoA thioesterase 10 ) and Lpl ( Lipoprotein lipase ) ( Figure S5A ) . Additionally , we observed an increase in the levels of Fatp1 ( Long-chain fatty acid transport protein 1 ) and Fabp3 ( Fatty acid binding protein 3 , muscle ) , proteins that are involved in the cellular uptake of long-chain fatty acids [31] , [32] , only in DM hearts ( Figure S5A ) . We next looked at the expression of genes involved in glycolysis and found an increase in Pdk4 ( pyruvate dehydrogenase kinase , isozyme 4 ) levels in mtDNA mutator hearts that was highly augmented upon UCP2 depletion ( Figure S5B ) . PDK4 is an enzyme that inactivates the pyruvate dehydrogenase complex ( PDC ) and therefore prevents the usage of pyruvate . This is crucial for the conservation of 3-C compounds for glucose synthesis when glucose is scarce [33] and could be a consequence of the high lactic acidosis observed in DM mice ( Figure 2A ) . To dissect the mechanism by which UCP2 regulates the metabolism of mtDNA mutator mice , we examined the metabolic capacity of isolated mitochondria using different substrates in the presence of ADP ( State III ) : complex I ( glutamate/malate ) or fatty acids ( octanoyl-carnitine or palmitoyl-carnitine ) ( Figure 5C and 5D , respectively ) . We also measured State IV , which represents the oxygen consumption not linked to ATP synthesis , i . e . , respiration due to uncoupling ( Figure 5C ) . In the presence of complex I substrates , oxygen consumption rates ( OCR ) in both inducible states ( State III and maximal respiration ) were decreased in mtDNA mutator and DM mice ( Figure 5C ) . The respiratory control ratio ( RCR ) , an index of mitochondrial uncoupling , was not changed in mtDNA mutator and DM heart mitochondria indicating that mitochondrial dysfunction did not trigger a higher proton leak , regardless of the presence of UCP2 . When either medium ( octanoyl-carnitine ) or long chain fatty acids ( palmitoyl-carnitine ) were used as substrates , OCR was higher in mtDNA mutator mitochondria ( Figure 5D ) . In agreement with these results , we observed decreased maximal oxidation rates in DM animals , when a mixture of glutamate plus either fatty acid was provided as a substrate for energy production ( Figure 5E ) . In contrast , mtDNA mutator mitochondria had unchanged maximal OCR ( Figure 5E ) , suggesting that , by increasing mitochondrial fatty acid oxidation , these animals can sustain normal energy production for prolonged periods and therefore postpone the onset of mitochondrial cardiomyopathy .
We describe a novel role of UCP2 in protection against early pathological changes in cardiomyocytes induced by respiratory deficiency due to increased mtDNA mutation load . Our results argue that this protective mechanism does not rely on the uncoupling activity of UCP2 leading to decreased ROS production in heart mitochondria . Instead , we show that UCP2 promotes fatty acid oxidation in heart , while also protecting from lactic acidosis resulting from systemic respiratory deficiency . This has an overall beneficial effect resulting in prolonged survival , delayed signs of mitochondrial cardiomyopathy and longer lifespan of mtDNA mutator mice . Whether UCP2 plays a role in proton transport is still a matter of controversy . The increase in mitochondrial membrane potential in both macrophages and pancreatic β cells of UCP2 deficient mice is consistent with the proposed uncoupling activity [10] , [34] . Besides , several lines of evidence showed that UCP2 decreases ROS production by lowering the membrane potential and therefore reducing reverse electron transfer into complex I [10] , [34] . There are also strong arguments against the role of UCP2 in proton conductance . Unlike UCP1-deficient mice , UCP2 KO mice are resistant to cold exposure and they are not prone to obesity , even when fed a high fat diet , arguing against a role of UCP2 in energy expenditure [16] . Furthermore , depletion of UCP2 in tissues such as spleen or lung that express high levels of the protein does not change the uncoupling state of these cells [13] . In addition , a switch from fatty acid oxidation to glucose metabolism was demonstrated in UCP2-deficient mouse embryonic fibroblasts [35] , in agreement with our results . Until very recently no UCP2 substrates were known and it was even proposed that UCP2 , like UCP3 , act as an outward transporter of long-chain fatty acid anions from the mitochondrial matrix in situations where the fatty acid delivery to mitochondria exceeds the oxidative capacity [36] . However , a recent study showed that UCP2 acts as a metabolite transporter that regulates substrate oxidation in mitochondria [37] . UCP2 seems to be involved in both glucose and glutamine metabolism by catalyzing the exchange of malate , oxaloacetate , aspartate and malonate for phosphate plus a proton from opposite sides of the membrane [37] . The higher levels of citric acid cycle intermediates found in the mitochondria of cells where Ucp2 is silenced , indicate that , by exporting C4 compounds out of mitochondria , UCP2 limits the oxidation of acetyl-CoA–producing substrates such as glucose and enhances glutaminolysis [37] Thus , UCP2 activity decreases the contribution of glucose to mitochondrial oxidative metabolism and promotes oxidation of alternative substrates such as glutamine and fatty acids [37] . We believe that UCP2 plays a role in stimulating lipid metabolism in mtDNA mutator cardiomyocytes . In combination with the observed general fasting-like phenotype that should promote increased lipolysis and availability of circulating fatty acids , this would allow better utilization of fatty acid oxidation while maintaining the energy-balance in conditions of moderate respiratory deficiency . As a result , high energy demanding tissues , such as heart , manage to produce enough energy , at least until fat stores are depleted . In the case of UCP2 deficiency , cells turn to glucose metabolism . However , respiratory deficiency makes the aerobic glycolysis inefficient , triggering a “vicious cycle” of metabolic events leading to diminishing efficiency of energy production , earlier development of mitochondrial cardiomyopathy and premature death . The increase in fatty acid delivery , in combination with defective fatty acid utilization , promotes lipid accumulation in the cardiomyocytes , which could additionally contribute to mitochondrial dysfunction in DM mice [38] , [39] . Indeed , neurohumoral changes in heart failure , such as high adrenergic activity , can also increase the delivery of fatty acids to the heart by increasing lipolysis in adipose tissues [40] . Studies of substrate utilization in heart failure mostly show that fatty acid utilization is substantially decreased in advanced heart failure [41] . However , in contrast to patients with idiopathic dilated cardiomyopathy ( DCM ) and ischemic heart disease ( IHD ) , patients with mitochondrial cardiomyopathy ( MIC ) have increased expression of genes involved in fatty acid metabolism , including Ucp2 , Cpt1 , Pparα and Pgc1-α [23] . It was debated that this is a maladaptive mechanism leading to the worsening of phenotypes [23] . Indeed , it may seem paradoxical that in mitochondrial cardiomyopathy , fatty acid metabolism is upregulated . However , our results argue that higher level of fatty acid oxidation is actually beneficial for the respiratory-deficient heart that cannot use aerobic glycolysis to its fullest and therefore probably fails to provide enough energy to sustain cardiac function . Lactic acidosis , as detected in DM mice , is a common symptom in patients with mitochondrial diseases and is largely postponed in mtDNA mutator mice as a consequence of UCP2 overexpression [42] . Aerobic glycolysis normally does not result in an increase in circulating lactate levels , since tissues including heart can use lactate as energy source when their respiratory chain is intact [42] . However , systemic lactic acidosis could cause further decline of mitochondrial function in tissues , as shown in Trans-mitochondrial mice carrying high levels of pathogenic mtDNA molecules [43] . Therefore , we propose that the observed further decline of cardiac mitochondrial function in DM mice is a result of both tissue autonomous ( respiratory deficiency combined with lipotoxicity ) and systemic ( lactic acidosis ) factors . Our study provides further evidence that premature aging phenotypes and mitochondrial dysfunction caused by accumulation of random mtDNA mutations arise without increased ROS production , thus strengthening our view that mechanisms other than oxidative stress play an important role in this process . However , it is still possible that aberrant ROS signalling or altered redox status might play a role in the development of different phenotypes observed in mtDNA mutator mice . This was supported by results showing that both , neuronal stem cell and hematopoietic pluripotent cell defects could be ameliorated by N-acetyl cysteine treatment ( NAC - a compound with antioxidant capacity and an effect on redox balance ) . Furthermore , although no evidence of increased oxidative damage to proteins , lipids , or nucleic acids was ever found in the tissues or cells of mtDNA mutator mice [2] , [44] their cardiomyopathy was attenuated by overexpression of mitochondrial-targeted catalase [45] . Hence , we believe that the interplay of mitochondrial dysfunction and ROS signaling is likely much more complex than we currently understand and mtDNA mutator is an invaluable model to dissect this even further . However , our results argue that UCP2 protein does not play a significant role in this process , or at least not in the highly energy demanding tissue such as heart .
UCP2-deficient mice and mtDNA mutator animals used in this study have been backcrossed for more than 20 generations to C57Bl6 background . To produce the double mutant ( DM ) , PolgAmut/mut; Ucp2−/− animals , we initially crossed mice heterozygous for the mtDNA mutator allele ( +/PolgAmut ) with mice deficient in UCP2 ( Ucp2−/− ) [2] , [34] . After a series of mating steps , we obtained UCP2-deficient mice that also carry one copy of the mtDNA mutator allele ( +/PolgAmut; Ucp2−/− ) . These were then intercrossed to obtain UCP2 KO and DM mice . Wild type and mtDNA mutator mice were generated by intercrossing animals heterozygous for the mtDNA mutator allele [2] . Genotyping for both alleles was performed as described earlier [2] , [34] . Mice were group-housed with food and water ad libitum and were maintained on a 12 h light-dark cycle . Animal protocols were in accordance with guidelines for humane treatment of animals and were reviewed and approved by the Animal Ethics Committee of the Stockholm region , Sweden and North Rhine-Westphalia , Germany . Daily food intake was calculated as the average intake of normal chow diet during 2 weeks . Mice were acclimated to the food intake settings for 5 days and were weighed every week to follow changes in body weight . Fed blood glucose and lactate concentrations were measured after tail-vein incision in 25-week-old animals , using glucose or lactate strips , which were read for absorbance in a reflectance meter ( ACCU-CHEK AVIVA and Accutrend Plus , Roche Diagnostics GmBH , Mannheim , Germany ) . Serum Non-esterified fatty acids ( NEFA ) levels were determined using an acyl-CoA oxidase based colorimetric kit ( WAKO NEFA–C; WAKO Wako Life Sciences , Inc . , USA ) . NEFA standard solutions were used for the linear regression plot and absorbency measured at 550 nm in a Paradigm plate reader ( Molecular Devices ) . Protein lysates were obtained by disrupting the tissue in lysis buffer ( 50 mM HEPES , pH 7 . 4 , 1% Triton X-100 , 0 . 1 M NaF , 10 mM Na Orthovanadat , 10 mM EDTA , 0 . 1% SDS , 50 mM NaCl , 20 mM PMSF , 1 tablet protease inhibitor ( Roche ) in a tissue homogenizer ( Precellys24 , Bertin Technologies ) . After centrifugation , supernatants , containing solubilised proteins , were used for further analysis . Mitochondria were isolated from different tissues as previously described [46] . The antibodies and dilutions used for Western blot analyses are as follows: CALNEXIN ( 1∶2000 , Calbiochem ) , Complex II 70 kDA Fp subunit ( 1∶1000 , Molecular Probes ) , CPT1B ( 1∶1000 , alpha Diagnostic ) , GLUT-1 ( 1∶1000 , Abcam ) , GLUT-4 ( 1∶1000 , Millipore ) , HSC-70 ( 1∶10000 , Santa Cruz ) , MnSOD ( 1∶1000 , Upstate Millipore ) , TOM20 ( 1∶1000 , Santa Cruz ) , UCP3 ( 1∶1000 , Abcam ) , UCP2 ( 1∶500 , [12] ) , 4-HNE ( 1∶3000 , Millipore ) . Oxyblots were performed according to the manufacturer instructions with 15–20 µg proteins ( OxyBlot Protein Oxidation Detection Kit , Millipore ) . Northern blot analyses were performed as described [47] . Measurements were performed in respiration buffer containing 120 mM Sucrose , 50 mM KCl , 20 mM Tris , 1 mM EGTA , 4 mM KH2PO4 , 2 mM MgCl2*6 H2O , 0 . 1% fatty acid free BSA , pH 7 , 2 . Matrix space was determined by using 4 . 5 mCi [3H]H2O and 0 . 45 mCi inner membrane impermeable [14C]sucrose . ΔΨ and ΔpH were determined by the distribution of [3H]TPMP+ and [3H]acetate , respectively . [3H]TPMP+ is a lipophilic cation and its binding coefficient was determined as being equal to 0 . 38 [48] . Routinely , after equilibration , mitochondria were separated from the medium by rapid centrifugation ( 12000 g , 30 s ) , then treated as described previously [49] . Isolated RNA was treated with DNAse ( DNA-free Kit , Ambion ) and subsequently reversely transcribed with the High capacity reverse transcription kit ( Applied Biosystems ) . Probes for target genes were from TaqMan Assay-on-Demand kits ( Applied Biosystems ) ( Cat , Cpt1a , Cpt2 , Fgf21 , Glut4 , Gpx , Hk1 , Lpl , Sod1 , Sod2 , Txn1 , Txn2 , Ucp2 ) . For other genes Brilliant III Ultra-Fast SYBR Green QPCR Master Mix ( Agilent Technologies ) and primers as in Table S2 were used . Samples were adjusted for total RNA content by TATA box binding protein ( Tbp ) and Hypoxanthine-guanine phosphoribosyltransferase ( Hprt ) . Enzyme histochemical analyses of succinate dehydrogenase ( SDH ) and cytochrome c oxidase ( COX ) activities were performed on 14 µm cryostat sections of fresh frozen hearts [50] . The measurement of respiratory chain enzyme complex activities was performed as previously described [51] . Levels of hormones were quantified in serums of 30-week-old mice ( diluted 1∶4 ) using Magnetic Bead Metabolic Assays ( Bio-Plex , Bio-Rad , UK ) and a Bio-Plex 200 system ( Bio-Rad , UK ) according to the manufacturer's instructions . Small pieces from the myocardium were fixed in 2% glutaraldehyde and 1% paraformaldehyde in 0 . 1 M phosphate buffer ( PB ) , pH 7 . 4 at room temperature and stored at 4°C . Specimens were rinsed in a PB and postfixed in 2% osmiumtetroxide in PB at 4°C for 2 h , dehydrated in ethanol followed by acetone and embedded in LX-112 ( Ladd , Burlington , USA ) . Ultrathin sections ( approximately 40–50 nm ) were cut by a Leica ultracut ( Leica , Wien , Austria ) . Sections were contrasted with uranyl acetate followed by lead citrate and examined in a Tecnai 10 transmission electron microscope ( Fei Company , Eindhoven , The Netherlands ) at 100 kV . Digital images were randomly taken by using a Veleta camera ( Olympus Soft Imaging Solutions , GmbH , Münster , Germany ) on myofibrils from sections of the myocardium . Mitochondria were isolated from heart tissue according to [46] and resuspended in MSE buffer ( 225 mM Mannitol , 75 mM Sucrose , 1 mM EGTA , 5 mM HEPES ) at a final concentration of 20 µg/ul . The mitochondrial membrane potential was assessed by fluorimetric detection of Rhodamine 123 fluorescence at a Shimadzu 5003PC spectrofluorimeter . Calibration curves to determine membrane potential from K+-diffusion potentials were performed with 140 µg Rhodamine 123 stained mitochondria treated with 1 µg/ml valinomycin , 2 µg/ml oligomycin and 100 nM rotenone in oxygen-saturated potassium-free buffer ( 10 mM Na H2PO4 , 60 mM NaCl , 60 mM Tris/HCl , 110 mM Mannitol , 0 . 5 mM EDTA , pH 7 . 4 ) containing 5 mM glutamate and 5 mM malate by titration with increasing KCl concentrations . Measurement of membrane potential was conducted with 140 µg Rhodamine 123 stained mitochondria in air-saturated potassium-containing buffer ( 10 mM K H2PO4 , 60 mM KCl , 60 mM Tris/HCl , 110 mM Mannitol , 0 . 5 mM EDTA , pH 7 . 4 ) supplemented with 10 mM succinate by addition of increasing concentrations of malonate . Total uncoupling was assessed by addition of 1 µM TTFB . Oxygen consumption was analyzed under the conditions described for the determination of membrane potential in air-saturated potassium-containing buffer at 30°C in an Oroboros oxygraph . The respiration measurements were performed on isolated heart mitochondria ( as previously described ) at 30°C using a high-resolution Oroboros-oxygraph in air-saturated medium consisting of 110 mM mannitol , 60 mM KCl , 5 mM MgCl2 , 10 mM KH2PO4 , 0 . 5 mM Na2EDTA , and 60 mMTris – HCl ( pH 7 . 4 ) containing either 5 mM malate , 2 mM ADP , 1 mM octanoyl carnitine or 5 mM malate , 2 mM ADP , 50 uM palmitoyl carnitine . The maximal oxidation rates in these conditions were analyzed by addition of 10 mM glutamate . The quality of mitochondria was assessed by incubation with 0 . 2 mM atractyloside . Mitochondrial oxygen consumption flux was measured as previously described [52] at 37°C using 65–125 µg of crude mitochondria diluted in 2 . 1 ml of mitochondrial respiration buffer ( 120 mM sucrose , 50 mM KCl , 20 mM Tris-HCl , 4 mM KH2PO4 , 2 mM MgCl2 , 1 mM EGTA , pH 7 . 2 ) in an Oxygraph-2 k ( OROBOROS INSTRUMENTS , Innsbruck , Austria ) . The oxygen consumption rate was measured using , either 10 mM pyruvate , 5 mM glutamate and 5 mM malate ( PGM ) or 10 mM succinate ( S ) . Oxygen consumption was assessed in the phosphorylating state with 1 mM ADP ( state 3 ) or non-phosphorylating state by adding 2 . 5 µg/ml oligomycin ( pseudo state 4 ) . In the control mitochondria , the respiratory control ratio ( RCR ) values were >8 with pyruvate/glutamate/malate . The rate of H2O2 production was determined by monitoring the oxidation of the fluorogenic indicator Amplex red ( 1 µM ) in the presence of horseradish peroxidase ( 5 U/ml ) . Fluorescence was recorded at the following wavelengths: excitation 560 nm and emission 590 nm . A standard curve was obtained by adding known amounts of H2O2 to the assay medium in the presence of the reactants . Mitochondria ( 65 µg protein/ml ) were incubated in the respiratory medium at 37°C and the H2O2 production rate was initiated by adding either 10 mM pyruvate , 5 mM glutamate and 5 mM malate ( PGM ) or 10 mM succinate ( S ) or all substrates together ( PGMS ) . Complex III inhibitor antimycin A was added ( 0 . 5 µM ) after initial reading to further induce ROS production . The H2O2 production rate was determined from the slope of a plot of the fluorogenic indicator versus time . Primary MEFs ( passage 1-3 ) were cultivated in Dulbecco's modified Eagle's medium ( DMEM ) with high glucose ( 4500 mg/L ) , 4 mM L-glutamine and 1 mM sodium pyruvate supplemented with 10% FBS and 100 units/ml penicillin , and 100 µg of streptomycin . Cells ( 80-90% confluence ) were washed by warm PBS , harvested by trypsinization and collected with complete culture medium by centrifugation ( 5 min at 200 g ) . After washing with PBS , cells were resuspended in 500 µl PBS , stained with 10 µmol/L of CM-H2DCFDA ( 5- ( and-6- ) -carboxy-2′ , 7′-dichlorodihydrofluorescein diacetate ) and incubated in a cell incubator [ ( 37°C ) , high relative humidity ( 95% ) , and controlled CO2 level ( 5% ) ] in the dark for 45 min . Propidium Iodide ( 1 µg/ml ) was added to gate the living cells and tubes were kept on ice for immediate flow cytometry analysis . A total of 25000 events were analyzed and data expressed as Median +− SEM . Magnetic resonance imaging ( MRI ) was performed using a vertical Bruker AVANCEIII 9 . 4 Tesla Wide Bore NMR spectrometer equipped with an actively shielded 57-mm gradient set and a 30-mm birdcage resonator . Mice were anesthetized with 1 . 5% isoflurane and kept at body temperature during the whole experiment . Acquisition and analysis of data were performed as described by Jacoby et al . [53] . All measurements were performed in a PhenoMaster System ( TSE systems , Bad Homburg , Germany ) , which allows measurement of metabolic performance and activity monitoring by an infrared light-beam frame . Mice were placed at room temperature ( 22°C–24°C ) in 7 . 1-l chambers of the PhenoMaster open circuit calorimetry . Mice were allowed to acclimatize in the chambers for at least 24 hr . Locomotor activity and parameters of indirect calorimetry were measured for at least 48 hr . Food and water were provided ad libitum . For the glucose tolerance test , animals were fasted for approximately 16 hours and blood glucose was measured immediately prior and 15 , 30 , 60 , and 120 minutes after the intraperitoneal injection of a glucose solution ( 2 g/kg body weight ) . Insulin tolerance tests were performed on random fed animals between 2:00 and 5:00 PM . Animals were injected with 0 . 75 U/kg body weight of human insulin ( Insuman Rapid 40 IU/ml ) into the peritoneal cavity . Blood glucose values were measured immediately before and 15 , 30 , and 60 min after the injection . Results were expressed as percentage of initial blood glucose concentration . Statistical significance for all figures was determined by Student's t-test . In addition we have analyzed all results with one-way ANOVA followed by Tukey's Multiple Comparison . Results of ANOVA analyses are presented in Table S1 . | Mitochondria produce the majority of the energy needed for numerous cell functions through oxidative phosphorylation . However , this comes with the cost in the form of potentially harmful reactive oxygen species ( ROS ) that could damage all kinds of biological macromolecules . Changes in mitochondrial membrane potential through mild uncoupling could alter ROS production in the cell ( “uncoupling to survive” ) . Mitochondrial uncoupling proteins ( UCPs ) are believed to play a central role in this process . We detected increased amounts of UCP2 in mtDNA mutator mice , a model for premature aging . Depletion of UCP2 in mtDNA mutator mice led to further shortening of the lifespan with earlier signs of mitochondrial cardiomyopathy accompanied with high systemic lactic acidosis , often used as a marker of mitochondrial diseases . Remarkably , our results demonstrate that the presence of UCP2 wields beneficial effect on respiratory deficient mitochondria without affecting ROS production or uncoupling . Instead , UCP2 protein seems to mediate a valuable upregulation of fatty acid metabolism detected in mtDNA mutator hearts . Our results provide a novel mechanism of adaptation of mitochondria to respiratory deficiency mediated by UCP2 that clearly argues against the “uncoupling to survive” theory . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"animal",
"models",
"biochemistry",
"medicine",
"and",
"health",
"sciences",
"model",
"organisms",
"biology",
"and",
"life",
"sciences",
"bioenergetics",
"mouse",
"models",
"energy-producing",
"organelles",
"research",
"and",
"analysis",
"methods",
"mitochondrial",
"diseases",
"clinical",
"genetics"
] | 2014 | Loss of UCP2 Attenuates Mitochondrial Dysfunction without Altering ROS Production and Uncoupling Activity |
Filarial nematodes cause serious and debilitating infections in human populations of tropical countries , contributing to an entrenched cycle of poverty . Only one human filarial parasite , Brugia malayi , can be maintained in rodents in the laboratory setting . It has been a widely used model organism in experiments that employ culture systems , the impact of which on the worms is unknown . Using Illumina RNA sequencing , we characterized changes in gene expression upon in vitro maintenance of adult B . malayi female worms at four time points: immediately upon removal from the host , immediately after receipt following shipment , and after 48 h and 5 days in liquid culture media . The dramatic environmental change and the 24 h time lapse between removal from the host and establishment in culture caused a globally dysregulated gene expression profile . We found a maximum of 562 differentially expressed genes based on pairwise comparison between time points . After an initial shock upon removal from the host and shipping , a few stress fingerprints remained after 48 h in culture and until the experiment was stopped . This was best illustrated by a strong and persistent up-regulation of several genes encoding cuticle collagens , as well as serpins . These findings suggest that B . malayi can be maintained in culture as a valid system for pharmacological and biological studies , at least for several days after removal from the host and adaptation to the new environment . However , genes encoding several stress indicators remained dysregulated until the experiment was stopped .
Lymphatic filariasis ( LF ) is a neglected tropical disease caused by three filarial nematodes: Wuchereria bancrofti , Brugia malayi , and Brugia timori , which are transmitted by several species of mosquitoes [1] . LF is presently endemic in 60 countries , mainly in subtropical and tropical regions of the world . It is estimated that over 120 million people are currently infected and up to 800 million people are at risk [2] . Chronic LF can lead to severe disabilities due to clinical manifestations such as chronic lymphoedema ( elephantiasis ) and hydrocoele in men , and those affected are often plagued by social stigma and adverse economic consequences [3] . In 1994 , the UNDP/World Bank/World Health Organization Special Programme for Research and Training in Tropical Diseases ( TDR ) initiated The Filarial Genome Project ( FGP ) ; B . malayi was chosen as a model organism due to the availability of all life cycle stages for the construction of cDNA libraries [4] . In 2007 , the nuclear and mitochondrial genomes of this parasite were sequenced , as well as the genome of its bacterial endosymbiont Wolbachia [5] . Access to genomic data is key to advancing our understanding of parasitic nematodes and developing new ways to control and eliminate diseases caused by them . In vitro studies are vital to the advancement of filariasis research . A weakness of in vitro culture systems for all pathogens , especially metazoans such as helminths , is that they do not accurately replicate the physiological conditions at the infection site in a host , as evidenced by the inability to maintain prolonged viability of adult stages . Hence , culture studies provide results that are of uncertain relevance for the biology of the parasite in situ . Organisms have the ability to sense and adapt to environmental changes ( short or long term ) to maintain homeostasis [6] . Alteration of gene expression plays an important role in adaptation , with extensive regulation at the transcriptional and post-transcriptional levels . Changes in environmental factors such as temperature , humidity , water , light intensity , supply of nutrients , and interactions with other organisms ( i . e . , infection or mechanical damage ) can lead to stress and altered gene expression patterns [7] . Subsequently , changes in gene expression can occur which are not usually observed in the unstressed organism . An example is the production of heat shock proteins as a specific response to elevated temperatures , and modification of basic metabolism as a non-specific response [8] . The goal of this study was to evaluate changes in gene expression over time upon in vitro maintenance of adult B . malayi female worms in culture as an index of adaptation to removal from the host . We examined the worm’s global transcriptome by Illumina sequencing technology , a method shown to be highly replicable for identifying differentially expressed genes [9] , from the time the parasites were extracted from jirds in Georgia ( USA ) , shipped to Montreal ( Canada ) , and after maintenance for up to 5 days in culture under controlled conditions . A number of in vitro drug testing studies have relied on worms shipped by the NIH-NIAID Filariasis Research Reagent Resource Center ( FR3 ) at the University of Georgia [10 , 11] with timing and conditions similar to those employed in the present work .
All animal procedures were approved by the University of Georgia Institutional Animal Care and Use Committee and complied with U . S . Department of Agriculture regulations ( USDA Assurance No . A3437-01 ) . Adult male jirds ( Meriones unguiculatus ) were injected subcutaneously with ≈400 B . malayi infective third-stage larvae ( L3 ) . After a minimum of 90 days post-infection ( ranging from 3 to 6 months ) , jirds were euthanized by exposure to CO2 and adult worms were collected from the peritoneal cavity via lavage . Using 3 jirds in total , female worms recovered upon necropsy from an individual jird were assigned to 8 groups ( 4 time points , 2 technical replicates ) of 8 worms without randomization , to assess transcriptomic variability attributable to host of origin ( Fig 1 ) . Worms selected for the first group ( T1 ) were thoroughly washed in sterile PBS and flash-frozen in liquid N2 before being shipped on dry ice to McGill University . The remaining groups of 8 were shipped overnight in separate 15 ml tubes containing RPMI-1640 ( Lonza , Walkersville MD ) and 1% gentamycin ( Gentamycin solution , 10 mg/ml , Sigma Aldrich , St . Louis , MO ) via FedEx from Georgia to Montreal . Upon arrival at McGill , two separate groups of 8 worms from each jird were washed 3 times in sterile PBS and used for RNA extraction ( T2 ) . The remaining groups were incubated 1 worm per well of a 12-well plate ( Costar ) containing 6 ml RPMI 1640 ( Sigma-Aldrich , St . Louis MO ) supplemented with 10% v/v heat-inactivated fetal bovine serum ( Sigma-Aldrich , F1051 ) , 5% penicillin/streptomycin ( Sigma–Aldrich ) and 2% gentamycin ( Gibco , 15750–060 ) for either 48 h ( T3 ) or 5 days ( T4 ) at 37°C in 5% CO2 . On a daily basis , 3 ml of culture medium in each well was replaced with fresh medium . Surfaces were washed with RNase AWAY ( Molecular BioProducts ) and all dilutions were prepared with UltraPure distilled water ( Invitrogen , Life Technologies , Burlington , ON ) . Washed live worms were pooled in 1 . 5-ml tubes; immotile worms were excluded . RNA was extracted from 5 to 8 worms per group . For both live and already frozen worms ( group T1 ) , 125 μl 0 . 1X nuclease-free Tris-EDTA ( TE ) buffer , pH 8 . 0 ( Ambion , Life Technologies , Burlington , ON ) and 375 μl Trizol LS reagent ( Ambion ) were added to each tube on ice . Three cycles of flash-freezing in liquid N2 and crushing of worms in TE/Trizol LS with plastic pestles were performed to obtain homogeneous worm extracts . One hundred ( 100 ) μl chloroform was added to each tube and the samples were vortexed and incubated 3 min at room temperature . The mixtures were transferred to phase-lock gel heavy tubes ( 5 PRIME , Gaithersburg , MD ) and centrifuged at 11 , 900 x g at 4°C for 15 min . The aqueous phase was transferred to fresh tubes and mixed with 250 μl ice-cold isopropanol . Tubes were centrifuged at 12 , 200 x g at 4°C for 30 min and left overnight at -20°C for RNA precipitation . Supernatants were discarded , pellets were washed twice with 80% EtOH and allowed to dry for several hours before resuspension in 50 μl 0 . 1X TE . A 10 min incubation at 55°C solubilized the pellet . Total RNA was purified and concentrated using an RNeasy Min-Elute Cleanup Kit ( Qiagen , Valencia , CA ) . Samples were treated with DNase to remove contaminating DNA using an Ambion DNA-free Kit ( Life Technologies , AM1906 , Burlington , ON ) . The concentration and quality of the RNA for each sample was assessed by spectrophotometry ( NanoDrop 1000 , Wilmington , DE ) . RNA samples were shipped overnight on dry ice to the NIH-FR3 ( Molecular Division ) at Smith College ( Northampton , MA ) for cDNA library preparation and Illumina sequencing . RNA concentration was further verified using the Qubit RNA BR Assay Kit ( Life Technologies , Q10210 , Burlington , ON ) and the integrity and purity was assessed on an Agilent 2100 Bioanalyzer ( Santa Clara , CA ) . Messenger RNA ( mRNA ) was enriched with a NEBNext Poly ( A ) mRNA Magnetic Isolation Module ( NEB , E7490 , Ipswich , MA ) . Using the enriched mRNA as template , cDNA libraries were constructed using the NEBNext Ultra RNA Library Prep Kit Illumina ( NEB , E7530 , Ipswich , MA ) and NEBNext Multiplex Oligos for Illumina ( Index Primer 1–12 ) ( NEB , E7600 , Ipswich , MA ) following the manufacturer’s instructions . To verify the quality , DNA concentration and product size of the cDNA libraries , a Qubit 2 . 0 Fluorometer ( Life Technologies , Q32866 ) , Qubit dsDNA BR assay kit ( Life Technologies , Q32850 ) , High Sensitivity DNA Analysis Kit ( Agilent , 5067–4626 ) and Bioanalyzer were used . cDNA libraries were sequenced on an Illumina MiSeq Platform employing a 150 base pair single-end NGS setting . Data from MiSeq sequencing runs were uploaded and stored in BaseSpace ( https://basespace . illumina . com ) for data analysis .
The total number of transcripts identified in each sample and the number of sequence reads for each cDNA library are shown in Table 1 . An average of 76 . 65% of the total number of high quality sequence reads were mapped to the B . malayi transcriptome after elimination of ambiguous sequence matches . Alignment summary metrics are shown in Table 1 . The number of sequence reads mapped per gene varies from one to > 49 , 000 . Between 93 . 94% and 95 . 40% of all sequence reads in each library mapped to a transcript . We used gene expression levels from the transcriptome of worms immediately after extraction from jirds as the baseline/control and compared gene expression levels in the other three transcriptomes ( upon arrival at McGill , after 48 h in culture and after 5 days in culture ) against this baseline to identify genes with differential expression in each sample relative to time . Pairwise comparisons between time points revealed 138 to 562 differentially expressed genes after applying log2 fold-change cutoffs of +1 . 0 and -1 . 0 ( Tables 2 and S2 ) . Between 35 . 4 and 47 . 1% of differentially expressed genes could be assigned GO terms , while between 49 . 8 and 65 . 2% had a C . elegans ortholog . The highest number of differentially expressed genes was observed between T3 and T2 , followed by T4 compared to T2 . The lowest number of changes resulted from the comparison between T4 and T3 . Comparing each time point ( T2 , T3 , and T4 ) to baseline ( T1 ) , we found 30 differentially expressed genes which overlap across all three comparisons . The greatest number of differentially expressed genes in common was 99 across the comparisons T3 vs T1 with T4 vs T1 . Among 266 genes dysregulated at T2 compared to T1 , 219 returned to baseline levels at T4 ( 48 h in culture ) , representing 82% recovery of the initial perturbation . At T4 , 35 genes returned to perturbed levels , as was already the case at T2 compared to baseline . Overlaps among the 3 comparisons are displayed in Fig 2 . Genes encoding myosin tail family proteins , cadherin domains and proteins orthologous to C . elegans titin were particularly represented . Applying the cutoff ( log2 fold-change ±1 . 0; i . e . , keeping genes with log2 fold-change > 1 . 0 and < -1 . 0 ) , the proportion of differentially expressed genes represented a maximum of 4 . 8% of the number of transcripts that were mapped for each time point . Without this cutoff , up to 18 . 7% of those total transcripts were differentially expressed ( in T3 vs T2 ) . Fig 3 displays the 32 most prominently differentially expressed genes ( log2 fold-change cutoff: +/- 3 . 5 ) . GO annotations ( Nematodes . net ) show that a collagen-related protein ( Bm8439 ) is among the most profoundly up-regulated genes . Similarly , a serpin precursor ( Bm9380 ) was strongly up-regulated in several comparisons . Comparing T3 to T2 , T4 to T2 and T4 to T1 revealed the most important changes , with over 300 differentially expressed genes . Comparing T4 to T3 revealed the lowest number of differentially expressed genes . Statistically significantly enriched GO terms in the T2 vs T1 comparisons were mainly related to regulation of developmental and multicellular organism growth , whereas terms involving phagocytosis and apoptotic cell clearance were most enriched in the T4 vs T2 comparison ( S4 Table ) and terms related to the nervous system in T3 vs T1 , T3 vs T2 , and T4 vs T1 . Fig 4 shows the timewise expression changes of these genes from GO and pathway enrichment analysis . Below , we present the changes observed comparing T4 to T1 and T4 to other time points in greater detail . Among functionally annotated up-regulated genes at T4 compared to T1 , a gene encoding a cuticle collagen showed the largest change in expression ( log2 fold-change = 5 . 42 ) . A B . malayi serpin precursor ( Bm9380; log2 fold-change = 3 . 1 ) , as well as a gene annotated as Epstein–Barr virus ( EBV ) nuclear antigen 2 ( ebna-2; Bm9996; log2 fold-change = 3 . 26 ) , were among the most up-regulated at T4 compared to T1 . Bm9996 is also orthologous to a mini-collagen protein ( C1IS34 , Malo kingi ) , with 75 . 6% identity ( E-value = 14 * 10−21 ) . Genes encoding neuropeptide precursors and neuropeptide receptors were also prominently represented in the up-regulated fraction , including a FMRFamide-like neuropeptide precursor ( Bm-flp-11; log2 fold-change = 2 . 87 ) , a neuropeptide receptor ( Bm-frprp-14; log2 fold-change = 2 . 59 ) , and a corticotropin-releasing factor receptor precursor ( Bm2293; log2 fold-change = 2 . 04 ) ( S2 Table ) . A dual-specificity phosphatase ( Bm12973; log2 fold-change = 2 . 46 ) , and a neurotransmitter transporter with sodium symporter activity ( Bm-SNF-11; log2 fold-change = 2 . 90 ) were also among the genes up-regulated after 5 days in culture . GO term and pathway analysis showed enrichment of terms related to neurogenesis and nervous system development ( Table 3 ) and enrichment of the Wnt and cadherin signaling pathways ( S3 Table ) . Applying a log2 fold-change cutoff of -3 . 5 , only one functionally annotated gene had decreased in expression at the end of the experiment ( log2 fold-change = -3 . 69 ) , compared to T1 . This hypothetical protein , F53F10 . 3 ( Bm1023 ) , is annotated as a probable mitochondrial pyruvate carrier in C . elegans . The T4 vs T1 comparison resulted mainly in significantly enriched GO terms related to the nervous system ( Table 3 ) . The comparison between T4 and T3 revealed 138 differentially expressed genes . We noted significant up-regulation of Bm8519 ( log2 fold-change = 3 . 06 ) . This gene is annotated as pherophorin-dz1 in GenBank , but its function has not been characterized . The sequence contains a ground-like domain which includes a characteristic pattern of conserved cysteine residues . Fifteen collagen genes , as well as the neuropeptide precursor Bm-flp-11 , were expressed at higher levels after 5 days in culture ( T4 ) than after 2 days ( T3 ) , showing log2 fold-changes between 1 . 0 and 2 . 3 . GO term enrichment analysis between the last two time points revealed only one statistically significant enriched GO term: inductive cell migration ( GO:0040039 ) . This biological process is defined by the « migration of a cell in a multicellular organism that , having changed its location , is required to induce normal properties in one or more cells at its new location » . Twenty-five cuticle collagen genes ( log2 fold-change ± 1 . 0 ) were significantly differentially expressed . Ten were down-regulated at T2 and/or T3 compared to T1 ( Bm11024 , Bm11095 , Bm7894 , Bm9941 , Bm9021 , Bm8043 , Bm2854 , Bm4507 , Bm8444 , Bm6324 ) . Fifteen were up-regulated at T4 compared to T3 ( Bm11024 , Bm11095 , Bm2854 , Bm3144 , Bm4507 , Bm4605 , Bm6324 , Bm6324 , Bm6421 , Bm7408 , Bm7894 , Bm8043 , Bm8439 , Bm9021 , Bm9092 ) . One , Bm8439 , was strongly up-regulated over time ( log2 fold-change between 1 . 47 and 6 . 26 ) , especially between T3 and T4 compared to T1 and T2 , but no significant change in its expression was observed between T2 and T1 . With the exception of Bm8439 , Bm1249 , Bm10414 , and Bm9504 , all collagen genes showing differential expression were down-regulated at T3 and T2 compared to the earlier time points ( T2 and T1 , respectively ) , but became more up-regulated over time . Several serpins were significantly up-regulated upon arrival ( Bm1937; log2 fold-change = 1 . 87 ) and at T3 ( Bm1988; log2 fold-change = 1 . 78 ) compared to T1 . Expression levels of Bm1937 decreased by T4 , returning to levels observed at extraction from the host . A substantial increase in expression of a serpin precursor occurred over time in culture ( T3 and T4 ) ( Bm9380; log2 fold-changes between 2 . 66 and 4 . 11 ) compared to T1 and T2 . We observed a very large increase in the expression of Bm3563 at T3 and T4 ( log2 fold-change = 3 . 53 at T3 vs T2 and log2 fold-change = 3 . 88 at T4 vs T2 ) . This gene is homologous to lymphocyte antigen 75 in Ascaris suum ( 48% sequence identity , E-value = 140*10−51 ) and clec-1 ( C-type lectin ) in C . elegans ( 37 . 9% sequence identity , E-value = 5 . 2*10−27 ) . Few genes ( 0–7 ) were differentially expressed in worms retrieved from different jirds upon extraction ( S5 Table and S1 Fig ) . Pearson correlation coefficients between samples at T1 were very high ( 0 . 954 to 0 . 995; S6 Table ) . Six genes were significantly differentially expressed between worms from jird 3 and jird 2 and four genes between jirds 2 and 1 . Five of the seven genes were annotated as hypothetical . A ShTK domain-containing protein ( Bm7941 ) was down-regulated in worms extracted from jird 2 and up-regulated in worms from jird 3 , and a Ser/Thr protein phosphatase family protein partial mRNA ( Bm7394 ) was significantly up-regulated in jird 3 . A three-dimensional Principal Component Analysis graph ( S2 Fig ) shows the spatial relationship of the level of proximity between gene expression data from biological replicates from each jird . Biological Coefficients of Variation ( BCV ) [27] were calculated using the EdgeR ( V 3 . 12 . 0 ) Bioconductor package in RStudio for pairwise comparisons at T1 and showed the lowest BCV between jird 2 and 3 ( S7 Table ) . No gene was significantly differentially expressed between jird 3 and jird 1 . We performed qPCR to confirm gene expression levels measured by sequencing . We randomly chose 5 genes that were significantly up- or down-regulated at different time points compared to T1 . A robust correlation was found between Illumina RNA sequencing and qPCR data , with a correlation coefficient r = 0 . 9961 , analyzed by the Pearson test ( p<0 . 01 ) ( see Fig 5 and S1 Table ) .
We assessed the impact over time of the maintenance of adult B . malayi females in vitro at the transcriptomic level . To ensure robustness of the data , we used worms from three different hosts and processed them in duplicate groups , for a total of six samples for each time point . The strong correlation between RNA sequencing data and qPCR for 5 genes confirm the accuracy of Illumina sequencing and the suitability of the approach for comparative transcriptomic studies . The qPCR results further emphasize the rigor of the study . Shipment of worms after removal from their hosts triggered a global but transient perturbation of the mRNA profile . Several elements could have contributed to this global perturbation ( e . g . , shipment in non-supplemented culture media , varying temperatures during this process ) which cannot be separately evaluated . Up to 3 worms per group of 8 were immotile and considered dead at the end of the experiment and were excluded from the study . Hence , culturing under these conditions for so long ( 6 days after removal from hosts ) is not optimal , because of the significant loss of viability . We observed a minor loss at T3 ( 48 h in culture ) : 3/48 worms considered for extraction at T3 were immotile and excluded . Our analysis revealed up to 562 differentially expressed genes in pairwise comparisons , representing up to 4 . 84% of all genes . This number stems from the fact that we applied a fairly permissive cutoff ( log2 fold-change of ± 1 . 0 ) , and is comparable to the number of genes observed to be differentially expressed under oxidative stress in C . elegans [28] . Using a much more stringent cutoff at ± 3 . 5 revealed a maximum of 32 differentially expressed genes in pairwise comparisons . This highlights the very small proportion of strongly dysregulated gene expression over 5 days in culture . Of particular interest are genes encoding cuticle collagens and serpins , which are typically highly expressed in adult females , eggs and embryos compared to other stages [29] . The pattern of gene expression of worms obtained from 3 different jird hosts was in general very highly conserved , with Pearson correlation coefficients ranging from 0 . 954 to 0 . 995 between samples . The nematode cuticle , which is predominantly composed of cross-linked collagens , is a highly impervious barrier between the animal and its environment and is required for the maintenance of body morphology and integrity [30 , 31] . It plays a critical role in locomotion via attachments to body wall muscles [32] . Cuticle collagens are believed to be involved in stress resistance , defense against environmental perturbations and longevity in C . elegans [28] . That we observed twenty-two collagen genes to be substantially and increasingly strongly up-regulated over time in culture may indicate that culture places increasing stress on the worms . Several genes encoding other cuticle collagens were down-regulated at T2 and T3 compared to T1 , but up-regulated at T4 compared to earlier time points . The serpins are a superfamily of serine protease inhibitors that employ a suicide substrate-like mechanism [33] . They are 350–500 amino acids in length and fold into a conserved structure . Serpins have been identified in animals , plants , insects , and certain viruses [34] . At least 14 serpins are predicted in the Brugia genome , but only two have been characterized: Bma-SPN-1 and Bma-SPN-2 , which are exclusively secretory [35–37] . Previously , Bm9380 ( related to Bma-SPN-2 ) and Bm1988 were found to be highly abundant in the excretory-secretory products of microfilaria [38 , 39] . Bma-SPN-2 is exclusively expressed in microfilariae and elicits a strong but short-lived immune response in mice and humans [40] . It may play a role in protection from immunity by inhibiting neutrophil function [35 , 41] . Bma-SPN-1 , in contrast , is expressed in all life cycle stages , but little has been reported about its target protease ( s ) [35] . None of the serpin-encoding genes that were dysregulated in the present study mapped to Bm-SPN-1 . Interestingly , we found a serpin precursor ( Bm9380 ) to be increasingly up-regulated until the end of the experiment . In C . elegans , intracellular serpins regulate proteolytic pathways leading to cell death in a pro-survival manner . SRP-6 , for example , functions by blocking intestinal cell lysosomal disruption , cytoplasmic proteolysis and death induced by hypotonic shock , thermal stress , oxidative stress , hypoxia , and cation channel hyperactivity [42] . Minutes after hypotonic shock , srp-6 null worms undergo a catastrophic series of events resulting in lysosomal disruption , cytoplasmic proteolysis , and death [42] . The up-regulation of serpins Bm9380 , Bm1937 and Bm1988 upon arrival suggests a pro-survival function for these genes . Our results also showed a very large increase in the expression of a lymphocyte antigen 75/clec-1 homolog ( Bm3563 ) at 48 h ( T3 ) and 5 days ( T4 ) in culture . The C-type lectins in B . malayi have not been well characterized . However , several pathogens exploit lectin receptors to escape intracellular degradation and to suppress the generation of an efficient immune response [43 , 44] . The substantial and persistent up-regulation of Bm3563 up to 5 days in culture is in line with suggested roles in the prevention of cellular degradation . The B . malayi genome encodes 14 sequences with ground-like domains . Ground-like genes are referred to as hedge-hog ( hh ) -related genes . Bm3090 , which contains a ground-like domain , was down-regulated at T3 compared to T2 , and Bm14306 was up-regulated in T4 compared to T2 . Bm3090 is homologous to the hh-related gene grl-4 in C . elegans , which encodes a protein that is expressed in the pharynx , reproductive system , vulva , larval neurons , and larval rectal epithelium [45] . Several hh-like proteins , including grl-4 , were up-regulated in C . elegans in response to oxidative stress [28] . In B . malayi , grl-4 is highly expressed in the L3 infective larval stage and may play a role in molting [46] . One of the genes with the most marked differential expression pattern across several time points was Bm9996 . This gene is annotated as Epstein–Barr virus ( EBV ) nuclear antigen 2 ( ebna-2 ) . It is the only ebna-2 gene sequence found in Nematoda . EBNA-2 is an EBV viral transcription factor which can regulate viral and cellular genes and is associated with Burkitt’s lymphoma and Hodgkin’s disease [47] . Interestingly , EBNA-2 is capable of mimicking notch 1 and , although not related by sequence , they have similar biochemical and functional properties [48] . Notch signaling is critical for cell-to-cell communication , development and metabolism [49] . Notch signaling pathway homolog protein 1 is present in B . malayi . The ebna-2 gene in B . malayi has been shown to be preferentially expressed in L3 and L4 stages [29] . A plausible explanation of the function of EBNA-2 in the context of in vitro culture is not readily apparent . Its high degree of sequence identity to mini-collagens is interesting and suggests that it may play a structural function . Mini-collagens are small collagen-like peptides containing long stretches of polyproline and many cysteine residues and are a major component of the inner wall of nematocysts in all species of cnidarians [50] . Among other significantly dysregulated genes were several that encode zinc finger domain-containing proteins of the C2H2 type ( Bm1469 and Bm3388 ) and a DHHC zinc finger domain-containing protein ( Bm11360 ) . C2H2 zinc finger domains are the most common DNA-binding motifs in eukaryotic transcription factors and can also bind to RNA and target proteins [51] . The DHHC zinc finger domain-containing protein functions in post-translational modification by attaching palmitate via a thioester linkage mainly to cysteine residues [52] . Several dysregulated genes were annotated as hypothetical ( Bm2888 , Bm5606 ) . GO analysis of the encoded proteins revealed associations with iron binding , oxygen binding , and oxidoreductase activity; two genes which were significantly down-regulated had calcium ion binding GO terms ( Bm4715 and Bm3541 ) . Neuropeptides are well-known modulators of nematode behavior . The gene encoding the neuropeptide precursor Bm-flp-11 was overexpressed after 5 days in culture ( T4 ) , compared to all previous time points . In C . elegans , FLP-11 peptides inhibit pharyngeal activity [53 , 54] . In A . suum , most FLPs exerted an inhibitory effect on oviposition [55] . In line with the trend of neuropeptide precursors to be overexpressed over time , a gene encoding the neuropeptide receptor Bm-frpr-14 was also up-regulated at T3 and T4 compared to the two earlier time points . After 5 days in culture ( T4 ) , we saw an enrichment of GO terms primarily related to the nervous system . Three genes linked to the cadherin and Wnt signaling pathways ( Bm3384 , Bm3576 , and Bm6122 ) were strongly down-regulated at T4 vs T1 . These 3 genes are orthologs of fmi-1 , cdh-4 , and hmr-1 in C . elegans , respectively . In C . elegans , hmr-1 encodes a neuronal classic cadherin involved in regulation of axon fasciculation , with loss-of-function mutations resulting in the disruption of axonal guidance in a subset of motor neurons [56] . Cadherin FMI-1 is mainly expressed in the nervous system in C . elegans and regulates GABAergic neuronal development . Loss-of-function mutants show patterning defects in the GABAergic ventral D-type ( VD ) neurons and fmi-1 mutants show defective axon pathfinding as well as reduced synapse number , aberrant size and morphology [57] . Interestingly , cadherin-4 functions in the same pathway as FMI-1 in the regulation of GABAergic neuron development and plays a role in axon guidance [58 , 59] . As the cadherin signaling pathway also converges with the Wnt signaling pathway , it is not surprising that we see an enrichment of both [56] . The significant down-regulation of these genes at T4 is suggestive of nervous system degeneration and a decline in axon regeneration in an aging cellular environment [60 , 61] . In the T4 vs T3 comparison , the only GO term found to be significantly enriched , with 4 C . elegans orthologs ( cocg-1 , emb-9 , ccdc-55 , and let-2 ) of Bm6792 , Bm7637 , Bm2249 , and Bm3144 , respectively , was “inductive cell migration” ( GO:0040039 ) . Interestingly , these genes also have GO terms associated with embryo and larval development , suggesting a role in reproduction . COGC-1 ( orthologous to Bm6792 which was down-regulated in T4 vs T3 ) is required for normal gonadal distal tip cell migration , as well as normal vulval morphology in C . elegans [62 , 63] . Bm2249 ( also significantly down-regulated ) is orthologous to ccdc-55 which in C . elegans also plays a role in distal tip cell migration and larval development [64] . The remaining two genes ( Bm7637 and Bm3144 ) were up-regulated in this comparison and are orthologous to emb-9 and let-2 , which encode collagen alpha-1 ( IV ) chain and collagen alpha-2 ( IV ) chain , respectively . Type IV collagen is a major component of basement membranes . Mutations in emb-9 and let-2 in C . elegans cause embryonic development arrest [65] , suggesting the importance of these genes in embryogenesis . The dysregulation of these 4 genes at T4 vs T3 suggests that reproduction and embryogenesis may be affected after 5 days in culture due to events similar to aging . The enrichment of GO terms at T4 vs T2 related to apoptotic cell clearance and phagocytosis is also consistent with the hypothesis that the animal is degenerating and dying cells are being removed and may suggest that the animal is under oxidative stress , which is known to be a mediator of apoptosis and neuronal cell death [66–68] . We observed differentially expressed genes paralleling oxidative stress responses in C . elegans ( overexpression of collagens , hedgehog proteins , etc . ) . However , heat shock proteins and ATPases , strongly represented in stressed C . elegans [28] , were not prominent in cultured B . malayi females ( with 1 gene and 8 genes respectively , in our dataset ) . In addition , GO terms associated with oxidative stress in C . elegans [28] did not overlap substantially with our findings . In C . elegans , elt-2 and osm-12 are markers of osmotic stress [69] and in the present study we saw the up-regulation of Bm2533 ( orthologous to elt-2 ) upon receipt ( T2 ) , which was down-regulated after 48 h in culture , and the up-regulation of Bm7137 at 48 h which returned to baseline levels by 5 days . This gene is annotated as a hypothetical gene in B . malayi yet is orthologous to osm-12 . In hyperosmotic stress conditions in C . elegans , glycerol-3-phosphate dehydrogenase is highly up-regulated [70] . We did not detect dysregulated expression of gpdh-1 . Finally , the variability attributable to the host was only minor . The number of significantly differentially expressed genes was low ( 0–7 genes with no fold-change cut-off ) and the high values obtained from the Pearson correlation of coefficients between samples documents the similarity of sample gene sets immediately after worm extraction . The biological coefficient of variation between replicates ranged from 14 . 4 to 25 . 3% . Moreover , the jirds used to maintain B . malayi are an outbred strain , paralleling human populations . In summary , we characterized the transcriptomic effects of in vitro maintenance of adult B . malayi females after up to 5 days in culture , i . e . 6 days after isolation from a host . We suggest that environmental changes encountered after removal from the host and shipping provoke important perturbations in gene expression . We noted changes in expression levels of a few genes that are general indicators of stress , best illustrated by strong increases in expression of genes encoding cuticle collagens . We conclude that the in vitro culture system is a valuable study tool after the worms are allowed to acclimatize to a new environment and suggest that the stress of removal and shipping can be partially overcome after 48 h in culture . In vitro cultivation , however , was not free of stress for the worms , as new dysregulated genes appeared at every time point . We suggest that culture of this parasite under the conditions used here ( no feeder cell layer ) should not be extended past 6 days post removal from hosts . After that , more worms are expected to die , highlighting the need for rigorous controls . | Infections with filarial worms cause serious physical impairment and affect tens of millions of people in tropical and subtropical countries . To better understand the biology and pharmacology of these parasites , Brugia malayi is often used as a model . This parasite can be maintained in the laboratory in Mongolian jirds , enabling researchers to test drugs in vivo and in vitro , among other studies . The effects of removing worms from their hosts and culturing them may affect many aspects of their physiology , including response to drugs , but the extent to which the worms undergo changes during culture has remained unknown . Using deep RNA sequencing and bioinformatics tools , we examined the global transcriptomic profile of B . malayi females at four different time points over 5 days in culture . Focusing on genes that are differentially expressed at various time points , we observed a general perturbation of the expression profile between dissection from the host and receipt after shipment . The expression of several genes remained changed at the end of the experiment , after 5 days under controlled conditions; in particular , genes encoding cuticle collagens were prominently represented and strongly overexpressed . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2016 | The Effect of In Vitro Cultivation on the Transcriptome of Adult Brugia malayi |
Mosquitoes are natural vectors for many etiologic agents of human viral diseases . Mosquito-borne flaviviruses can persistently infect the mosquito central nervous system without causing dramatic pathology or influencing the mosquito behavior and lifespan . The mechanism by which the mosquito nervous system resists flaviviral infection is still largely unknown . Here we report that an Aedes aegypti homologue of the neural factor Hikaru genki ( AaHig ) efficiently restricts flavivirus infection of the central nervous system . AaHig was predominantly expressed in the mosquito nervous system and localized to the plasma membrane of neural cells . Functional blockade of AaHig enhanced Dengue virus ( DENV ) and Japanese encephalitis virus ( JEV ) , but not Sindbis virus ( SINV ) , replication in mosquito heads and consequently caused neural apoptosis and a dramatic reduction in the mosquito lifespan . Consistently , delivery of recombinant AaHig to mosquitoes reduced viral infection . Furthermore , the membrane-localized AaHig directly interfaced with a highly conserved motif in the surface envelope proteins of DENV and JEV , and consequently interrupted endocytic viral entry into mosquito cells . Loss of either plasma membrane targeting or virion-binding ability rendered AaHig nonfunctional . Interestingly , Culex pipien pallens Hig also demonstrated a prominent anti-flavivirus activity , suggesting a functionally conserved function for Hig . Our results demonstrate that an evolutionarily conserved antiviral mechanism prevents lethal flaviviral infection of the central nervous system in mosquitoes , and thus may facilitate flaviviral transmission in nature .
Mosquitoes transmit many human pathogens of medical importance throughout the world . Flaviviruses , such as West Nile ( WNV ) , Japanese Encephalitis ( JEV ) , Dengue ( DENV ) and Yellow Fever ( YFV ) viruses that are transmitted by mosquitoes are the etiologic agents of human hemorrhagic fever , encephalitis and meningitis [1] . As natural vectors , mosquitoes are very permissive to and allow systematic and persistent flavivirus infection [2 , 3] . For example , WNV infection is persistent in many tissues of mosquitoes , including the nervous system , salivary glands , midgut , and fat body [4] . The head of mosquitoes , where the central neural system locates , can maintain productive flavivirus infection [4] . Unlike human infection , which can cause severe neurological sequelae , flaviviral infection of the mosquito nervous system intriguingly does not lead to significant malignant pathological consequences , and also does not dramatically influence mosquito behavior or lifespan [5 , 6 , 7 , 8] . The ability of the neural antiviral mechanisms to control viral replication and to maintain a normal mosquito lifespan may facilitate viral dissemination in nature . However , the machinery that controls flavivirus infection of the mosquito nervous system is still largely unknown . Hikaru genki ( Hig ) is predominantly expressed in the pupal and adult nervous system of Drosophila and is crucial for the development of neural circuits [9 , 10] . The Hig gene encodes multiple immune-related domains , including an immunoglobulin ( Ig ) domain and five complement control protein ( CCP ) domains ( also designated Sushi repeat domains ) [9] . The Hig protein is therefore speculated to be an immune factor in Drosophila , in addition to its role in neural development [9] . The CCP domain is a signature module present in many mammalian complement proteins and is shown to mediate protein-protein interactions of complement components or to recognize microbial pathogens [11] . Human complement receptor 2 , encoding 16 CCPs , serves as a cellular entry receptor for Epstein-Barr virus , and its CCP-1 and -2 domains are required for Epstein-Barr virus binding [12] . The membrane cofactor protein ( MCP ) , with 4 CCP repeats , has been demonstrated to function as a cellular receptor for Measles virus [13] . Another complement regulator , Factor H , encoding 20 CCP repeats in the protein , was reported to bind the human immunodeficiency virus ( HIV ) surface glycoproteins gp41 and gp120 [14 , 15] . Our previous study also identified that an insect-specific scavenger receptor ( SR ) with 2 CCP domains in A . aegypti , designated as AaSR-C , serves as a pattern recognition receptor to efficiently recognize DENV , and consequently recruits mosquito complement components to limit dengue replication [11] . Aedes aegypti , a member of the Culicinae subfamily , is a natural vector for Dengue and Yellow Fever viruses [1] . Several neurotropic flaviviruses , including WNV and JEV , have also been isolated in native A . aegypti or other Aedes species ( http://www . cdc . gov/westnile/transmission/ ) [16] . Because these mosquitoes are easy to cultivate and the genome has been characterized , A . aegypti is an ideal insect model for viral pathogenesis and immune studies [17] . In this study , we have identified a hig homolog gene in A . aegypti , designated as AaHig . AaHig is highly expressed in the mosquito nervous system and enriched on the plasma membrane of neural cells . AaHig recognized DENV and JEV to directly interrupt flavivirus internalization into mosquito cells , therefore limiting flaviviral amplification in the mosquito brain . Immuno-blockade of AaHig resulted in a robust viral replication in mosquito brains , increased apoptosis of neural cells , and a dramatic reduction of the mosquito lifespan after flaviviral infection , suggesting that AaHig resists flavivirus spreading in the mosquito nervous system and therefore facilitates mosquito survival in the infection . Moreover , genetic or immune depletion of Hig homologue in Culex pipien pallens also significantly increased JEV infection , indicating Hig protein is functionally conserved in mosquitoes . Our study uncovered a previously unappreciated antiviral mechanism for Hig in the mosquito nervous system , which may provide insight into the sophisticated interactions between mosquito-borne viruses and the vector's antiviral immunity .
The complement control protein ( CCP ) domain is an evolutionarily conserved module that is essential for complement function [18] by mediating the protein-protein interactions of complement components and recognition of pathogenic microorganisms [11 , 12 , 13 , 19] . Our previous work has identified a group of 10 proteins with CCP domains in A . aegypti , 6 of which play a role in the control of Dengue and Yellow Fever viruses infection of mosquitoes [11] , indicating a general antiviral role for the CCP genes of A . aegypti . We next characterized these six genes by sequence comparison . Herein , a CCP gene , AAEL004725 , was identified as a homologue of the Drosophila neural factor Hikaru genki ( DmHig ) ( Fig 1A ) . The AAEL004725-encoded protein is predicted to contain 5 CCP domains and 1 immunoglobulin ( Ig ) domain , which is identical to those of Drosophila Hig ( Fig 1B ) . We therefore designated AAEL004725 as A . aegypti Hig ( AaHig ) throughout this investigation . The genomic comparison showed that the Hig genes are comprehensively expressed in insects , which are widely distributed throughout the orders of Diptera , Coleoptera , Hymenoptera and Lepidoptera ( Fig 1C ) . However , no homolog was identified in other arthropods and vertebrates with available genomic information . The amino acid sequences of Hig proteins are evolutionarily conserved among various insect species , suggesting possible similar functions of these proteins . Drosophila Hig is a secretory protein that is specifically expressed in the nervous system , which is an essential factor for the development of fly neural circuits [9 , 10] . We therefore assessed the abundance of AaHig in different tissues of A . aegypti . AaHig was highly expressed in the mosquito head , where the central nervous system is located . A mild level of AaHig abundance was also detected in the mosquito carcass ( Fig 2A ) , which may come from the peripheral nervous system , e . g . , the neural cells in the ventral nerve cords . To further understand the distribution of the AaHig protein , we expressed and purified a full-length peptide of AaHig in Escherichia coli ( S1A Fig ) and generated a polyclonal antibody in mice . The antibody was able to recognize S2-expressed recombinant AaHig protein ( S1B Fig ) . Consistent with its mRNA expression pattern , the AaHig protein was detected specifically in mosquito heads and carcasses ( Figs 2B and S2 ) . We subsequently examined if AaHig expression is influenced by DENV infection . The infection did not change AaHig mRNA abundance in the investigated tissues at all evaluated time points ( S3A–S3D Fig ) . Unlike Drosophila Hig , that has a signal peptide [9] , AaHig lacks a signal peptide at its N-terminus by sequence prediction ( Fig 1B ) . To assess whether AaHig is secreted , we cloned the full-length AaHig ( 1-2436bp ) into a pAc5 . 1/V5-His A expression vector , and the recombinant plasmid was subsequently transfected into Drosophila S2 cells . AaHig was strongly detected in the supernatant of transfected Drosophila S2 cells by Western-blotting ( Fig 2C ) . Abundant AaHig was observed on the surface of transfected A . aegypti Aag2 cells ( Fig 2D ) , indicating that AaHig is secreted and enriches on the plasma membrane ( S4 Fig ) . To accurately measure the in situ localization of AaHig in the mosquito central nervous system , we stained the mosquito brains using an AaHig antibody . An anti-horseradish peroxidase ( anti-HRP ) antibody , used as a "pan-neuronal" label in insects to detect several epitopes on neuronal processes [20 , 21] , was selected as a positive marker for the surface staining of mosquito neural cells . AaHig was detected comprehensively in both the antennal and optic lobes of mosquito brains and was enriched on the surface of neural cells ( Fig 2E ) . To demonstrate the subcellular localization of AaHig , we collected brain tissues from female A . aegypti mosquitoes . The subcellular fractions , including the nucleus , mitochondria , cytoplasm and plasma membrane , were separated . Specific staining for AaHig was observed in the plasma membrane fraction ( S5 Fig ) , further confirming the location of AaHig on the cell membrane of mosquito brain cells . dsRNA-mediated knockdown of AaHig significantly enhanced the DENV-2 and YFV burdens compared with that found in green fluorescent protein ( GFP ) mock dsRNA-treated mosquitoes , suggesting that AaHig is an antiviral factor in mosquitoes [11] . We reproduced the effect of dsRNA-mediated AaHig silencing on DENV-2 infection of A . aegypti . The intrathoracic inoculation of AaHig dsRNA significantly decreased AaHig expression in the whole mosquito bodies and heads at both the mRNA ( S6A and S6B Fig ) and protein ( S6C Fig ) levels . Three days after gene silencing , 10 M . I . D . 50 of DENV-2 were microinjected into the mosquitoes . The viral burden was assessed in whole bodies and heads via qPCR at 3 days and 6 days post-infection . In agreement with our previous observations [11] , the knockdown of AaHig enhanced the DENV-2 burden in whole mosquitoes ( S6D Fig ) and heads ( S6E Fig ) , confirming the important antiviral activity of AaHig in mosquitoes . To rule out any off-target effects of dsRNA-mediated RNA interference and further validate the role of AaHig in flaviviral infection , we immuno-blocked the native AaHig by thoracic inoculation of an AaHig antibody for functional investigation . To assess whether the antibody is delivered to the mosquito brain via its circulation system , we inoculated the diluted murine AaHig antibody into mosquito thoraxes , and subsequently tested its distribution in mosquito brains by in situ staining . The murine AaHig antibody was clearly detected on the surface of neural cells by fluorescence labeled anti-mouse IgG ( S7 Fig ) , demonstrating the successful delivery of the AaHig antibody to the brain . Similar amount of the antibody was distributed in the brains of inoculated mosquitoes ( S8 Fig ) . Therefore , serially diluted AaHig antibody was premixed with DENV-2 for mosquito microinjection . Neutralization of AaHig significantly enhanced DENV-2 infectivity of whole mosquitoes both 3 days ( Fig 3A , i ) and 6 days ( Fig 3B , i ) post-infection . Because AaHig is dominantly expressed in the central nervous system of mosquitoes ( Fig 2E ) , we evaluated the viral burden in mosquito heads . Similarly , inoculation of AaHig antibody augmented viral replication in the mosquito heads on 3 and 6 days after infection ( Fig 3A , ii and Fig 3B , ii ) . Through the approach of viral infection by oral feeding , immuno-blockade of AaHig also significantly enhanced the DENV burden in the whole bodies ( S9A and S9B Fig ) and heads ( S9C and S9D Fig ) of A . aegypti . AaHig antibody does not bind DENV-2 surface E protein or virions , indicating that increased viral load in mosquito heads was not a result of non-specific viral recognition by AaHig antibody . Furthermore , the viral burden in the salivary glands and the midgut was not influenced by the inoculation of AaHig antibody ( S10 Fig ) , suggesting that AaHig is a neuron-specific factor that efficiently controls viral replication in the nervous system , regardless of the peripheral tissues . Japanese encephalitis virus ( JEV ) is a mosquito-borne , neurotropic flavivirus that invades the central nervous system of vertebrates . JEV infection results in severe pathological and physiological damage to the human brain , high mortality and morbidity [1] . We therefore investigated the role of AaHig in JEV infection , using A . aegypti as a model . Compared to the pre-immune IgG , AaHig antibody treatment significantly enhanced the viral burden in both the whole bodies and heads on 3 ( Fig 3C ) and 6 ( Fig 3D ) days post-infection in A . aegypti . We next determined the role of Hig homologue in JEV infection of its natural vector , Culex mosquito . The Hig homologue ( CpHig , KP780883 ) from the Culex pipiens pallens cDNA library was isolated and silenced via dsRNA thoracic inoculation in C . pipiens pallens . The expression of CpHig was significantly reduced at both the mRNA ( Fig 4A and 4B ) and protein ( Fig 4C ) levels . Three days post-dsRNA treatment , JEV was microinjected into the mosquitoes and the viral load was assessed via Taqman qPCR 3 and 6 days post-infection . Consistent with the results of JEV infection of A . aegypti , the knockdown of CpHig significantly increased the JEV load in the whole bodies ( Fig 4D and 4E ) and heads ( Fig 4F and 4G ) of C . pipiens pallens mosquitoes . Moreover , the inoculation of the AaHig antibody , which can also react with CpHig ( Fig 4C ) , significantly increased the JEV burden in the C . pipiens pallens bodies ( Fig 4H and 4J ) and heads ( Fig 4I and 4K ) , suggesting that the Hig protein is functionally conserved in mosquitoes . To test if the antiviral activity of AaHig can be extended to other mosquito-borne viruses , we chose Sindbis virus ( SINV ) , a member of the alphavirus genus . SINV was originally isolated from Culex mosquitoes , but a large number of mosquito species , including Aedes , are also able to act as vectors for SINV transmission in nature [22] . The A . aegypti-SINV system has been used extensively as a model for understanding arbovirus-mosquito interactions [23 , 24 , 25] . We therefore intrathoracically inoculated AaHig dsRNA in A . aegypti . Three days post-gene silencing , 10 M . I . D . 50 of SINV were microinjected into the mosquitoes and the viral burden was assessed in whole bodies and heads via qPCR at 3 days and 6 days post-infection . However , the silencing of AaHig did not influence the SINV burden in whole bodies ( S11A and S11B Fig ) or mosquito heads ( S11C and S11D Fig ) . Subsequent investigation demonstrated that AaHig did not interact with the SINV envelope proteins E1 , E2 or E3 ( S11E and S11F Fig ) , suggesting a flavivirus-specific antiviral role for AaHig . The mosquito lifespan is rarely decreased by persistent flavivirus infection [5 , 6 , 7 , 8] . The antiviral factors in mosquitoes are unable to eradicate viruses from mosquitoes , but can limit the viral burden to a tolerable level that does not elicit significant tissue damage . Without these antiviral mechanisms , viral replication could cause damage to the mosquito physiology and therefore decrease the mosquito lifespan [26] . Previous studies have suggested that arboviral infection leads to apoptosis in mosquito tissues . The midgut epithelial cells of Culex pipiens showed apoptosis following infection by WNV [27] . Apoptosis of the tissues of Culex quinquefasciatus was observed after WNV infection [28] . Moreover , the level of apoptosis is correlated with viral persistent infection in mosquitoes [29 , 30] . We therefore assessed apoptosis via TUNEL staining in DENV-2 infected mosquito brains with or without AaHig antibody treatment . The number of apoptotic cells in the brains of AaHig antibody/DENV-2-treated mosquitoes was greater than that in the mock controls ( Fig 5A ) . Additionally , the more severe apoptotic damage in the AaHig-depleted mosquito brains was determined by a cleaved caspase-3 antibody for detection of apoptosis in Drosophila [31] ( Fig 5B ) . We consequently assessed whether immuno-blockade of AaHig influences mosquito survival upon flavivirus infection . 10 M . I . D . 50 DENV-2 or JEV were premixed with the diluted AaHig antibody for mosquito microinjection , respectively . Pre-immune antibody with the viruses and the antibodies without viruses served as mock controls . Consistent with previous studies , DENV infection was not lethal to mosquitoes when compared to mock infection . However , the infection became lethal when AaHig function was blocked with an antibody ( Fig 5C ) . Similar results were observed with JEV infection ( Fig 5D ) . These data clearly demonstrate that AaHig is a key factor for mosquito survival during persistent flavivirus infection . The CCP domain functions as a viral recognition module [11 , 12 , 13 , 15 , 19] . We next tested if AaHig employs a similar strategy to control viral infections . The full-length AaHig protein was generated and purified in a Drosophila S2 cell expression system ( Fig 6A ) . The purified AaHig protein strongly interacted with the DENV-2 E protein in a co-IP assay ( Fig 6B ) and captured DENV-2 virions efficiently by ELISA ( Fig 6C ) . Moreover , DENV-2 virions were pulled down by ectopically expressed AaHig protein in the infected mosquito Aag2 cells ( Fig 6D ) . We next performed immunofluorescence staining of the mosquito brains to explore whether native AaHig interacts with DENV-2 particles . Co-staining of AaHig and DENV was clearly observed on the surface of mosquito neural cells ( Fig 6E ) . The interaction between AaHig and JEV was also measured by ELISA ( S12A Fig ) and co-IP ( S12B Fig ) assays . We next investigated the details of flavivirus-AaHig interaction . The length of the Dengue E protein is approximately 500 amino acids , of which the N-terminal 400 amino acids form an ectodomain . The ectodomain consists of three domains that are referred to as envelope protein domain I ( ED1 ) , ED2 , and ED3 [32] . Both ED1 ( 1-52AA; 133AA-193AA; 281-296AA ) and ED2 ( 53-132AA; 194-280AA ) are structural domains , and the linear motifs of ED1 and ED2 are interlaced . However , the ED3 domain consists of an entirely linear sequence ( 297AA-400AA ) [32] . Based on these defined domains , we first cloned and expressed two truncated peptides in Drosophila S2 cells: the ED1+ED2 ( 1-296AA ) and ED3 ( 297-400AA ) peptides ( Fig 6F ) . AaHig strongly interacted with the ED1+ED2 peptide , but no interaction was detected between AaHig and the ED3 ( Fig 6G and 6H ) peptide . To further determine the binding motifs in ED1 and ED2 , we next constructed five truncations in which the linear motifs were sequentially deleted from the ED1 and ED2 domains ( Fig 6I ) . The results indicated that the 53 AA-132 AA motif in ED2 is indispensable for the interaction with AaHig ( Fig 6I ) . We then assessed whether the 53 AA-132 AA motif is highly conserved among flaviviruses . The motif sequence shares 46%-75% identity among the E proteins of DENV , JEV and YFV . Computational structure modeling also demonstrated that the 53 AA-132 AA motif is conserved among the three flaviviruses , suggesting that a consistent binding mechanism exists between AaHig and different flaviviruses . To examine the physiological relevance of AaHig and DENV binding , the purified AaHig protein together with DENV-2 virions was microinjected into A . aegypti and the DENV loads were assessed . Inoculation of AaHig significantly impaired DENV-2 infectivity on 3 ( Fig 6J , i ) and 6 days ( Fig 6J , ii ) post-infection , validating an important antiviral role for AaHig in mosquitoes . We next evaluated the importance of the conserved functional domains of AaHig for its antiviral activity . Six deletion mutants were constructed into the pAc5 . 1/V5-His A vector and expressed in Drosophila S2 cells ( Fig 7A and 7B ) . Both co-IP and ELISA assays showed that deletion of the second C-terminal CCP module ( 680 AA-742 AA ) abrogated the interaction between the AaHig and DENV-2 E proteins , suggesting that this module is necessary for the protein-virus recognition ( Fig 7C and 7D ) . Aag2 is an A . aegypti cell lineage of embryonic origin [33] that is permissive to many mosquito-borne flaviviruses [34 , 35] , and is thus used as a model for the study of mosquito immunity and viral pathogenesis . However , Aag2 cells did not express AaHig . We then determined the impact of transient expression of AaHig on viral infectivity . The DENV burden was significantly decreased in AaHig-transfected Aag2 cells in comparison with that in pAc-GFP transfected cells ( Fig 7E ) . Notably , the truncations that retained the ability to interact with the DENV2 E protein ( AaHig-F and AaHig-Full ) all showed a significant antiviral activity , while the truncations ( AaHig-A ~ AaHig-E ) that failed to bind DENV-E lost antiviral activity ( Fig 7F ) . These results indicate that the binding capacity between AaHig and viruses is essential for the antiviral activity of AaHig in mosquito cells . Since AaHig is secreted and enriched on the plasma membrane of neural cells , we next investigate the role of membrane localization in the antiviral activity of AaHig . Six truncations ( Fig 7A ) in which the functional domains of AaHig were sequentially deleted were constructed in the pAc5 . 1/V5-His A vector and expressed in mosquito Aag2 cells . Immunofluorescence staining indicated that these AaHig truncations were localized to the surface of mosquito cells ( S13 Fig ) , suggesting that the N-terminal domain ( located in AaHig-A ) may be responsible for anchoring AaHig in the membrane . We therefore constructed a deletion mutant that lacked the N-terminal sequence ( AaHig-G , 323 AA-812 AA ) ( Fig 7G ) . AaHig-G retained the ability to bind to the DENV E protein ( Fig 7H ) ; however , AaHig-G failed to localize to the plasma membrane of mosquito cells ( S13 Fig ) . As a result , AaHig-G exhibited no antiviral activity ( Fig 7I ) , indicating that the antiviral activity of AaHig also relies on its proper association with the plasma membrane . AaHig is a resistant factor against Dengue infection in A . aegypti cells ( Fig 7E ) . To investigate whether the antiviral mechanism of AaHig is specific to mosquito cells , we assessed the role of AaHig in DENV-2 infection of A549 ( a human alveolar basal epithelial cell line ) and Vero ( an African green monkey kidney epithelial cell line ) cells . The presence of AaHig failed to reduce the DENV-2 burden in the mammalian cells ( S14A and S14B Fig ) . Because Drosophila S2 cells are susceptible to DENV infection [36] , we determined the effect of Drosophila melanogaster Hig ( DmHig ) on DEVN-2 infection of Drosophila cells . DmHig was cloned and expressed in S2 cells ( S14C Fig ) . The overexpression of DmHig significantly impaired DENV-2 infectivity in both A . aegypti Aag2 ( S14D Fig ) and Drosophila S2 ( S14E Fig ) cells , indicating that the antiviral mechanism of Hig proteins is specific to and conserved in insects . The antiviral activity of AaHig relies on proper localization to the cellular membrane ( Figs 7I and S13 ) . To determine the reason for the lack of AaHig antiviral activity in mammalian cells ( S14A and S14B Fig ) , we determined the ability of AaHig to bind to the surface of Vero cells . AaHig cannot coat on Vero cells , as indicated by an immuno-staining assay ( S15 Fig ) , suggesting that mammalian cells may not express the membrane receptor ( s ) for AaHig anchoring . These results explain why AaHig exerts no antiviral activity in mammalian cells . In the absence of adaptive immunity , mosquitoes employ sophisticated innate immune machineries to detect and limit invading viruses , including RNA interference ( RNAi ) [37 , 38] , antimicrobial peptides ( AMPs ) [11 , 39] , reactive oxygen species ( ROS ) [40] and melanization [41 , 42] . To uncover the antiviral mechanism of AaHig , we investigated the role of AaHig in the above-mentioned mechanisms . Immuno-blockade of AaHig by antibody inoculation did not alter the expression of the AMP genes , RNAi-related genes ( Ago2 and Dicer2 ) , or ROS-related genes ( Duox1 , Duox2 ) in mosquitoes ( S16 Fig ) . Overexpression of AaHig in Aag2 cells did not influence the melanization activity ( S17A and S17B Fig ) , and knockdown of AaHig was also ineffective for H2O2 release in various mosquito parts ( S17C Fig ) . These results indicate that the antiviral activity of AaHig is independent of the known innate immune pathways . AaHig is enriched on the surface of neural cells ( Fig 2E ) , and also directly interacted with the surface envelope proteins of flaviviruses . Both the viral binding and membrane anchoring were necessary for the antiviral activity of AaHig ( Fig 7F and 7I ) . We therefore hypothesized that AaHig binding to flaviviruses may directly interrupt viral entry into mosquito cells . First , we assessed whether AaHig influenced flavivirus attachment on the surface of Aag2 and C6/36 cells . When virions are added to cells at 4°C , the virions are just tethered on the cell surface , but are not internalized [43 , 44 , 45] . After five washes with cold PBS buffer , the cells were collected to measure the viral burdens by qPCR . Compared to the mock control , incubation of AaHig did not block DENV-2 attachment onto the surface of Aag2 ( Fig 8A ) and C6/36 ( Fig 8B ) cells . Consistent results were also observed for JEV attachment to Aag2 ( S18A Fig ) and C6/36 ( S18B Fig ) cells . We next investigated the role of AaHig in viral internalization . Viruses and the proteins were incubated with the cells at 28°C ( Aag2 ) or 30°C ( C6/36 ) for various times , and viral loads were quantified after a stringent wash . AaHig blocked DENV-2 ( Fig 8C and 8D ) and JEV ( S18C and S18D Fig ) entry into the mosquito Aag2 and C6/36 cells at all time points post incubation , suggesting that AaHig reduces flaviviral replication by directly interrupting viral internalization into mosquito cells . DENV enters mosquito cells through clathrin-dependent endocytosis . The viral particles are then transported to endosomes , where the viral genome is released into the cytoplasm , leading to successful infection [43] . The entry processes can be dissected into ( i ) viral recognition by cellular attachment factors/receptors , ( ii ) viral entry via endocytosis , and ( iii ) viral membrane fusion with endosomal membranes , leading to the release of the viral genome into the cytoplasm [46 , 47] . Our data demonstrated that AaHig does not interrupt DENV attachment to the mosquito cell membrane ( Fig 8A and 8B ) ; however , AaHig significantly blocks the internalization of DENV into mosquito cells ( Fig 8C and 8D ) , suggesting the AaHig inhibits viral entry . We next determined whether AaHig interferes with the endocytic transport of DENV . The AaHig protein was premixed with DENV-2 and subsequently incubated with Aag2 cells at 28°C for a time course . The same amount of BSA mixed with DENV-2 was used as a mock control . The viral particles were stained and observed using high-resolution structured illumination microscopy ( SIM ) . The GTPase Rab5 was used as a specific marker for early endosomes [43 , 48] . In the control cells , the viruses were rapidly internalized into early endosomes . In contrast , most of the viral particles in the AaHig-treated cells were retained on the cell surface ( Fig 8E ) , suggesting that the AaHig protein interferes with early viral endocytosis . To determine whether AaHig is a general inhibitor of endocytic pathway or it specifically directly retains flaviviral particles on the cell surface to prevent viral entry , we used nano-beads with a 10–100 nm diameter , which mimic the size of the viruses , to perform a particle uptake assay in mosquito Aag2 cells . AaHig exhibited no nonspecific interactions with the beads . The number of internalized beads was measured using flow cytometry . Compared to the control groups , AaHig did not reduce particle uptake ( S19 Fig ) , indicating that AaHig does not interfere with general endocytosis . AaHig doesn’t interface with the SINV envelope proteins ( S11F Fig ) . We next explored the role of AaHig in Sindbis entry of human alveolar basal epithelial A549 and mosquito Aag2 cells . However , AaHig did not influence the Sindbis burden in either type of cells in the viral entry assay ( S20 Fig ) . In summary , AaHig specifically binds to flaviviral particles , this physical interaction does not interrupt viral attachment or binding to cellular receptors but inhibits receptor-mediated endocytosis .
The central nervous system plays a predominant role in organisms associated with cognition and higher-order functions , which is key to their successful survival . Many mosquito-borne flaviviruses particularly invade the central nervous system in vertebrates , resulting in dramatic neural degeneration and damage . As natural vectors , mosquitoes are highly permissive to flaviviral infection , and yet they maintain a normal physiology and life span [5 , 6 , 7 , 8] . The mosquito’s brain , similarly to the mammalian brain , is sensitive to viral infection [4] , and thus its antiviral machinery is supposedly very efficient in limiting the viral burden to a safe level [5 , 6] . However , little is known about the neuron-specific antiviral mechanism in mosquitoes . In this study , we report that AaHig controls viral replication specifically in the nervous system by interfering with viral entry , and its activity prevents lethal flaviviral infection of mosquitoes . Both mammals and insects are equipped with sophisticated immune systems to detect and eliminate invading pathogens before they cause significant physiological damage . Viral recognition by host factors is an essential process for the antiviral response . The complement control protein ( CCP ) domain is evolutionarily conserved and interfaces with the surface of several pathogenic viruses [18] . We have identified 6 CCP genes against DENV and YFV infections of A . aegypti , in which a scavenge receptor ( AaSR-C ) with 2 CCP modules recognizes DENV and the complement component to exert potent anti-DENV activity [11] . In the current study , we found that a CCP factor named AaHig is specifically expressed in the nervous system of A . aegypti . AaHig is a secretory factor localized to the surface of neural cells . Immuno-blockade of AaHig significantly enhanced flaviviral replication in mosquito heads , and AaHig was capable of efficiently capturing flaviviral particles in vivo and in vitro , therefore directly interrupting viral entry into mosquito cells to limit viral replication . The antiviral activity of Hig homologue was also observed in JEV infection of C . pipien pallens . Both the virus-binding and membrane-targeting ability of AaHig were essential for its antiviral activity . Indeed , the direct blockade of viral entry is a common strategy against viral invasion of mammalian cells . A siRNA-mediated gene silencing screen has identified a family of interferon-inducible transmembrane ( IFITM ) proteins that restrict the late stage of the endocytic pathway of many enveloped viruses [49 , 50] . Further investigation revealed that IFITMs directly block the entry of viruses by disrupting intracellular cholesterol homeostasis [51] , restricting viral membrane fusion [52] , or interrupting the formation of viral fusion pores [53] . Meanwhile , the human collectins , such as mannose binding lectin ( MBL ) and surfactant proteins ( SPs ) , are able to directly block virus entry . The pre-incubation of serial dilutions of MBL with HIV cell-derived living particles dramatically neutralized HIV infection [54 , 55] . MBL may directly inhibit HIV entry into T cells mediated by DC-SIGN , a key attachment factor for HIV invasion , via prevention of the direct HIV/DC-SIGN interaction [56] . MBL was also reported to interact with the envelope glycoproteins of Ebola and Marburg viruses , resulting in the impairment of viral entry by blocking virus-DC-SIGN interactions [57] . SP-D binds Influenza A virus , thereby inhibiting the attachment and entry of the virus by viral aggregation [58 , 59 , 60 , 61] . Here , our investigation shows that arthropods have evolved a similar machinery to recognize and interrupt viral entry , indicating the common ancestry of the innate immune arm presents in both mammals and arthropods . Extracellular viruses are easily exposed to and destroyed by host immune effectors , such as antibodies in humans and antimicrobial peptides ( AMPs ) in insects . AMPs , regulated by NF-KappaB ( REL ) -mediated signaling , can directly electrostatically or hydrophobically associate with the viral surface components [62] , subsequently resulting in viral inactivation [11 , 63 , 64 , 65] . Release of AMPs is a potent antiviral response to protect insect cells against viral infection [11 , 40 , 63 , 66] . For intrathoracic microinjection , the wound could elevating antimicrobial peptides ( AMPs ) gene expression [67] . However , in this study , the AMPs expression did not differ between the AaHig antibody-inoculated and pre-immune antibody-inoculated mosquitoes ( S16A–S16C Fig ) . AaHig is capable of tethering flaviviral particles onto the cell membrane and blocking viral entry into mosquito cells . The virions exposed outside mosquito cells have a greater chance to be eliminated by AMPs and other antiviral effectors . Therefore , the direct blockade of flaviviral entry could be an effective strategy for virus killing in mosquitoes . In addition to killing viruses and infected cells , one goal of the antiviral systems is to protect the uninfected cells from viral invasion . RNA interference ( RNAi ) , which is deemed as the major antiviral machinery in mosquitoes , however only limits viral replication in infected cells [68 , 69] . The system cannot function in the extracellular milieu or prevent viral spread among mosquito cells . The AaHig-mediated antiviral mechanism may complement the intracellular antiviral machinery to efficiently control viral dissemination among mosquito cells . Mosquitoes serve as the principal vectors for a number of flaviviruses in nature . In mosquitoes , the sophisticated antiviral systems successfully limit viral infection to a tolerable level , which protects the tissues from pathological damage by viral infection . Mosquitoes do not die of flaviviral infection in nature and in our experimental system . However , infection becomes lethal to mosquitoes when AaHig function is compromised , suggesting a critical role for AaHig in restricting virus-caused damage . Notably , the AaHig-mediated antiviral machinery is restricted to the brain , and it is dispensable in other tissues such as the salivary glands . In this regard , AaHig may potentially promote flavivirus transmission in nature by enabling mosquito survival and maintaining their normal life span . Characterizing the special antiviral mechanisms of insects may greatly extend our understanding of the sophisticated interactions between mosquito-borne viruses and their vectors and therefore may provide novel strategies for arboviral disease control in the future .
A . aegypti ( the Rockefeller strain ) and C . pipien pallens ( the Beijing strain ) were reared in a low-temperature illuminated incubator , model 818 ( Thermo Electron Corporation , Waltham , MA ) at 26°C and 80% humidity according to standard rearing procedures [2 , 69] . Aedes albopictus C6/36 cells were grown at 30°C in Dulbecco’s modified Eagle's medium for DENV-2 ( New Guinea C strain ) , SINV ( MX10/YN87448 strain ) and JEV ( SA-14 strain ) production . A . aegypti Aag2 and Drosophila S2 cells were cultured at 28°C in Schneider’s Drosophila medium for viral infection . All media were supplemented with 10% heat-inactivated fetal bovine serum , 1% L-glutamine , and 100 U/mL each of penicillin and streptomycin . The viruses were stocked in a -80°C ultra-freezer . DENV-2 , SINV and JEV were titrated by both plaque formation assay ( pfu ) and 50% mosquito infectious dose ( M . I . D . 50 ) as described previously [11 , 70 , 71] . The transfection efficiency was largely determined by the status of the Aag2 cells . The monolayer cells without aggregation were suitable for transfection . Briefly , the Aag2 cells were seeded at 3×106 cells/ml per well in a 6-well plate . The cells formed a monolayer after 12 hrs of culture . Then , 0 . 4 μg of plasmid was premixed with Effectene ( Qiagen , Cat . No# 301425 ) according to the manufacturer’s instructions , and consequently added to the cells . After 6–18 hrs of transfection , the medium was replaced with fresh medium . The cells were cultured for the following investigation . The sequences of the Hig genes in insects were obtained from the NCBI database . The unrooted phylogenetic tree was built with the Neighbor-joining method [72] using MEGA software v . 6 . 06 based on the alignment of the sequences determined using CLUSTAL W [73] . The bootstrap consensus tree was inferred from 5000 replicates . The functional modules of AaHig and DmHig were predicted using the SMART ( http://smart . embl-heidelberg . de/smart/set_mode . cgi ? GENOMIC=1 ) and Pfam ( http://pfam . sanger . ac . uk/ ) websites . The sequence accession numbers of the Hig proteins are: Drosophila melanogaster Hig , NP_724772 . 1; Drosophila ananassae Hig , XP_001959877 . 1; Drosophila pseudoobscura Hig , XP_001361057 . 2; Drosophila mojavensis Hig , XP_002005215 . 1; Drosophila grimshawi Hig , XP_001987337 . 1; Ceratitis capitata Hig , XP_004526452 . 1; Musca domestica Hig , XP_005176426 . 1; Anopheles gambiae Hig , XP_001237862 . 2; Aedes aegypti Hig , AIS74715; Culex quinquefasciatus Hig , XP_001850783 . 1; Tribolium castaneum Hig , XP_971621 . 2; Dendroctonus ponderosae Hig , ERL89483 . 1; Nasonia vitripennis Hig , XP_001604226 . 2; Cerapachys biroi Hig , EZA58999 . 1; Camponotus floridanus Hig , EFN61193 . 1; Acromyrmex echinatior Hig , EGI66877 . 1; Solenopsis invicta Hig , EFZ22069 . 1; Megachile rotundata Hig , XP_003708514 . 1; Bombus terrestris Hig , XP_003396303 . 1; Bombus impatiens Hig , XP_003484650 . 1; Apis florea Hig , XP_003693489 . 1; Apis dorsata Hig , XP_006615530 . 1; Danaus plexippus Hig , EHJ65552 . 1; Bombyx mori Hig , NP_001108468 . 1 . A flaviviral E protein 4G2 monoclonal antibody was produced from a hybridoma cell line ( ATCC , Cat . No# D1-4G2-4-15 ) . The cleaved-caspase-3 ( Asp175 ) antibody was purchased from Cell Signaling Technology ( Cat . No# 9661 ) [31] . The anti-horseradish peroxidase ( anti-HRP ) antibody was bought from Thermo Fisher Scientific ( Cat . No# PA1-26409 ) [20 , 21] . The Drosophila Rab5 antibody was purchased from Abcam ( Cat . No# ab31261 ) , which can be used in detection of mosquito Rab5 ( S21 Fig ) . The Wheat germ agglutinin ( WGA ) conjugated with Alexa Fluor-488 was used as the plasma membrane marker ( Cat . No# W11261 , Invitrogen ) for immunofluorescence staining [74 , 75] . The antibodies for the tags were purchased from Medical & Biological Laboratory ( MBL , Japan ) and Cell Signaling Technology . For antibody generation , the AaHig gene was amplified from A . aegypti cDNA and cloned into a pET-28a ( + ) expression vector ( Novagen , Cat . No# 69864–3 ) . The cloning primers are presented in the S1 Table . The recombinant AaHig protein was expressed in the Escherichia coli BL21 DE3 strain , with the insoluble form in inclusion bodies . The protein was then resolved by 8 M urea and purified using a purification kit ( Clontech , Cat . No# 635515 ) . The polyclonal antibody was produced by 3 boosting immunizations of AaHig in B6 mice . We have described the detailed procedures for gene silencing and viral challenge in mosquitoes elsewhere [2] . Briefly , female mosquitoes were cold-anesthetized on a cold tray , and subsequently 1 μg/300 nL of double-strand RNA ( dsRNA ) was microinjected into the mosquito thoraxes . The injected mosquitoes were allowed to recover for 3 days under standard rearing condition . The mosquitoes were then thoracically microinjected again with 10 M . I . D . 50 /300 nL ( for functional investigation ) or 1000 M . I . D . 50 /300 nL viruses ( for the detection of gene expression ) for additional investigations . In the immuno-blocking assay , we premixed the serial dilutions of the AaHig antibody with 10 M . I . D . 50 DENV-2 or JEV , and consequently microinjected the mixture into mosquitoes . The inoculated mosquitoes were reared in double containers under standard condition . At 3 and 6 days post-infection , the inoculated mosquitoes were killed and the total RNA of the whole bodies or heads was isolated to assess the viral burden with qPCR . The primers for viral detection are shown in the S1 Table . The whole bodies or heads of the infected mosquitoes were homogenized in Buffer I of an RNeasy Mini Kit ( Qiagen , Cat . No# 74106 ) with a Pestle Grinder System ( Fisher Scientific , Cat . No# 03-392-106 ) . The detailed procedure of total RNA isolation is described in the RNeasy Kit manual . Complementary DNA ( cDNA ) was randomly reverse-transcribed using an iScript cDNA Synthesis Kit ( Bio-Rad , Cat . No# 1708891 ) . The viral burden was then quantified with qPCR . The primers and probes are shown in the S1 Table . The amount of virus was normalized with A . aegypti actin ( AAEL011197 ) . For detection in Aag2 cells , the total RNA of cultured cells was isolated for reverse transcription into cDNA . The viral burden was then measured with qPCR with the described primers and probes in the S1 Table . The gene of AaHig was amplified and inserted into the pMT/BiP/V5-His A vector ( Invitrogen ) , and then the recombinant plasmids were transfected into Drosophila S2 cells in combination with a Hygromycin selection vector pCoHygro for stable cell construction . The primers for PCR and gene cloning are shown in the S1 Table . The stable cell screening and purification were described in our previous study [2 , 3] . Briefly , the transfected cells were selected using 300 μg/mL Hygromycin-B ( Invitrogen , Cat . No# 10687–010 ) for 4 weeks . The resistant cells were grown in spinner flasks , switched to Express Five serum-free medium ( GIBCO , Invitrogen , Cat . No# 10486025 ) for 3 days , and induced with copper sulfate at a final concentration of 500 μM for 4 days . The culture medium was collected for protein purification with a metal affinity resin ( Clontech , Cat . No# PT1320-1 ) . The protein was eluted with 150 mM imidazole , extensively dialyzed against PBS ( pH 7 . 8 ) , and concentrated via centrifugal filtration through a 5-kDa filter ( Millipore Corp . , Cat . No# pLCC07610 ) . The protein concentration was measured using a Protein Assay Dye ( Bio-Rad , Cat . No#500–0006 ) and a Nanodrop 2000c spectrophotometer ( Thermo Scientific ) . The protein purity was checked with sodium dodecyl sulfate polyacrylamide gel electrophoresis , and the specificity of purification was confirmed by western blotting . For the assay with purified proteins , five micrograms each of purified DENV-2 E and AaHig were incubated at 4°C for 2 hrs . Subsequently , 1 μg of baited antibody was added to pull down the protein complex . For the experiment with cell supernatant/lysate , the recombinant plasmids were transiently transfected into Drosophila S2 cells with an Effectene transfection kit ( Qiagen , Cat . No# 301425 ) . The cell supernatant/lysate was incubated with purified protein overnight at 4°C for the pull-down assay . To investigate the interaction between AaHig and DENV virions in the infected cells , pAc-AaHig was transfected in mosquito Aag2 cells , and subsequently the cells were infected by 5 M . O . I . DENV-2 at 12 hrs post transfection . The uninfected cells transfected by pAc-AaHig were used as a mock control . After 48 hrs infection , the cells were lysated and an anti-flaviviral E 4G2 mAb was added into the lysate for the pull-down assay . The experimental details are described in the Pierce Classic IP kit product manual ( Thermo Scientific , Cat . No# 26146 ) . The microtiter plate ( Nunc , Roskilde , Denmark ) was coated with 2 μg purified protein overnight at 4°C . After 5 washes with PBS containing 0 . 05% Tween 20 ( PBST ) , the supernatant/lysate of transfected cells was added to each well and incubated at room temperature ( RT ) for 2 hrs . The wells were then washed 5 times with PBST . Primary antibody was added , and incubation continued at RT for 2 hrs . The wells were washed again , and 100 μL of secondary IgG-horseradish peroxidase was added . After incubation at RT for 1 hr , a commercial peroxidase substrate system was used ( Kirkegaard & Perry Laboratories , Inc . , MA , Cat . No# 50-76-11 ) , and the optical density at 450 nm was measured with an ELISA reader . The interaction between AaHig and DENV-2 virions was also measured by ELISA . In the procedure , the plate was coated with 2 μg of purified AaHig protein at 4°C overnight . After 5 washes with PBST , 2 μg of purified inactivated DENV-2 virions ( MicroBix , Canada , Cat . No# EL-22-02-001 ) in PBS was added to each well and incubated for 2 hrs at 4°C . After washing with PBST , a flavivirus E protein 4G2 mAb was added , and further incubated for 2 hrs at 4°C . The analysis followed the procedure is outlined above . We assessed the viral attachment and internalization by Aag2 and C6/36 cells . For the attachment assay , the purified AaHig protein was premixed with viruses , and the cells were consequently pre-adsorbed with the mixture at 4°C for a time course . After five washes with pre-cooled PBS buffer , the cells were collected to isolate total RNA . The copy number of the viral genome was determined by qPCR . For the entry assay , AaHig and the viruses were premixed , and then the cells were incubated with the mixture at 28°C ( Aag2 ) / 30°C ( C6/36 ) for a time course . After washing with PBS at RT , the cells were used to determine the viral burden . For the assay at 48 hrs , the cells were washed 5 times after a 1 hr incubation , then cultured at 28°C ( Aag2 ) / 30°C ( C6/36 ) for an additional 48 hrs . The cells were collected to isolate the total RNA for viral genomic detection by qPCR . The protocol of A . aegypti brain isolation and staining was described in previous studies [21 , 76] . Briefly , the mosquito heads were cut off , then fixed in 4% paraformaldehyde at 4°C for 1 week . The brains were dissected by fine forceps and probes , treated with 2% Triton X-100 for 1 hr , and then blocked by the BD Perm/Wash buffer ( BD , Cat . No# 51-2091KZ ) . The tissues were placed on sialylated slides ( PGC Scientific , USA ) . After stained by primary and secondary antibodies , the tissues were imaged using a Zeiss LSM 780 meta confocal microscope ( Carl Zeiss , Germany ) with a Multi-Track mode . The apoptosis of mosquito tissues was determined by a terminal deoxynucleotidyl transferase ( TdT ) -mediated dUTP nick end-labeling ( TUNEL ) assay . After six days post DENV-2 infection with or without AaHig antibody treatment , the mosquito heads were cut off and fixed in 4% paraformaldehyde for brain isolation . The TUNEL assay was performed by a cell death in situ detection kit , Fluorescein ( Roche , Cat . No# 11684817910 ) . A FITC filter was used to detect TUNEL staining ( green color ) . TUNEL-positive staining patterns were acquired by a Zeiss LSM 780 meta confocal microscope ( Carl Zeiss , Germany ) with a Multi-Track mode . Aag2 cells were seeded in a glass bottle cell culture dish ( Nest Biotechnology , Cat . No# 801001 ) for 12 hrs . The 2 μg purified AaHig protein was premixed with 10 M . O . I . DENV-2 in 500 μL medium , and the mixture was then added to the cells . After the cells were cultured at 28°C for a time course , the cells were washed with PBS and fixed in 4% paraformaldehyde for 1 hr . Permeabilization was performed using 0 . 05% Triton X-100 for 30 min . After three washes in PBS , the cells were blocked using the Perm/Wash buffer ( BD , Cat . No# 51-2091KZ ) . After the primary and secondary antibodies staining , the cells were imaged using a Zeiss ELYRA PS . 1 structured illumination microscope ( Carl Zeiss , Germany ) . After 48 hrs of transfection , the Aag2 cells were stimulated by 1 M . O . I . DENV-2 or JEV for 30 min . The supernatant was collected by centrifugation at 3000 rpm for 10 min at 4°C in order to remove debris . The PO activity assays were performed in 96-well plates . One hundred microliters of 50 mM Sodium Phosphate buffer ( pH 6 . 5 ) containing 2 mM dopamine ( the substrate for the PO , Sigma , Cat . No# D-9628 ) was added to 20 μL of cell culture medium . PO activity was monitored over 30 min by measuring the absorbance at 490 nm using a plate reader [77 , 78] . After 6 hrs of infection , the mosquito parts including heads , carcasses and whole bodies , were collected into the PBS buffer with 2 mg/ml of the catalase inhibitor 3-amino-1 , 2 , 4-triazole ( Sigma , Cat . No# A8056-10G ) . After homogenization , the samples were filtered through a spin filter with a 10K molecular weight cutoff ( Corning Spin-XUF , Corning , Cat . No# 431486 ) . The elution from each experimental group was then collected and tested using a hydrogen peroxide assay kit ( BioVision , Cat . No# K265-200 ) . The fluorescence intensity was detected at Excitation/Emission = 550/590 using a fluorescence microplate reader , according to the manufacturer's instructions . The value was normalized by the total amount of proteins in the sample , as determined by a Protein Assay Dye ( Bio-Rad , Cat . No# 500–0006 ) and a Nanodrop 2000c spectrophotometer ( Thermo Scientific ) . The nano-beads with a 10–100 nm diameter ( Cat . No# LB1-1ML , Sigma ) , which mimic the size of the viruses , was selected to perform a particle uptake assay in mosquito Aag2 cells . The beads were pre-labeled by Fluorescein isothiocyanat ( FITC ) . AaHig protein was premixed with the beads , then incubated the materials with Aag2 cells at 28°C for 30 min . The same amount of BSA mixed with the beads was used as the mock control . The incubated cells were washed 3 times by PBS buffer , and then treated by 0 . 2%Trypan Blue to quench the fluorescence of the beads attached on the cell surface [35 , 79] . The amount of uptake beads , which had been internalized into mosquito cells , was measured by the flow cytometry . The treated cells were then examined using a FACS Calibur flow cytometer ( BD Biosciences , San Diego , CA ) . Dead cells were excluded on the basis of forward and side light scatter . Data were analysed using FlowJo software . | The central nervous system plays a predominant role in organisms associated with cognition and higher-order functions , which is key to their normal behavior and successful survival . Many mosquito-borne flaviviruses particularly invade the central nervous system in vertebrates , resulting in dramatic neural degeneration and damage . As natural vectors , mosquitoes are highly permissive to flaviviral infection that can be persistent in the mosquito nervous system . However , the infection intriguingly does neither lead to significant malignant pathological sequelae , nor dramatically influences mosquito behavior or lifespan , and thus mosquitoes can transmit viruses efficiently . Little is known about the neuron-specific resistant mechanism in viral infection of mosquitoes . Here we report that a neuron-specific factor specifically controls flaviviral replication in the mosquito nervous system by interfering with viral entry , and its activity prevents lethal flaviviral infection of mosquitoes . Our study provides insight into the sophisticated interactions between mosquito-borne viruses and their vectors , and offers an important target for arboviral limitation in nature . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Neuron-Specific Antiviral Mechanism Prevents Lethal Flaviviral Infection of Mosquitoes |
Lymphatic filariasis ( LF ) is a so-called neglected tropical disease , currently overshadowed by higher-profile efforts to address malaria , tuberculosis , and HIV/AIDS . Despite recent successes in arresting transmission , some 40 million people who already have the disease have been largely neglected . This study aims to increase understanding of how this vulnerable , neglected group can be helped . We used purposive sampling to select 60 men and women with filarial lymphoedema ( 45 with filarial elephantiasis and 15 men with filarial hydrocoele ) from the south of Sri Lanka in 2004–2005 . Participants were selected to give a balance of men and women and poor and nonpoor , and a range of stages of the disease . Participants' experiences and the consequences of their disease for the household were explored with in-depth qualitative , semistructured interviews . LF was extremely debilitating to participants over long periods of time . The stigma attached to the condition caused social isolation and emotional distress , and delayed diagnosis and treatment , resulting in undue advancement of the disease . Free treatment services at government clinics were avoided because the participants' condition would be identifiable in public . Loss of income due to the condition was reported by all households in the sample , not just the poorest . Households that were already on low incomes were pushed into near destitution , from which it was almost impossible to escape . Affected members of low-income households also had less opportunity to obtain appropriate treatment from distant clinics , and had living and working conditions that made hygiene and compliance difficult . This highly vulnerable category of patients has low visibility , thus becoming marginalized and forgotten . With an estimated 300 , 000 total cases of elephantiasis and/or oedema in Sri Lanka , and around 300 , 000 men with filarial hydrocoele , the affected households will need help and support for many years to come . These individuals should be specially targeted for identification , outreach , and care . The global strategy for elimination is aimed at the cessation of transmission , but there will remain some 40 million individuals with clinical manifestations whose needs and problems are illustrated in this study .
Recently , the profile of the “neglected diseases” [1] , [2] has been enhanced by a renewed interest by policymakers , including the new Director-General of the World Health Organization ( WHO ) . These diseases cause long-term morbidity , rather than high mortality , but have been overshadowed by higher-profile efforts to address malaria , tuberculosis , and HIV/AIDS [2] . Recent studies show extensive and underestimated morbidity for the neglected diseases [3] , totalling around 56 million cumulative disability-adjusted life years , which is more than for malaria and tuberculosis [4] . Lymphatic filariasis ( LF ) is one of these diseases and one of the leading causes of disability , infecting some 120 million individuals , with a further 1 . 3 billion people at risk [5] . Some of the best “global health buys , ” in terms of cost per disability-adjusted life years averted , are preventive chemotherapy for the control of intestinal helminths , elimination of LF , and control of onchocerciasis ( the latter two programmes are based on drug donations ) [6] . Treatment costs of such chemotherapy packages range from US$0 . 03 to US$1 [7]–[9] , and it is recognised that cost savings by integration of NTD programmes can reach as much as 47% [10] . Economic rates of return on controlling the neglected diseases are 15%–30% [1] . The Global Programme to Eliminate Lymphatic Filariasis is arguably the most rapidly expanding global health intervention [5] . Since 2000 , when nearly 12 million people were treated , the latest WHO figures show that around 381 million people received treatment in 2005 in 42 countries [5] . There is strong evidence to suggest that the WHO strategy has eliminated the transmission by mosquitoes of the causative agent Wuchereria bancrofti in Egypt [11] , [12] , whilst in other settings , including China , the disease is reported to have been eliminated [13] or transmission arrested [14] . These successes give an incomplete picture , because some 40 million people who are deformed , stigmatised , and disabled by the disease have been largely neglected . There are , however , promising interventions that could improve the quality of life and reduce the level of disability of patients . If effective interventions are to be successfully implemented , a greater understanding is required of the consequences of the disease for individuals and their families , the barriers they face to accessing the care they need , and their coping strategies . Whilst there have been studies in a number of countries on the social and cultural aspects of LF prior to the advent of the Global Programme to Eliminate Lymphatic Filariasis [15]–[17] , we report here a recent in-depth study into the social and economic impact of filarial elephantiasis in Sri Lanka from the perspective of the people suffering from the disease themselves . The objective of the study was to inform future interventions and policy to help these vulnerable , neglected people . By doing so , it responds to needs for specific research identified in the most recent review of the sociocultural aspects of filariasis [18] .
Reference to LF in Sri Lanka has been traced to the 13th century AD [19] . Brugian filariasis , caused by Brugia malayi , was eliminated by chemotherapy and vector control through the Anti-Filariasis campaign , which began in 1947 . The infection currently endemic in the country is due to W . bancrofti , and is presently confined to eight districts in the Southern , Western , and Northwestern Provinces [20] . Our study took place in three villages in Matara and one in Galle in 2004–2005 . Further details of the geography , ecology , and social structure of the communities can be found in earlier published work [21] . For the qualitative study , systematic purposive sampling was used to select 60 participants with LF for in-depth interviews concerning their experiences and consequences of the disease . Participants were selected by poverty status , sex , and lymphoedema stage ( Table 1 ) . Thirty of the 60 participants with LF were selected from three villages: Polhena , Wagama , and Madihe in Matara District . A survey in 2003 [20] identified 117 cases of lymphoedema of varying stages , and six more were identified subsequently . Of the total 123 , 107 consented to take part in a lymphoedema management experiment . A sample of 30 was selected from the 107 cases for qualitative interviews , to include a balance of women and men , of poor/nonpoor status , and of lymphoedema stage . Poverty status was identified for the initial sample selection from the questions on occupation in the epidemiological survey , with participants in informal labour occupations categorised as poor ( the subsequent in-depth interviews provided detailed information on income to define participants' income status more directly ) . The main stages of advance of lymphoedema were graded according to the classification of Dreyer et al . [22] . Six of the initial sample of 30 were willing to participate but were unable to complete the full interviews , and so were omitted from the analysis . Six replacements were selected from the 107 cases , all of whom completed the interviews . A further 15 participants with LF were identified from a fourth village 10 km away from the villages in Matara District , Unawatuna ( Galle District ) , chosen because it had no involvement in the lymphoedema management experiment . Key informants helped identify households containing people with LF . House visits were made to the named individuals and snowballing was then used to recruit further participants for interview . A total of 47 cases of lymphoedema were identified by this process , of which 15 were selected to provide a mix of sex , poor/nonpoor status , and lymphoedema stage . All agreed to participate in interviews . A separate sample of 15 men with filarial hydrocoeles was selected from one village in Matara District where the Medical Officer of Health for the area considered hydrocoele to be prevalent . Local officials acted as key informants to help identify men with the condition . Recruitment by snowballing identified 42 cases , of whom 15 met the criteria and agreed to participate . Of the 15 men , three had undergone surgery for their condition , while 12 had not ( Table 2 ) . A team of ten trained interviewers ( four women and six men ) supervised by a senior project officer conducted guided interviews with study participants in the local language , Sinhalese . Interviewers worked in single-sex pairs: one conducting the interview , the other recording the responses manually . To respect gender sensitivities of participants , the pairs of interviewers were assigned to interview participants of the same sex . Interview notes were transcribed and later translated into English for analysis . Interpretations of the data were fed back to , and refined with , the interviewers . All the interviewers had at least 3 y of experience of conducting interviews according to the Affordability Ladder framework ( see below ) . The discussions encouraged patients to “tell their story , ” beginning with the first symptoms to the time of the interview , which covered periods ranging from 1 mo to nearly 30 y . Health-seeking behaviour , costs of access to treatment , and expenditure in the household at each stage were obtained through a historical profile of the disease and its consequences for the household economy . Although the participants recounted experiences that occurred over several years , most had vivid recall of the milestones in their illness because it had made significant marks in their lives . Each interview took 3–4 h , some being undertaken over 2 d . These would normally be considered exceptionally lengthy interviews , but this situation resulted not from the researchers' schedules but from the desire of the interviewees themselves to talk freely and at length about their experiences . Some commented that the interview itself provided a therapeutic release from long-pent-up emotions , as they had been socially isolated when their condition advanced . An adaptation of the affordability ladder framework [23] was employed to organise and analyse the data . Figure 1 [24] illustrates the basic conceptual framework . The starting point for the Affordability Ladder analysis on the left of the figure is a perceived or professionally defined health problem , a “need”; in this case , symptoms of LF . Perceived need and the consequences of that need may vary for different types of household depending on socioeconomic circumstances , and it is therefore important in the analysis to look at what happens to different groups in the population . Once the symptoms of LF are perceived by households , their experiences of seeking help for the condition may be very different in different types of households and are represented by the four main steps on the ladder: ( 1 ) No care; ( 2 ) informal care and/or self-care; ( 3 ) access to and utilisation of professional care; and ( 4 ) quality of professional care received . At each step of the ladder there are health and social consequences and a burden of payment as a result of the actions taken , as indicated by arrows . The policy environment also affects people's choices and actions in all these steps , as the arrows denote . These are not necessarily sequential steps: people may treat themselves with medicines or consult an informal provider , for example , at the same time as seeking professional help . The pattern of seeking care , however , may differ , again depending on socioeconomic differences . In using a systematic approach to examine the many different aspects of the pathways from need to appropriate care , one important aim is to identify much more closely where and why the system is working well and where it is breaking down for different groups in the population [23] . For the purposes of analysing the intricacies of participants' experiences from the qualitative data , we adapted this basic affordability ladder framework to incorporate four distinct ladders ( Figure 2 ) . The first , a reference ladder , documented the progression of the illness ( historical profile of illness ) as described above . A treatment and expenditure ladder recorded the direct and indirect costs incurred at each stage of the illness . The household economy ladder charted the changes in the household economy after a member of the household was identified as having LF and throughout the duration of the illness . Lastly , the impact ladder traced the economic and social consequences of the illness to the participant , the household and other family members . A process of constant cross-referencing each individual horizontally across ladders allowed interactions and the ordering of events and consequences to be identified . Recurring themes within and across the different types of household ( low- , middle- , and high-income ) were then identified by reviewing the entire dataset within the four-ladders framework . Emerging themes were noted , sorted , and grouped into main themes . Quotations and field notes describing interviewees' experiences with LF ( presented in Boxes 1–4 ) are used to illustrate the main themes identified in the analysis . From the income information obtained in the interviews , households were categorised as “high , ” “middle , ” or “low” income , judged against the level of incomes reported in the latest Consumer Finance Survey of the Central Bank [25] . “Low-income households” had income within the lowest three deciles range on the national scale , “high-income households” had income in the top three deciles range , and “middle-income households” had incomes that fell within the 4th to the 7th deciles range of the national scale . To minimise recall errors on expenditures , only costs incurred in the last 3 y , and costs for the most recent episode of inpatient care within the last 5 y , were used . Direct and indirect costs and costs as a proportion of the household income for participants in different types of household were calculated . Names of study participants have been changed to protect their anonymity . The ethical approval for the study was obtained from the Research Ethics Committee of the Liverpool School of Tropical Medicine and the Ethics Committee of the University of Ruhuma , Galle , Sri Lanka . Verbal ( oral ) consent was given by the participants who were invited to participate; participants were reassured that they could withdraw from all or part of the interview at any time . The investigators judged , on the basis of their experience , that written consent was not obtainable because of the community-wide mistrust of signing any official forms and the level of literacy in the population . The ethics committees accepted this constraint . Studies conducted by the Marga Institute in similar settings have used the same approach , respecting the communities' concerns .
Participants' poverty and associated way of life severely limited their ability to prevent or cope effectively with the condition at all stages of the disease and its treatment . At the infection stage , for instance , poorer participants reported having to work for long hours in contact with stagnant water , with daily exposure to mosquito breeding places . Two common occupations for poor women—making coir yarn and weaving thatch—involved soaking materials in stagnant pits . Often the women had to stand chest-high in them for hours . These pits were generally sited adjacent to homes and were breeding places for Culex quinquefasciatus , the vector of W . bancrofti . The ability of patients to adopt preventive measures in the home was also severely limited . The poor could not afford the costs involved in avoiding exposure to mosquitoes , such as mosquito netting and repellent , and did not have the types of houses that would keep out mosquitoes . When we traced participants' experiences in accessing and using the appropriate medical care for LF with the affordability ladder , multiple problems for households were revealed . Delayed diagnosis was common and had irreversible consequences . Both poor and nonpoor participants had experienced delays in diagnosis . The low-income households were more likely to report adopting home remedies for all types of illnesses , including lymphoedema . They tended to seek medical treatment only when the disease seriously affected their livelihoods , and then they tended to opt for indigenous treatment ( which would not provide an accurate diagnosis ) , citing convenience , proximity , and cost as reasons . Even low-income patients consulted private practitioners who charged a moderate fee but had no facilities to diagnose LF . These practitioners invariably treated only the immediate symptoms of fever and pain , and sometimes misdiagnosed the condition . One man , for example , had been treated for 7 y by an indigenous practitioner for the effects of “snake bite . ” The lymphoedema of one pregnant woman went untreated for 9 mo because the symptom was judged to be due to the pregnancy . Some participants reported that they chose to go to a private practitioner to avoid the social stigma brought about by exposure of their affected limbs at a government clinic , despite treatment being free at such clinics . The nonmedical costs of travel and the indirect costs in accessing a distant government hospital were also cited as reasons by low-income participants . The average reported delay from first symptoms to diagnosis for low-income participants was 3 . 5 y ( range 2–7 y ) , while for middle- and high-income participants it was 2 . 2 mo ( range 1–4 mo ) . Once diagnosed , the ability of patients to follow prescribed drug treatment was severely constrained . Although a long course of the drug diethylcarbamazine ( DEC ) ( currently 84 tablets , but previously up to 120 tablets [26] ) was provided free by the Anti-Filariasis Campaign , side effects were reported by participants , who then interrupted or gave up the treatment . One low-income man said of the treatment , “In our daily routine and our struggle to find work each day , how can we think of tablets ? ” Another low-income woman commented: “Tablets can cause nausea and stomach cramps . Then we cannot go out and do our work . ” Travel costs and income foregone were deterrents for low-income participants to obtaining free drugs from the government hospital . An average direct cost of a visit to the hospital for outpatient care by a low-income participant was Rs 215 ( US$2 ) which was the equivalent of 2 d of earnings for a low-income household . Inpatient care had an average direct cost of Rs 469 ( US$4 . 50 ) , which was equivalent to about 4 d of earnings for a low-income household . Corresponding indirect costs of income foregone because of attendance at health facilities amounted to 2 d of earnings for an outpatient visit and 15 d of earnings for an inpatient episode . Earned income in this population was not constant , but fluctuated from week to week and was unpredictable in poor households ( see Box 1 ) .
More advanced stages of the disease were present among both poor and non-poor participants , but there were marked differences in the opportunities for participants from different types of household to manage their condition and ameliorate symptoms . Middle and high-income participants generally benefited from clean homes and facilities to maintain personal hygiene , they reported fewer episodes of fever and fewer injuries to the limbs , and they could afford bandages to reduce swelling of the limbs . Poorer participants lived in less-hygienic conditions and thus were more prone to infection , and they could not avoid frequent lesions and wounds because of the hazardous nature of their work . Several participants reported having wounds that turned into suppurating sores , but out of necessity they had continued to work with an infected limb . More than half of the low-income participants reported that they could not afford the cost of attending the medical centre . The cost of frequent episodes of fever and swelling with pain was high for low-income participants . One low-income woman with lymphoedema had such episodes every 2 mo on average . She lost Rs 300 ( US$3 ) income ( equivalent to 6 d of female wages ) from her thatch weaving , her husband lost the equivalent of 2 d of income ( Rs 200 [US$2] ) when he stayed at home to cook and look after the children and they spent about Rs 100 ( US$1 ) during an episode on Panadol and herbal applications . A total loss of Rs 500–600 ( US$5–6 ) per episode of illness had drained their income , putting them in debt . The stigma associated with LF was a dominant theme in the accounts of most participants ( see Box 1 ) , which caused their condition to be hidden and contributed to delay in diagnosis with the subsequent advancement of the disease . For participants who had social standing in their village , social stigma tended to be a more important factor than costs in deterring them from seeking health care . Even high-income participants who reported no economic problems experienced mental health problems due to the stigma they suffered within the family . Among the male participants , hydrocoele was a source of both physical suffering and intense social stigma . All 15 men recounted embarrassment and stigma associated with the hydrocoele , which had led them to hide their condition for years , until it was advanced and severely debilitating . Most low-income men earned their living from casual labour , mainly coconut picking , which involved climbing to great heights . They either had to give up the occupation , which caused loss of earnings , or continue to climb with the hydrocoele , thus greatly aggravating the condition . One man commented that his hydrocoele was as big as the coconuts he was picking , but he still had to continue working with it . ( see Box 2 ) .
The social and economic consequences for the whole household , not just the participant , spanned years . Loss of income because of the condition was reported by all households in the sample , across all income levels—it was not just confined to the poorest . The narratives of participants revealed reasonably well-off households , the members of which were gradually degraded into poverty by the condition over many years ( Box 2 ) . Equally , households that were already poor were pushed further toward destitution ( Box 3 ) . For some households in the sample , the presence of a member with LF had been a hindrance to family progress , rather than a cause of poverty , holding the family finances back when they could have achieved an improved standard of living . In one case , a family opted to deny the existence of the family member with lymphoedema , leaving him in a shabby room , given food , but unwashed and depressed , while other family members continued to make social and economic advancement ( Box 4 ) . A less extreme case of rejection by a high-income family ( Box 4 ) , illustrates the mental distress , as well as economic hardship , that was a consequence of lymphoedema .
While LF has been recognised for some time as a leading cause of disability globally , it has been relatively neglected by public health policy makers . Part of the reason for this neglect may be that the full extent of the disability associated with this disease is hidden and not recorded in standard assessments restricted to physical impairment . In this study , we have shown the extremely debilitating nature of LF over a long period of time when mental health , social , and economic consequences are taken into account using the affordability ladder framework . We have identified four areas in which the clinical manifestations of W . bancrofti infection had a major impact on the lives and livelihoods of patients and their families in Sri Lanka . First , the condition and its diagnosis were severely affected by both stigma and costs . People with LF experienced the negative responses of others to their disfigured limbs or genitals , causing them to cover up the affected parts and , as the disease progressed , to hide themselves away from society in general . The social isolation from the stigma of the disease caused emotional distress , delay in diagnosis , and treatment , resulting in advancement of the disease beyond possible treatment . Second , treatment services that were available—free—from Government clinics were avoided because the participants' condition would be publicly identifiable . Local private practitioners were favoured , where their condition could be more easily hidden . However , the consequence of this behaviour was that the patients received less effective , or even ineffective , treatment from private practitioners , compared with the interventions available through the government clinics . Third , we found devastating economic and social consequences of the disease , for both patients and the household . The debilitating physical symptoms restricted the kind and quantity of work that participants could undertake , resulting in loss of earnings and impoverishment . Households were further impoverished by the costs incurred in using health services ( even though the services themselves were free ) and the cost of drugs , which had to be sustained over many years—leading to a medical poverty trap [27] . The impact of LF on productivity of the patients themselves can be considerable . In India , for example , a estimated US$842 million are lost to patients and households every year in treatment costs and reduced working time through acute and chronic disease caused by LF [7] . Other studies indicate that productivity loss in weavers can be as high as 27% [28] , and male patients with chronic episodes of LF can lose an equivalent of 15% of their earning capacity in any one year [29] . A study of the costs of nonfilarial elephantiasis in Ethiopia provided similar estimates . Direct costs of podoconiosis ( nonfilarial endemic elephantiasis of the lower leg ) amounted to US$143 per patient per year with productivity lost per patient of 45% of working days , equivalent to monetary loss of US$63 . The overall costs of this form of elephantiasis in one zone where the population is 1 . 5 million was estimated to be US$16 million per year [30] . For nearly all the participants in our sample , the incomes of other members of the household , in addition to the participant , were affected , either by having to forego employment to look after the patient or by making contributions to the health care costs . Several households in our sample had to withdraw children from school to help with work , which would perpetuate intergenerational poverty . Fourth , the adverse social and economic consequences were socially patterned . While we found that households from all three income levels had suffered reductions in income , those who were already on low incomes were pushed into near destitution by LF , from which it was almost impossible to escape . Low-income households also had less opportunity to obtain effective treatment from distant clinics , coupled with living and working conditions that made hygiene and compliance with treatment regimes more difficult . They were also less protected from stigma . These findings have significant policy implications . In Sri Lanka the prevalence of filarial elephantiasis in the population of three villages in Matara district has been estimated to be 3% and the prevalence of hydrocoele to be 6 . 2% [21] . The villages are typical of endemic areas in terms of socioeconomic mix and occupations . Scaling up the estimates to the whole 10 million population of the endemic provinces gives an expected 300 , 000 cases of elephantiasis among both women and men , and around 300 , 000 men with filarial hydrocoele from a male population of approximately 4 . 8 million . Every afflicted person lives in a household with another four individuals on average , all of whom may potentially suffer social and economic consequences as a result of having a family member with this condition . Even if the LF elimination programme is successful in arresting transmission of the disease so that there are no new cases , hundreds of thousands of people in Sri Lanka will continue to suffer clinical manifestations of the disease and will remain trapped in poverty . The affected households will need help and support for many years despite transmission having been arrested . Donors and the national government , who have to date understandably focussed on the single-dose annual preventive strategy where DEC and albendazole ( 400 mg chewable tablet donated by GlaxoSmithKline ) have been given annually since 2001 , need to re-evaluate how this neglected group can be served more effectively . This will first require a comprehensive survey to ascertain the number of people with elephantiasis and hydrocoele in the endemic districts . This survey needs to be followed by a reassessment of post-Mass Drug Distribution strategies , expansion of the lymphoedema treatment programmes to maximise coverage for those that remain symptomatic , and an aggressive approach to the provision of surgical care for male patients with hydrocoele . Direct financial support to afflicted families could be provided under the Sri Lankan Samurdhi poverty alleviation scheme of allowances for stipulated poor households . This approach could then take account of the context and barriers to effective treatment when designing and scaling up the post-Mass Drug Distribution implementation programmes . Supporting the evaluation of clinical interventions for the amelioration of lymphoedema will also be important , as the interventions currently being piloted in Sri Lanka and elsewhere need to recognise the social and economic context and constraints on the lives of participants , and how these differ by level of poverty . The inclusiveness and the caring quality of a health strategy for any given disease has to be judged by its capacity to reach out to the most vulnerable groups affected . The present study demonstrates one of the dilemmas that can arise in a strategy for the control and prevention of a disease leading to chronic conditions of ill health such as LF . The strategy itself , as in the case of Sri Lanka , can achieve its main objectives of prevention and elimination of the disease through large-scale interventions that reach the great majority of the population exposed to it . However , a highly vulnerable category of patients in advanced stages of the disease tends to have low visibility , becoming marginalized and forgotten . Special measures are needed to identify , reach and care for them . As the Global Filariasis Elimination Programme reports successes in arresting transmission , those with the condition should not be neglected but be specially targeted for the support the condition requires . Such support could be promoted by specific poverty reduction policies , which would be entirely appropriate given the evidence presented in this paper of the impact of the disease on poor communities , and particularly at the household level . | Lymphatic filariasis ( LF ) is a tropical disease causing extreme swelling of the limbs and male genitals . Despite recent successes in preventing transmission of the disease , some 40 million people worldwide who already have the disease have been largely neglected . We aimed to increase understanding of how this vulnerable , neglected group can be helped , by asking people with LF in Sri Lanka to recount their own experiences . Study participants reported that LF was extremely debilitating over a long period of time . The social isolation from stigma caused emotional distress and delayed diagnosis and treatment . Free treatment services at government clinics were avoided because the participants' condition would be identifiable in public . Loss of income due to the condition was reported by all households , not only those of the poorest . Households that were already on low incomes were pushed into near destitution by LF . Low-income households also had fewer opportunities to obtain effective treatment from distant clinics , and had living and working conditions that made treatment more difficult . With an estimated 300 , 000 people with swelling of the limbs in Sri Lanka , and around 300 , 000 men with swelling of the genitals , we conclude that the affected households will need help and support for many years to come , and offer suggestions for immediate action . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"public",
"health",
"and",
"epidemiology/health",
"policy",
"public",
"health",
"and",
"epidemiology/social",
"and",
"behavioral",
"determinants",
"of",
"health",
"infectious",
"diseases/helminth",
"infections",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"public",
"health",
"and",
"epidemiology/health",
"services",
"research",
"and",
"economics"
] | 2007 | Neglected Patients with a Neglected Disease? A Qualitative Study of Lymphatic Filariasis |
Only two classes of antiviral drugs , neuraminidase inhibitors and adamantanes , are approved for prophylaxis and therapy against influenza virus infections . A major concern is that influenza virus becomes resistant to these antiviral drugs and spreads in the human population . The 2009 pandemic A/H1N1 influenza virus is naturally resistant to adamantanes . Recently a novel neuraminidase I223R mutation was identified in an A/H1N1 virus showing cross-resistance to the neuraminidase inhibitors oseltamivir , zanamivir and peramivir . However , the ability of this virus to cause disease and spread in the human population is unknown . Therefore , this clinical isolate ( NL/2631-R223 ) was compared with a well-characterized reference virus ( NL/602 ) . In vitro experiments showed that NL/2631-I223R replicated as well as NL/602 in MDCK cells . In a ferret pathogenesis model , body weight loss was similar in animals inoculated with NL/2631-R223 or NL/602 . In addition , pulmonary lesions were similar at day 4 post inoculation . However , at day 7 post inoculation , NL/2631-R223 caused milder pulmonary lesions and degree of alveolitis than NL/602 . This indicated that the mutant virus was less pathogenic . Both NL/2631-R223 and a recombinant virus with a single I223R change ( recNL/602-I223R ) , transmitted among ferrets by aerosols , despite observed attenuation of recNL/602-I223R in vitro . In conclusion , the I223R mutated virus isolate has comparable replicative ability and transmissibility , but lower pathogenicity than the reference virus based on these in vivo studies . This implies that the 2009 pandemic influenza A/H1N1 virus subtype with an isoleucine to arginine change at position 223 in the neuraminidase has the potential to spread in the human population . It is important to be vigilant for this mutation in influenza surveillance and to continue efforts to increase the arsenal of antiviral drugs to combat influenza .
Two classes of antiviral drugs are approved for prophylaxis and therapy of influenza virus infected patients [1] . Antiviral therapy against the new ( swine-origin ) 2009 pandemic A/H1N1 influenza virus relies on the neuraminidase inhibitor ( NAI ) class of antiviral drugs only , because this subtype is resistant to the adamantane class ( amantadine and rimantadine ) of drugs [2] . In 2009 pandemic influenza viruses , this resistance pattern is mainly caused by an asparagine at amino acid position 31 ( N31 ) in the viral M2 membrane protein . Fortunately , NAI treatment , both as prophylaxis and therapy , has been shown to be effective against most 2009 pandemic H1N1 virus infections so far [3] , [4] . To date , the incidence of NAI resistant 2009 pandemic A/H1N1 viruses is very low . Nevertheless , 565 cases of patients infected with an ( H275Y , N1 numbering ) oseltamivir ( OS ) resistant virus have been reported to the World Health Organization [5] . In most of these cases , OS resistance was found in patients receiving prolonged antiviral therapy , in particular patients under immunosuppressive therapy [6] . The H275Y mutant viruses are cross-resistant to peramivir ( PER ) , but remain susceptible to zanamivir ( ZA ) . Successful clearance of a H275Y mutant virus from a patient treated with ZA was reported previously [7] . Within the first years after approval of the NAIs in 1999 , antiviral resistance in influenza viruses at a population level was rare ( 0 . 4% ) . In clinical trials , the incidence of resistant viruses was higher , varying from 0 . 4 to 1% in adults and up to 18% in young children [8] , [9] . However , a dramatic increase , up to 100% , of de novo circulating oseltamivir-resistant A/H1N1 viruses characterized the epidemic seasons of 2007-2008 and 2008-2009 [10] , [11] . This resistance phenotype was also caused by a H275Y mutation . Remarkably , earlier studies on H275Y mutant H1N1 viruses had characterized these viruses as attenuated and not of clinical importance [12] , [13] , [14] . The resistant viruses from 2007-2008 did not seem to be affected in replication capacity , transmissibility and their ability to cause severe disease in humans [15] , [16] , [17] . A compensatory role was assigned to the NA amino acid changes V234M , R222Q and D344N [18] , [19] . These substitutions may have restored the initial loss of NA activity due to the NAI resistance mutation and facilitated the appearance of the H275Y change in the epidemic influenza A/H1N1 viruses that circulated before the 2009 outbreak of the new pandemic virus . Recently , several research groups have studied the fitness of H275Y mutant pandemic influenza A/H1N1 viruses using both in vitro and in vivo experiments [20] , [21] , [22] , [23] , [24] . Overall , these data indicate that pandemic viruses with the NA H275Y substitution were comparable to their oseltamivir susceptible counterparts in pathogenicity and transmissibility in animal models . Recently , the identification of a novel multidrug resistant 2009 pandemic A/H1N1 virus was reported , isolated from an immune compromised child with reduced susceptibility to all NAIs [25] . An isoleucine to arginine substitution at position 223 in NA ( I223R , N1 numbering ) was detected in the patient after antiviral therapy with OS had failed due to the emergence of the H275Y mutation and therapy was switched to ZA . This I223R containing isolate , in which the H275Y mutation had disappeared , showed reduced susceptibility to OS ( 45-fold ) , PER ( 7-fold ) and ZA ( 10-fold ) . In vitro analysis showed that reversion of the arginine to isoleucine fully restored NAI susceptibility . In another case , an I223R/H275Y double mutant virus was isolated that showed high resistance to the NAIs [26] . In combination with the natural resistance of pandemic A/H1N1 viruses to adamantanes , an infection of such a multi-drug resistant virus leaves physicians without antiviral treatment options . The emergence of this pandemic 2009 A/H1N1 virus prompted us to investigate the properties of this clinical isolate by evaluating its in vitro replication kinetics and its pathogenicity and transmissibility in the ferret model . We here show that this 2009 pandemic influenza A/H1N1 clinical isolate , harboring a neuraminidase I223R substitution retains its virulence and transmissibility , but is less pathogenic than a virus prototype without this mutation . In addition , recombinant NL/602/09 with a single I223R amino acid substitution transmitted as well as its recombinant parental virus , suggesting that no additional mutations are needed to compensate for the presence of this I223R mutation in the 2009 pandemic A/H1N1 virus backbone .
A pandemic 2009 influenza virus with reduced susceptibility to all NAIs that was isolated from a Dutch immune compromised child was studied here . Full genome sequencing of this clinical isolate A/NL/2631_1202/2010 ( NL/2631-R223 , GenBank accession numbers JF906180-906187 ) harboring an I223R mutation in the neuraminidase was performed . Since no drug susceptible virus had been isolated from this patient before start of antiviral therapy , the well-characterized NAI-susceptible virus isolate A/NL/602/2009 ( NL/602 , GenBank accession numbers CY046940-046945 and CY039527-039528 ) was used as a reference virus in all experiments . This reference virus is a representative of pandemic H1N1 viruses that circulated in 2009 , with only amino acid changes I108V and V407I ( N1 numbering ) in NA being unusual among the deposited sequences in the Influenza Research Database [27] , [28] . Pair-wise comparison revealed , in addition to the amino acid change I223R , 5 amino acid differences in NA ( V106I , V108I , N248D , N386D and I407V ) and 1 in HA ( S203T ) . The NA and HA amino acid positions are given according to the N1 and H1 numbering . Eleven additional amino acid differences were found in gene segments PB2 ( 3 ) , PB1 ( 2 ) , PA ( 2 ) , NP ( 3 ) and NS ( 1 ) compared to NL/602 . None of these mutations have previously been identified as a virulence marker or as a compensatory mutation involved in restoration of NA activity loss , as a result of the presence of resistance mutations . By studying these isolates , a direct comparison could be made between a NAI susceptible and a novel I223R resistant virus , but such comparison does not address the impact of the single I223R mutation directly . Therefore , we introduced the I223R mutation in the recNL/602 backbone , resulting in the drug-resistant recNL602-I223R , to evaluate the impact of the single I223R mutation on virus replication , virus shedding from the upper respiratory tract and transmissibility in the ferret model . Virus replication was studied in vitro by multi-cycle replication kinetics of the viruses of interest . For this purpose , MDCK or MDCK-SIAT1 cell cultures were inoculated at a multiplicity of infection of 0 . 001 TCID50 per cell and at fixed time points supernatants were harvested to determine viral titers ( Figure 1 ) . Overall , the initial virus replication rates and end point titers were similar for the clinical isolate NL/2631-R223 and recNL/602 . A recombinant derivative of NL/602 with the I223R mutation in NA ( recNL/602-I223R ) replicated to lower peak titers in both cell lines compared to recNL/602 and NL/2631-R223 . In addition , initial virus replication of recNL/602-I223R was delayed by 6 to 12 hours in MDCK-SIAT1 cells . The pathogenicity of clinical isolate NL/2631-R223 was compared with NL/602 in the ferret model that was previously established to study the ability of influenza viruses to cause pneumonia [29] . Two groups of 6 ferrets were inoculated intratracheally with 106 TCID50 of virus . The animals were weighed daily as an indicator of disease . Over the 7-day period , no significant differences were observed in weight loss between the two groups inoculated with either virus . At day 4 post infection ( p . i . ) , when there were still 6 animals present in each group , the mean percentage of weight loss was 8 , 2±2 , 4% and 7 , 6±6 , 7% for NL/602 and NL/2631-R223-inoculated animals respectively , not statistically significant ( Figure 2A and B ) . In addition , no marked differences were observed for other clinical parameters , such as lethargy , sneezing and interest in food . Nose and throat swabs were collected daily from the inoculated animals and virus titers were determined by end-point titration in MDCK cells . Infectious virus shedding from the throat was detected from day 1 p . i . onwards in all ferrets , with similar patterns of virus shedding from the throat of the animals in the two groups ( Figure 2C ) . At day 4 p . i . , 5 and 4 animals were shedding virus from the nose in the NL/602 and NL/2631-R223 inoculated group respectively ( Figure 2D ) . Sequence analysis confirmed the presence of the I223R mutation in the respiratory samples collected at day 7 p . i . from the NL/2631-R223 inoculated ferrets . At day 4 and 7 p . i . , three animals of each group were euthanized and lungs were collected for virological and pathological examination . At day 4 p . i . , no marked differences were found between the virus titers for both groups of ferrets ( Figure 3A ) . At day 7 p . i . , no virus was detected in the lungs of ferrets inoculated with either virus . Gross pathology of the lungs of all animals revealed pulmonary lesions at day 4 and 7 p . i . ( Figure 3B ) . At day 4 p . i . , no marked difference was observed between the groups , but at day 7 p . i . , the percentage of affected lung tissue was higher in the group inoculated with NL/602 . The mean relative lung weight increased from day 4 to day 7 , with no difference between the animals inoculated with NL/602 or NL/2631-R223 ( Figure 3C ) . Histopathological examination of the lungs showed multifocal to coalescing alveolar damage in both groups characterized by the presence of macrophages and neutrophils within the lumina and thickened alveolar walls . At day 4 p . i . , the severity of alveolitis did not differ between the two groups ( Figure 4D ) . However , in agreement with the increased percentage of affected lung tissue at day 7 p . i . ( Figure 3B ) , also higher alveolitis scores were determined for the NL/602 inoculated animals at day 7 p . i . ( Figure 4D ) . The bronchial and bronchiolar epithelium from ferrets in both groups showed slight multifocal necrosis with moderate intra-epithelial infiltrates of neutrophils and multifocal peribronchiolar infiltration of macrophages , lymphocytes , neutrophils and plasma cells . The lumina contained moderate amounts of mucus mixed with cellular debris and few neutrophils . The tracheal epithelium in both groups showed mild neutrophilic infiltrates . The severity of both bronchiolitis and tracheitis increased from day 4 to 7 p . i . in ferrets infected with both viruses , but the differences in scores between groups were minimal ( Figure 4E and F ) . Individually housed ferrets were inoculated with virus isolate NL/2631-R223 or NL/602 and naïve animals were placed in a cage adjacent to each inoculated ferret at day 1 p . i . to allow aerosol or respiratory droplet transmission . All inoculated ferrets started to shed virus at day 1 p . i . with virus titers up to 106 TCID50/ml in throat and nose swabs ( Figure 4A and C ) . The naïve ferrets became infected , because of aerosol or respiratory droplet transmission , 1 , 2 or 3 days p . e . In the naïve animals , virus was detected in 4 ( NL/602 ) , or 3 ( NL/2631-R223 ) out of 4 animals ( Figure 4B and D ) . The exposed animal in the NL/2631-R223 transmission experiment , from which no virus could be isolated , did not seroconvert in the course of the experiment . At day 5 p . e . , the presence of the I223R mutation was confirmed by sequencing the NA gene of virus isolated from the throat swabs of the positive animals . When the multi-cycle replication kinetics were studied of viruses with or without the I223R substitution in MDCK cells , it was noticed that the recombinant virus in which the I223R mutation was introduced , recNL/602-I223R , replicated to lower titers than its parental virus recNL/602 ( Figure 1 ) . To address if this difference in in vitro replication capacity could be extrapolated to reduced replication in vivo , the ability of recNL/602-I223R to transmit in the ferret model was studied . It was expected that reduced replication in ferrets would impede the virus to transmit to naïve animals , thereby suggesting that compensatory mutations are needed to balance the fitness loss induced by the I223R mutation . In contrast to the results obtained in MDCK cells , recNL/602-I223R replicated and transmitted as well as recNL/602 when evaluated in the ferret transmission model . Inoculated animals started to shed virus from the upper respiratory tract from day 1 p . i . onwards and transmission was detected in 4 out of 4 ( recNL/602 ) , or 2 out of 2 ( recNL/602-I223R ) naïve animals from day 2 onwards ( Figure 5 ) . The presence of the I223R mutation in the recNL/602 backbone was confirmed in throat samples obtained from these animals at day 5 p . e .
Here , a 2009 pandemic influenza A/H1N1 virus isolate , harboring an I223R multidrug resistance mutation , was characterized by studying its replication capacity in MDCK cells and its pathogenicity and transmissibility in the ferret model . This I223R mutant virus is not attenuated for replication in the ferret respiratory tract and transmitted as well as NAI susceptible reference virus NL/602 . Furthermore , it was demonstrated here that compensatory mutations for the I223R mutation are not required , since recombinant NL/602 with a single I223R change transmitted as efficiently as its parental virus in ferrets . To date , 2009 pandemic viruses with an amino acid substitution at position 223 have only sporadically been isolated from patients . A I223V/H275Y double mutant was detected in two closely residing patients who were treated with OS [30] . Besides the I223R single mutant virus studied here , an I223R/H275Y double mutant was detected in an immune suppressed patient treated with OS and ZA [26] . The combination of these mutations resulted in an increased NAI resistance pattern , as compared to the resistance induced by the single mutations . This emphasizes that neuraminidase position 223 is an important marker for antiviral resistance and may be a key residue in the emergence of influenza viruses with resistance to all NAIs , especially in combination with other resistance-associated mutations . So far , the incidence of 2009 pandemic viruses with a 223 change is very low . Notably , 2009 pandemic viruses were reported with a serine to asparagine change at position 247 [31] . In combination with the H275Y change , these viruses demonstrated resistance patterns similar to the I223R/H275Y mutant . In a pathogenesis experiment , no statistical significant differences were found when weight loss was compared of ferrets inoculated with clinical isolates NL/2631-R223 or NL/602 ( Figure 2A and B ) . In agreement with high viral loads found in respiratory specimens collected from the patient who was infected with NL/2631-R223 , high viral loads were detected in the throat of animals inoculated with the same virus . Overall , identical patterns of virus shedding were observed during the course of the experiment in the throats of animals inoculated with either virus . However , virus shedding from the nose could not be detected in all inoculated animals . Although virus shedding from the nose of NL/2631-R223-inoculated animals seem somewhat delayed in comparison with NL/602-inoculated animals , these differences were not significant due to the large variations within groups and small group size after day 4 p . i . ( Figure 2C and D ) . Both macroscopic and microscopic evaluation of the lungs of the ferrets at day 4 p . i . , revealed no major differences in the percentage of affected lung tissue and relative lung weights between NL/2631-R223 and NL/602 ( Figure 3B and C ) . However , at day 7 p . i . the lungs of ferrets inoculated with NL/2631-R223 had not further deteriorated , whereas the percentage of affected lung tissue had increased to 50% in the NL/602 inoculated animals ( Figure 3B ) . This higher score for affected lung tissue in the NL/602-inoculated animals was also reflected by the higher score for the degree of alveolitis at day 7 p . i . compared to day 4 p . i . , whereas the alveolitis scores in the NL/2631-R223-inoculated animals at day 4 and 7 p . i . were similar . To recapitulate , both viruses replicated to the same extent in the respiratory tract of ferrets , but the NL/2631-R223 seemed less pathogenic compared to the NL/602 virus . Despite the moderate pathogenicity of NL/2631-R223 , this virus transmitted to 3 out of 4 exposed animals via aerosols or respiratory droplets ( Figure 4B ) . This result is comparable to the data obtained from NL/602 , in which 4 out of 4 exposed animals got infected ( Figure 4D ) [27] . This ferret transmission model was designed as a qualitative model for transmission and with the limited number of animals , quantitative information on virus transmission could not be obtained . Therefore , from these experiments it was concluded that both NL/2631-R223 and NL/602 transmitted via aerosols or respiratory droplets , although a delay in virus shedding by approximately 1 day was observed in the naïve animals exposed to NL/2631-R223 ( Figure 4B and D ) . When the impact of the single I223R mutation in the recombinant NL/602 backbone on in vitro replication kinetics was evaluated , a reduction in virus replication in MDCK cells was noticed ( Figure 1 ) . In addition , the initial virus replication of NL/602-I223R on MDCK-SIAT1 cells started 6 to 12 hours later as compared to its parental virus ( Figure 1B ) . These results suggested that compensatory mutations may be required to accommodate the isoleucine to arginine substitution at position 223 in NA and emphasizes the importance of the viral backbone used to study resistance-associated mutations . However , when recNL/602-I223R was tested in the ferret transmission model , the virus transmitted to 2 out of 2 exposed animals ( Figure 5B ) . When these results were compared with transmission data of recNL/602 ( Figure 5D ) [32] , no differences were found in the onset of virus shedding and virus titers that were detected in the collected throat and nose swabs from the exposed animals . This observation demonstrates that the transmissibility of recNL/602-I223R is not significantly diminished or can at least not be studied using a ferret transmission model . Although these results suggest that introduction of the I223R does not attenuate the virus , it cannot be ruled out that other mutations than 223R in NL/2631-R223 may have compensated for the initial loss of fitness due to the I223R mutation . Sequence comparison revealed 5 amino acid differences between NL/2631-R223 and NL/602 . The only amino acid substitution that is located near the active site of the neuraminidase is at position 248 , where NL/602 harbors an aspartic acid and NL/2631-R223 an asparagine . Interestingly , neighboring residue 247 has been linked to NAI resistance in combination with the H275Y mutation [31] . Further research is needed to study the I223R resistance mechanism in competitive mixture experiments and potential co-mutations on a molecular level [33] . To note , small differences between NL/602 and recNL/602 could be observed in replication capacity and transmission patterns in ferrets ( Figure 4 and 5 ) . Previously , differences were also found in pathogenesis experiments , where the wild type NL/602 was detected more abundantly in the lower airways of ferrets than recNL/602 [34] . These observed differences may be a result of the use of a virus isolate rather than a virus generated by reverse genetics and to a different batch of ferrets used in the different studies . A direct comparison between virus isolates and recombinant viruses can , therefore , not be made . The different inoculation routes and inoculation doses used for influenza research is subject of debate . The intratracheal route of inoculation is often used to study pathogenicity or to study the efficacy of vaccines to prevent lower respiratory tract infection . In contrast , the intranasal route of inoculation is used when transmissibility is studied . Unfortunately , these inoculation routes and inoculation doses do not accurately mimic the natural way of infection and may mask the fitness differences between the drug-resistant and drug sensitive viruses . However , the recipient animals in the transmission experiment are infected via the natural route; aerosols or respiratory droplets shed by the donor ferret . The virus secretion pattern , which is the combination of the amount of virus secreted and the duration of virus shedding from the upper respiratory tract , of animals exposed to recNL/602 and rec/NL602-I223R are similar . This suggests that no marked differences in viral fitness are introduced by the single I223R mutation . The present study demonstrates for the first time that a 2009 pandemic A/H1N1 clinical isolate containing a resistance mutation at position 223 in the NA is not attenuated in its replication capacity and transmissibility in a ferret model . Although the pathogenicity of this virus seems less severe compared to a relevant reference virus in the ferret model , it is unclear whether this moderate pathogenicity has implications for infections with multidrug-resistant viruses in humans . Continuous surveillance is needed to monitor the emergence of ( novel ) influenza viruses with reduced susceptibility to the NAIs or mutations that may facilitate the emergence of circulating multi drug resistant influenza viruses .
Animals were housed and experiments were conducted in strict compliance with European guidelines ( EU directive on animal testing 86/609/EEC ) and Dutch legislation ( Experiments on Animals Act , 1997 ) . All animal experiments were approved by the independent animal experimentation ethical review committee ‘stichting DEC consult’ ( Erasmus MC permit number EUR1821 ) and were performed under animal biosafety level 3+ conditions . Animal welfare was observed on a daily basis , and all animal handling was performed under light anesthesia using ketamine to minimize animal suffering . Influenza virus seronegative 6-month-old female ferrets ( Mustella putorius furo ) , weighing 800–1000 g . , were obtained from a commercial breeder . Madin-Darby Canine Kidney ( MDCK ) cells were obtained from American Type Culture Collection . MDCK-SIAT1 cells , constitutively expressing the human 2 , 6-sialyltransferase ( SIAT1 ) , were kindly provided by Professor H . D . Klenk , Philipps University Marburg [35] . Both cell lines were cultured in Eagle’s minimal essential medium ( EMEM ) ( Lonza , Breda , The Netherlands ) supplemented with 10% fetal calf serum ( FCS ) , 100 IU/ml penicillin , 100 µg/ml streptomycin , 2mM glutamine , 1 . 5mg/ml sodium bicarbonate ( Cambrex ) , 10 mM HEPES ( Lonza ) and non-essential amino acids ( MP Biomedicals Europe , Illkirch , France ) . In addition , MDCK-SIAT1 cells were cultured in the presence of 1 mg of antibiotic G418/ml . Influenza virus A/Netherlands/2631_1202/2010 ( NL/2631-R223 ) was isolated from a 5-year-old immune compromised child [25] . Clonal virus of this isolate was obtained by passaging this virus 3 times under limiting diluting conditions in MDCK cells . Full genome sequencing after the last MDCK passage confirmed the absence of mutations . Influenza A/Netherlands/602/2009 ( NL/602 ) was characterized previously [27] . All eight segments of this virus were cloned in a bidirectional reverse genetics plasmid pHW2000 and used to generate recombinant viruses by reverse genetics as described previously [36] . The I223R mutation was introduced in the NA gene of NL/602 using QuickChange multi site-directed mutagenesis kit ( Stratagene , Leusden , The Netherlands ) resulting in recombinant viruses recNL/602-I223R . The presence of this mutation was confirmed by sequencing . Virus titers in nasal and throat swabs , homogenized tissue samples , or samples for replication curves were determined by endpoint titration in MDCK cells . MDCK cells were inoculated with 10-fold serial dilutions of each sample , washed 1 hour after inoculation with phosphate-buffered saline ( PBS ) , and grown in 200 µl of infection medium , consisting of EMEM supplemented with 100 U/ml penicillin , 100 µg/ml streptomycin , 2 mM glutamine , 1 . 5 mg/ml sodium bicarbonate , 10 mM HEPES , nonessential amino acids , and 20 µg/ml trypsin ( Lonza ) . Three days after inoculation , the supernatants of inoculated cell cultures were tested for agglutinating activity using turkey erythrocytes as an indicator of virus replication in the cells . Infectious-virus titers were calculated from 4 replicates by the method of Spearman-Kärber [37] . Multi-cycle replication curves were generated by inoculating MDCK or MDCK-SIAT1 cells at a multiplicity of infection ( MOI ) of 0 . 001 50% tissue culture infectious dose ( TCID50 ) per cell . One hour after inoculation , at time point 0 , the cells were washed once with PBS , and fresh infection medium was added . The supernatants were sampled at 6 , 12 , 24 , and 48 h post infection and the virus titers in these supernatants were determined by means of endpoint titration in MDCK cells . For the pathogenesis experiment , statistical analysis was done for each time point , until 4 days after inoculation ( when there were still 6 animals present in each group ) . The Mann-Whitney-U test was used to compare weight losses and virus shedding of the six animals in both groups . P-values less than 0 . 05 were considered significant . | Recently , a 2009 pandemic A/H1N1 influenza virus was isolated from an immune compromised patient , with antiviral resistance to the neuraminidase inhibitor class of drugs . This virus had an amino acid change in the viral neuraminidase enzyme; an isoleucine at position 223 was substituted for an arginine ( I223R ) . Patients infected with a pandemic virus that is resistant to all neuraminidase inhibitors , would leave physicians without antiviral treatment options , since these viruses are naturally resistant to the other class of antivirals , the adamantanes . To date , it is unknown if this I223R mutant virus is affected in its ability to cause severe disease and to transmit to other humans . Therefore , we have addressed this question by comparing the I223R mutant virus with a wild type reference virus in a ferret pathogenicity and transmission model . We found that the I223R mutant virus was not severely affected in its pathogenicity , although fewer lung lesions and alveolitis scores were found for the I223R mutant virus . In addition , we demonstrated that this virus transmitted efficiently to naïve ferrets . Consequently , we conclude that this I223R mutant virus has the potential to cause disease and may spread among humans . Therefore , influenza surveillance for this resistance pattern is advised . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"influenza",
"viral",
"diseases",
"infectious",
"disease",
"control"
] | 2011 | Multidrug Resistant 2009 A/H1N1 Influenza Clinical Isolate with a Neuraminidase I223R Mutation Retains Its Virulence and Transmissibility in Ferrets |
Lymphatic filariasis ( LF ) is best known for the disabling and disfiguring clinical conditions that infected patients can develop; providing care for these individuals is a major goal of the Global Programme to Eliminate LF . Methods of locating these patients , knowing their true number and thus providing care for them , remains a challenge for national medical systems , particularly when the endemic zone is a large urban area . A health community-led door-to-door survey approach using the SMS reporting tool MeasureSMS-Morbidity was used to rapidly collate and monitor data on LF patients in real-time ( location , sex , age , clinical condition ) in Dar es Salaam , Tanzania . Each stage of the phased study carried out in the three urban districts of city consisted of a training period , a patient identification and reporting period , and a data verification period , with refinements to the system being made after each phase . A total of 6889 patients were reported ( 133 . 6 per 100 , 000 population ) , of which 4169 were reported to have hydrocoele ( 80 . 9 per 100 , 000 ) , 2251 lymphoedema-elephantiasis ( LE ) ( 43 . 7 per 100 , 000 ) and 469 with both conditions ( 9 . 1 per 100 , 000 ) . Kinondoni had the highest number of reported patients in absolute terms ( 2846 , 138 . 9 per 100 , 000 ) , followed by Temeke ( 2550 , 157 . 3 per 100 , 000 ) and Ilala ( 1493 , 100 . 5 per 100 , 000 ) . The number of hydrocoele patients was almost twice that of LE in all three districts . Severe LE patients accounted for approximately a quarter ( 26 . 9% ) of those reported , with the number of acute attacks increasing with reported LE severity ( 1 . 34 in mild cases , 1 . 78 in moderate cases , 2 . 52 in severe ) . Verification checks supported these findings . This system of identifying , recording and mapping patients affected by LF greatly assists in planning , locating and prioritising , as well as initiating , appropriate morbidity management and disability prevention ( MMDP ) activities . The approach is a feasible framework that could be used in other large urban environments in the LF endemic areas .
Lymphatic filariasis ( LF ) is a neglected tropical disease ( NTD ) that can have a devastating impact on affected individuals , with clinical symptoms such as acute dermatolymphangioadenitis ( ADLA , “acute attacks” ) , lymphoedema and elephantiasis , and hydrocoele , causing physical , mental and economic distress [1–6] . In recognition of this , the World Health Organization’s ( WHO ) Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) requires that countries wishing to be recognised as having eliminated LF are required not only to prove that disease transmission has been interrupted through mass drug administration ( MDA ) , but that they are also alleviating the suffering of those affected by providing a minimum package of care to each person with lymphoedema/elephantiasis ( LE ) and hydrocoele in LF endemic areas [7] . This package includes ( i ) surgery for hydrocoele [8 , 9] , ( ii ) support for episodes of ADLA [10 , 11] , and ( iii ) management of LE to prevent disease progression . Countries wishing to complete the WHO dossier for certification of elimination as a public health problem are therefore required to firstly estimate the number of patients in endemic areas at the implementation unit ( IU ) level , and to provide information on the number of facilities able to provide the necessary care to these identified patients . Finally , an assessment of the readiness and quality of the care being provided at these services is also required [12 , 13] . Patient numbers at the IU level will enable national LF elimination programmes to appropriately forecast , plan and manage patient care , and to meet the requirements of the WHO dossier for programme success . At present , despite great progress towards interrupting transmission of disease with 63 of the 73 endemic countries having initiated MDA , only 18 of these are reported to monitor morbidity management and disability prevention ( MMDP ) at this geographical level , i . e . have identified the number of IUs with known cases , or the IUs where MMDP services are provided [12] . There are currently no specific guidelines on the methods for obtaining patient estimates , although an MMDP toolkit is currently under development by WHO to provide additional guidance for this . Current documented suggestions include collecting LE and hydrocoele information during LF baseline prevalence surveys , when enumerating households during MDA , or conducting separate surveys either independently or in collaboration with other organizations concerned with similar disabilities and their care [14] . Many countries are known to collate morbidity information whilst distributing MDA , however the quality of this information has been shown to be very variable , with national programmes lacking the necessary resources to validate their data [15 , 16] . Examples of bespoke patient enumeration surveys can also be found in the literature , however these tend to be on a small scale and are labour intensive and as such may be difficult to scale up to the required geographical level [16 , 17] . Dar es Salaam is a large and densely populated region on the coast of Tanzania which at the time of the survey ( 2015 ) comprised of three districts: Temeke , Kinondoni and Ilala . In this region LF is caused by the Wuchereria bancrofti parasite , transmitted by the Culex mosquito . Previous estimates of LF prevalence in Dar es Salaam measured using immunochromatographic tests ( ICTs ) includes 9 . 9% ( Temeke = 13 . 2% , Kinondoni = 14 . 4% , Ilala = 4 . 7% ) , by Mwingira et al . ( 2017 ) [18] , and 3 . 0% by Mwakitalu et al . ( 2013 ) [19] . Four rounds of MDA have been completed in the Dar es Salaam region since 2013 , with varying therapeutic coverages due to the challenges associated with administering treatment within a large dynamic urban population . While it was anticipated that there was likely to be a significant number of LF clinical cases in Dar es Salaam , there was limited data on the scale of the problem . As such , there was a relative lack of evidence upon which to plan a suitable MMDP strategy [20] . The primary aim of this study was to improve our knowledge of the overall LF morbidity burden in Dar es Salaam , and further to gain an understanding of the geographical distribution of cases to guide the delivery of MMDP services to where they are most needed . Due to the association between the prevalence of clinical cases and of infection , knowing more about the locations of cases may also allow programmes to identify areas where MDA should be more focussed [21] . To undertake this task at this geographical scale in a time-effective manner , we implemented the mHealth short message service ( SMS ) reporting system , ‘MeasureSMS-Morbidity’ , which enables the rapid collection , collation and dissemination of estimated LF patient numbers [22 , 23] . Prior to this study , MeasureSMS-Morbidity had been implemented in rural areas of Malawi and Ghana , but had yet to be trialled in a densely populated urban area [22] . The system relies upon a network of local health workers to each collect individual-level patient data over small geographical areas which they then submit via SMS using their own mobile phones . These data are then automatically collated into an online database which can be accessed and interrogated using a web browser . Mobile technology-driven , community-led approaches to conducting surveys such as this have proven to increase time efficiency , and consequently cost-efficiency , of data collection whilst further increasing the sense of local data ownership and accountability [23] .
LF patient identification and reporting activities are part of routine programme activities conducted by the Ministry of Health and Social Welfare , Tanzania , and as such ethical clearance in Tanzania was not required , and a waiver was granted . Oral consent to record and report details relating to their condition was obtained from all identified LF patients , however this was not documented . Reported and verified patients were verbally informed of the purpose of the activity , and all resulting data were analysed anonymously . Data reporters did not record any information on patients who refused to disclose any information , and those who did not wish to be examined by the clinical officer were excluded from the verification survey . Ethical clearance was obtained from the Liverpool School of Tropical Medicine Research Ethics Committee ( Research Protocol 12 . 22 ) . In order to achieve the primary aim of this study i . e . to assess the LF morbidity burden in Dar es Salaam , a patient identification survey was undertaken . This survey was undertaken in three phases between March and August 2015 , covering each of Dar es Salaam’s three districts i . e . Temeke , Kinondoni and Ilala . To undertake this task efficiently with respect to both time and resources , a health community-led door-to-door survey approach was used and the MeasureSMS-Morbidity reporting tool was incorporated to rapidly collate and monitor the data . Detailed information on the MeasureSMS-Morbidity system can be found elsewhere [22–24] . In brief , this system allows basic information on identified patients such as their location , sex , and age , plus information on their clinical condition ( LE or hydrocoele ) , severity of LE ( mild , moderate , severe ) , and number of acute attacks experienced in the last 6 months , to be recorded by the health community staff and reported via SMS using their own phones . This information can then be viewed in real-time via a web browser . Each individual SMS report also generates a SMS response indicating either that the message had been sent in the correct format , or that there are reporting errors that required correcting . The severity of LE was classified as either mild = slight swelling , moderate = enlarged limb with shallow folds , and severe = greatly enlarges limb with deep folds as previously described [22] . These categories correspond to a Dreyer staging of 1–2 ( mild ) , 3–4 ( moderate ) and 5+ ( severe ) . Prior to this current study , the MeasureSMS-Morbidity system had been implemented in rural settings only , and this was the first time at which the system had been implemented at such a large scale , and in an urban area . To facilitate this scale up , the implementation process was revised and refined after each phase . Each phase consisted of a training period , a patient identification and reporting period and a data verification period . This verification period was incorporated into the study to address the secondary objective of assessing data quality . Specifically , the verification period enabled the positive predictive value ( PPV ) of the identified patients to be estimated i . e . the proportion of patients identified during the survey who were confirmed to have lymphoedema or hydrocole . Phase 1 ( Temeke ) was initiated in March 2015 , and Phases 2 ( Kinondoni ) and 3 ( Ilala ) was commenced in July and August 2015 respectively . The initial process and the subsequent refinements are described below .
Table 1 presents the number of patients with each condition reported by SMS . In absolute terms , Kinondoni had the highest number of reported patients ( 2846 ) comprised of 950 LE only patients , 1654 hydrocoele only patients , and 242 with both conditions . This was followed by Temeke ( 2550 total , 807 LE , 1560 hydrocoele , 183 with both ) , then Ilala ( 1493 total , 494 LE , 955 hydrocoele , 44 with both ) . Prevalence estimates ( per total population and per male population for hydrocoele ) were calculated using the 2015 population estimates as the denominator , which were extrapolated from the 2012 census using annual growth rates of 5 . 8% , 4 . 9% and 6 . 5% for Temeke , Kinondoni and Ilala respectively , i . e . population estimates of 1 , 621 , 148 , 2 , 048 , 976 and 1 , 485 , 308 , totalling 5 , 155 , 432 , with male population estimates of 707 , 861 , 902 , 981 and 634 , 663 totalling 2 , 245 , 505 [25] . The overall reported morbidity prevalence for Dar es Salaam was 133 . 6 per 100 , 000 total population , with the highest prevalence seen in Temeke ( 157 . 3 per 100 , 000 ) and the lowest in Ilala ( 100 . 5 per 100 , 000 ) . This pattern was consistent for both conditions , with LE only prevalence per 100 , 000 population ranging from 33 . 3 in Ilala to 49 . 8 in Temeke , and hydrocoele only prevalence per 100 , 000 males ranging from 150 . 3 in Ilala to 220 . 4 in Temeke . The ratio of LE to hydrocoele patients was consistent across all three districts , with the number of hydrocoele patients being almost twice that of LE cases . Table 2 presents summaries of the reported patients by age and sex . District-level population data was obtained from the National Bureau of Statistics [26] for 2012 , and the totals were projected to represent 2015 as described above . The prevalence of LE was approximately equal between males and females in all three districts ( Temeke: 64 . 84 and 71 . 72 per 100 , 000 population; Kinondoni: 67 . 89 and 60 . 37; Ilala 39 . 86 and 42 . 84 for males and females , respectively ) . A positive relationship was observed between age and LE prevalence in all three districts and both sexes , with the highest prevalence being observed in the oldest age group in Temeke ( >74 years , 708 . 44 patients per 100 , 000 population ) , and in the 60–74 age group in Kinondoni ( 474 . 30 patients per 100 , 000 population ) and Ilala ( 353 . 16 per 100 , 000 population ) . For hydrocoele , a similar trend in age was observed ( 1 , 075 . 4 patients per 100 , 000 population aged >74 in Temeke , 727 . 8 patients per 100 , 000 population aged 60–74 in Kinondoni and 614 . 2 patients per 100 , 000 population aged 60–74 in Ilala ) . Table 3 presents summaries of severity of reported LE ( mild , moderate , severe ) by district , including the mean and standard deviation of the number of reported number of acute attacks experienced in the previous six months . Overall , severe LE patients account for approximately a quarter of those reported ( 26 . 9% overall ranging from 25 . 0% in Temeke to 31 . 0% in Ilala ) , with the number of acute attacks increasing with reported severity ( 1 . 34 in mild cases , 1 . 78 in moderate cases , 2 . 52 in severe ) . The variability in the number of reported attacks also increases with severity ( 1 . 33 in mild cases , 1 . 62 in moderate , 1 . 84 in severe ) . Similar trends are seen in each of the three districts . Maps of prevalence by ward level , using extrapolated 2015 population estimates as the denominator ( http://ihi . eprints . org/2168/1/Village_Statistics . pdf ) are presented in Fig 2 . These figures highlight the high morbidity prevalence in the southern peri-urban wards of Temeke and the northern peri-urban wards of Kinondoni . The MeasureSMS-Morbidity system stores every SMS received and automatically checks the SMS for formatting errors . Table 4 summarises the SMS acceptance rates ( % of SMS without any formatting errors ) for each of the three patient identification phases . These error rates reflect two processes . Firstly , they give an indication of the data reporters’ ability to report data using the MeasureSMS-Morbidity system , which may in turn reflect improvements in the training process . Secondly , they reflect the effectiveness of the data supervision process , as the incoming data were reviewed each day by the supervision team and data reporters were contacted when repeated errors or data inconsistencies were identified . During Phase 1 ( Temeke ) , data supervision was largely undertaken by the donors whereas phases 2 ( Kinondoni ) and 3 ( Ilala ) was managed by the Ministry of Health staff , with support from the donor . In comparing the acceptance rates between these three period , no significant differenced were observed ( p = 0 . 3788 ) . However once duplicates were removed , significant differences were observed ( p<0 . 001 ) , with a greater proportion of accepted messages being kept in phase 1 ( Temeke = 98 . 5% ) in comparison to phases 2 and 3 ( Kinondoni = 94 . 7 and Ilala = 94 . 0% ) . Fig 3 presents the acceptance rate by reporting day for each of the three districts . We again observe slightly higher acceptance rates by day in Phase 1 ( Temeke ) . Table 4 indicates that in each district , data were reported over a period of 12–15 days , with most data ( greater than 80% ) being sent in the first 7 days of the reporting period . We therefore hypothesise that the decline in acceptance rates towards the end of the patient identification period seen in Fig 3 is likely due to there only being a small number of reports being submitted during this time , with these data reporters being those who had difficulties using the system . The length of the verification surveys varied with each phase . Due to adverse flooding conditions experienced in March and May 2015 , the simple ( stratified ) random sampling approach used , and the difficulties in locating selected patients as described in the methods section , the verification process in Temeke lasted over 13 months and was completed in May 2016 . Over this period , 38 patients were randomly selected for verification and visited by the verification team . No issues were observed between paper forms and data sent via SMS when paper forms were reviewed as the initial part of the verification . When comparing the written records collected during this verification visit with the initial data reported during the patient identification survey it was possible to match 36 ( 15 reported LE , 21 reported hydrocoele ) of the 38 patients by patient ID and gender . Of these 36 , the main difference observed were in the initially reported age and the age reported during verification , with a median difference of two years . As inconsistencies in age recall is common in these settings , differences of up to 10–15 years were expected . Four matched patients in Temeke had an age difference of greater than 15 years . Adaptations made to the data collected during patient identification i . e . the inclusion of the patient’s phone number , plus the refinements made to the sampling process resulted in the verification for Kinondoni and Ilala being completed much more efficiently . There were however still some delays due to the transience of the population in Dar es Salaam as well as some restructuring of the city resulting in large areas of houses being demolished between the time of patient identification and data verification period . In both Kinondoni and Ilala , 115 patients , reported by 16 data reporters in each district , were verified over a 6 month period . Of the 115 patients in Kinondoni it was not possible to match 17 patients with the reported data , hence only 98 verified patients ( 24 reported LE , 71 reported hydrocoele , 3 reported with both conditions ) were included in the final dataset . The median difference in reported age for these 98 patients was two years , with 10 patients having age differences of greater than 15 years . In Ilala , only one patient could not be matched , hence 114 patients were included ( 30 reported LE , 75 reported hydrocoele , 9 reported with both conditions ) . The median difference in reported age of these patients was three years , with 12 patients having age differences greater than 15 years . Table 5 presents the verification results by district . Of the 81 patients reported to have either LE or both conditions , 63 ( 77 . 8% ) were diagnosed by the verification team as having LE , ranging from 71 . 8% ( 28/39 ) in Ilala to 86 . 7% ( 13/15 ) in Temeke . Of the 179 patients reported to have hydrocoele or both conditions , 165 ( 92 . 2% ) were diagnosed as having hydrocoele by the verification team , ranging from 88 . 1% ( 74/84 ) in Ilala to 95 . 9% ( 71/74 ) in Kinondoni . Similar results were observed after removing the 26 patients whose reported age differed by more than 15 years between the two data sources ( S1 Table ) . Of the 18 people misdiagnosed with LE , 14 were confirmed to have hydrocoele , whereas 4 had other conditions . Of the 14 people misdiagnosed with hydrocoele , 5 had LE , 1 had a hernia , 2 had previously had hydrocoele but had been operated on , 5 had other conditions and 1 did not have any discernible illness . It is also worth noting that of the 165 confirmed to have hydrocoele , 27 ( 16 . 3% ) had a concurrent hernia . Twelve patients with both LE and hydrocoele were included in the verification survey . Only 4 ( 33 . 3% ) of these were confirmed to have both conditions , 5 had hydrocoele only , 2 had LE only and 1 had a non-LF associated condition . Of the 63 patients correctly verified as having LE , 60 had reported the severity of the condition ( 11 mild , 26 moderate , 23 severe ) . In comparing these with the Dreyer score determined for each patient during the verification process ( stage 1–2 = mild , stage 3–4 = moderate , stage 5+ = severe ) , 3 ( 27 . 3% ) were correctly verified as mild , 16 ( 61 . 5% ) were correctly identified as moderate and 6 ( 26 . 1% ) were correctly identified as severe ( Table 6 ) . Overall , there was low agreement between the severity assigned by the patient identifiers and the verification team ( Cohen’s kappa = 0 . 083 ) .
With the prevalence of LF cases previously unrecorded , these results indicate a much higher burden of LE and hydrocoele in Dar es Salaam than anticipated for an urban centre , with 2251 patients reported to have LE , 4169 patients reported to have hydrocoele plus a further 469 patients having both conditions . Whilst it is recognised that there may be some false positives in these reports , and further that some patients may have been missed from the survey , the verification survey results give the national LF elimination programme confidence that these numbers are representative of the true burden . Further to these absolute numbers , the national programme now also has additional information on the geographical distribution of disease , with the district of Temeke being the most affected of Dar es Salaam’s three districts with the larger , less densely populated wards reporting in excess of 500 cases per 100 , 000 people . This information is crucial in determining the resources needed to manage the conditions affecting these patients , and will assist in selection in locations within the urban area within which care services could be most effectively provided . As with many endemic countries , strategic partnerships have been developed in Tanzania between the national LF programme and international donors to address the immense burden of LF clinical disease within the country , leading to the development of plans to scale up hydrocele surgery throughout 2016 . Further , plans to engage CHVs to provide home-based LE training and care for patients and their caregivers have already been developed with the region’s NTD and home-based care teams . These services are intended to be supplementary to the specialist filariasis clinic which has been operated by the national programme in Dar es Salaam since 2000 [20] . The data obtained in this study will also , in addition to guiding morbidity management activities , provide an insight into disease transmission within Dar es Salaam which is likely to aid the management of future MDA campaigns , or perhaps the development of alternative transmission breaking strategies such as vector control through improved drainage and Culex mosquito population reduction e . g . using polystyrene beads [27 , 28] . Difficulties in conducting MDA within an urban environment often arise due to the factors such as high population movement , and the resulting low compliance can result in MDA coverage levels that are inadequate to permanently break transmission . It is also not clear to what extent the distribution of patients reflects the transmission patterns in such a mobile urban population . However , the additional disease transmission information provided by patient mapping may be useful in raising awareness across the city in general , targeting potential high risk areas with more intensive community sensitisation campaigns , and/or increasing MDA distribution points to increase coverage [18 , 19 , 29 , 30] . It is likely that future MDAs will benefit from the active and successful morbidity management activities , as it has been shown that LE management programmes can be important for improving increasing MDA coverage [31] . In implementing the two-tiered patient identification and reporting process in an iterative manner , it was possible to refine the process with each phase of implementation . Relocating patients after the initial patient identification survey proved to be the most challenging aspect of this approach , with unavoidable contributing factors including adverse weather conditions and population displacement due to the demolition of many houses . Difficulties also arose due to the patient identifier being unfamiliar with the area to which they had been assigned . Whilst this problem lessened over the course of the exercise , primarily through ensuring the national programme could contact the patient directly by mobile phone , it may be beneficial to consider using locally-based patient identifiers should this exercise be repeated elsewhere . Additionally , by increasing the emphasis on the staging and severity of LE in the training , the accuracy of the reported data could be improved , thus providing more accurate information on the level of services required in any given area . Despite the implementation challenges , there were many benefits to the patient identification and the reporting process adopted for this exercise . Notably , the ability to view and assess the quality of the patient identification data in real time using the MeasureSMS-Morbidity tool was a great asset . Enabling information on the morbidity burden to be known almost instantaneously , as opposed to having to wait a prolonged period for collation and digitization of the paper forms , also facilitates the provision of needed care to patients much more efficiently [20] . By the end of the study , the responsibility of monitoring the real-time data and ensuring its quality was led by the national programme team themselves , thus empowering the local teams to manage and control their own success . This approach of promoting the national programme to take full ownership of the data and the implementation of the process had the effect of building valuable data management and health surveillance capacity within the local team [32 , 33] . This exercise highlights how crucial patient estimates are for the provision of much needed care to LF patients and the information obtained greatly assists in planning , locating and prioritising , as well and initiating , appropriate MMDP activities . The approach to patient identification and reporting presented in this paper provide a feasible framework that could be adopted in other large urban environments [24] , and thereby enable these endemic areas to achieve the MMDP components of the GPELF [12] , or could be adapted to address patient identification issues for other diseases . The focus should now be to develop and implement strategies to meet the needs of the patients identified during this exercise , and thus ensuring no patient is left behind . | Lymphatic filariasis ( LF ) can cause disabling conditions in infected patients including lymphoedema-elephantiasis ( LE ) and hydrocoele . Identifying the number and locations of these patients is the first step towards ensuring that these patients receive the care they require , however there is currently no standardised approach for this essential action . This paper presents a health community-led approach for rapidly identifying patients in urban areas using an SMS reporting system , MeasureSMS-Morbidity , that allows health workers to report individual-level patient information ( age , sex , location , condition , severity ) , which can be then be viewed in real-time via a web browser . The quality of the data can be easily monitored during the data collection period , and there is instant availability of patient information . This system is used here in the large urban centre of Dar es Salaam , Tanzania . A total of 6889 patients were identified , equating to 80 . 9 hydrocele patients per 100 , 000 population , 43 . 7 LE patients per 100 , 000 people , and 9 . 1 patients with both conditions . This information is now enabling the national neglected tropical disease ( NTD ) program to provide the essential care facilities and training for LF healthcare in locations in the city where it is most needed . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"population",
"dynamics",
"tropical",
"diseases",
"geographical",
"locations",
"parasitic",
"diseases",
"health",
"care",
"data",
"management",
"filariasis",
"tanzania",
"urban",
"environments",
"neglected",
"tropical",
"diseases",
"population",
"biology",
"lymphatic",
"filariasis",
"africa",
"morbidity",
"computer",
"and",
"information",
"sciences",
"geography",
"people",
"and",
"places",
"helminth",
"infections",
"health",
"statistics",
"urban",
"areas",
"earth",
"sciences",
"geographic",
"areas",
"biology",
"and",
"life",
"sciences",
"terrestrial",
"environments",
"geographic",
"distribution"
] | 2017 | Lymphatic filariasis patient identification in a large urban area of Tanzania: An application of a community-led mHealth system |
Schistosomiasis has reemerged in China , threatening schistosomiasis elimination efforts . Surveillance methods that can identify locations where schistosomiasis has reemerged are needed to prevent the further spread of infections . We tested humans , cows , water buffalo and the intermediate host snail , Oncomelania hupensis , for Schistosoma japonicum infection , assessed snail densities and extracted regional surveillance records in areas where schistosomiasis reemerged in Sichuan province . We then evaluated the ability of surveillance methods to identify villages where human infections were present . Human infections were detected in 35 of the 53 villages surveyed ( infection prevalence: 0 to 43% ) , including 17 of 28 villages with no prior evidence of reemergence . Bovine infections were detected in 23 villages ( infection prevalence: 0 to 65% ) and snail infections in one village . Two common surveillance methods , acute schistosomiasis case reports and surveys for S . japonicum-infected snails , grossly underestimated the number of villages where human infections were present ( sensitivity 1% and 3% , respectively ) . Screening bovines for S . japonicum and surveys for the presence of O . hupensis had modest sensitivity ( 59% and 69% respectively ) and specificity ( 67% and 44% , respectively ) . Older adults and bovine owners were at elevated risk of infection . Testing only these high-risk human populations yielded sensitivities of 77% and 71% , respectively . Human and bovine schistosomiasis were widespread in regions where schistosomiasis had reemerged but acute schistosomiasis and S . japonicum-infected snails were rare and , therefore , poor surveillance targets . Until more efficient , sensitive surveillance strategies are developed , direct , targeted parasitological testing of high-risk human populations should be considered to monitor for schistosomiasis reemergence .
The success of disease control programs in reducing schistosomiasis infections and morbidity have prompted consideration of the elimination of human schistosomiasis [1] , [2] . Dramatic declines in Schistosoma haematobium and S . mansoni have been observed following widespread distribution of the antihelminthic drug , praziquantel , in six countries in sub-Saharan Africa [3]–[5] . Disease control efforts in China , including a ten-year partnership with the World Bank to promote treatment , have led to the interruption of S . japonicum transmission in 5 of 12 endemic provinces and 60% of endemic counties [6] , [7] . Currently , China is aiming to eliminate schistosomiasis , setting an initial goal of reducing human and bovine infection prevalence below 1% in every endemic region by 2015 [8] . If successful , China's program may serve as a model for schistosomiasis control elsewhere . However , schistosomiasis has reemerged in previously controlled regions , highlighting the challenges of sustaining reductions in infections . In Sichuan , China , schistosomiasis was identified in 8 of 46 counties that had met Chinese Ministry of Health criteria for transmission control , which require the reduction of human and bovine infection prevalence below 1% in every endemic village [9] . Nationwide , 38 counties that have met transmission control criteria have been reclassified as reemerging [7] . In the absence of a vaccine or lasting immunity , and with at least forty competent mammalian reservoirs , S . japonicum reemergence remains a threat in controlled regions [10] . Little is known about the epidemiology of reemerging schistosomiasis , including how infections are distributed across human populations , other mammalian reservoirs and intermediate host snails . Surveillance systems that can identify areas where lapses in control have occurred are an essential component of disease elimination strategies [11] , [12] . They can enable timely treatment of infected populations and interventions to prevent further spread of infections . In China , surveillance for schistosomiasis in controlled regions includes hospital-based surveillance for acute schistosomiasis , surveys for the intermediate host snail , Oncomelania hupensis , and direct testing of the human population [13] . Acute schistosomiasis is triggered by the migration of the parasite through the body shortly after infection , leading to rapid onset of symptoms including high fever , myalgia and eosinophilia [14] . Due to the quick and severe onset , acute schistosomiasis , which is a reportable disease in China , can serve as a sentinel event , signaling the reemergence or emergence of schistosomiasis , as occurred in the Yangtze River valley following flooding events and in Sichuan province [9] , [15] , [16] . But acute schistosomiasis is rare , comprising less than 1% of all schistosomiasis cases [17] . It is possible for schistosomiasis to reemerge more quietly , as schistosomiasis typically induces chronic morbidity [18]–[20] , leading to uncertainties about the sensitivity of surveillance methods that rely on acute schistosomiasis case reports . Similarly , surveillance for schistosome-infected snails and children is recommended in regions approaching schistosomiasis elimination , but how well these surveillance targets can identify areas where human infections are present remains uncertain [21] . In an effort to inform surveillance for S . japonicum reemergence , we examined the distribution of S . japonicum infections in human , bovine and snail populations in regions where schistosomiasis had reemerged . We then evaluated the ability of active and passive surveillance methods , including acute schistosomiasis case reporting , surveys for the presence of O . hupensis , and surveys for S . japonicum infections in O . hupensis , cows , water buffalo , and high-risk human populations , to identify villages where human infections were present .
In June 2007 , all residents aged six years and older in the 53 selected villages were invited to complete a brief survey about their age , sex , occupation , highest level of schooling , travel and schistosomiasis treatment history . The head of each household was also asked to complete a detailed questionnaire describing household agricultural practices , ownership of domestic animals and socioeconomic indicators . Given the challenges of estimating income in agrarian regions , household socioeconomic status was assessed based on household assets [22] . The head of each household was asked if any member of his or her household owned a car , tractor , motorcycle , computer , television , washing machine , air conditioner or refrigerator . A household asset score was assigned based on the number of assets owned . In 2008 , attempts were made to interview any participant in the human infection survey missing household or individual interview data from 2007 . Questionnaires were pilot-tested to ensure questions were appropriate for the study region . Interviews were conducted by trained staff at the Institute of Parasitic Diseases ( IPD ) , Sichuan Center for Disease Control and Prevention and the county Anti-schistosomiasis Control Stations fluent in Sichuan dialect , which is spoken by the study population . Household and individual interviews were scanned using optical mark recognition software ( Remark Office OMR , Gravic Corporation , Malvern , PA ) . Approximately 10% of scanned questionnaires were checked against paper records to ensure data accuracy . In November and December 2007 , all residents aged 6 to 65 years were invited to submit three stool samples from three consecutive days which were analyzed using the miracidium hatching test and the Kato-Katz thick smear procedure [23] , [24] . Of 3 , 009 participants , three stool samples were collected from 2 , 504 participants ( 85% ) , two samples from 7% and one sample from 8% . Samples were collected from villages daily and brought to a central laboratory in each county where they were stored out of direct sunlight until processing ( 90% were processed within one day of collection ) . The miracidium hatching test was used to examine each stool sample . Approximately 30 g of stool were suspended in aqueous solution , strained with copper mesh to remove large particles , then strained with nylon mesh to concentrate schistosome eggs . This sediment was re-suspended and left in a room with ambient temperatures between 28 and 30°C . Two , five and eight hours after preparation , samples were examined for the presence of miracidia for at least two minutes each time . Using the Kato-Katz thick smear procedure , three slides were prepared using 41 . 7 mg of homogenized stool from the first sample submitted by each participant . Three slides were prepared for 97% of infection survey participants ( two slides were prepared for 21 participants , one slide was prepared for 21 participants and no slides were prepared for 55 participants ) . Each slide was examined using a dissecting microscope and if any S . japonicum eggs were detected , the species and number of eggs was confirmed by a second reader . Infection intensity , expressed in eggs per gram of stool ( EPG ) , was calculated as the total number of S . japonicum eggs divided by the total sample weight . A person was classified as infected if the miracidium hatching test was positive or at least one egg was detected using the Kato-Katz technique . The domestic bovines , water buffalo ( Bubalus bubalis ) and cows ( Bos taurus ) , were tested for S . japonicum infection at the same time as the human surveys . Attempts were made to collect three stool samples from all bovines in study villages by keeping the animal in a pen or tied until stool was produced on three separate days . Samples were collected shortly after defecation and from the center of fresh stool samples in order to minimize potential contamination . Three samples were collected from 68% of the 537 bovines tested , two samples from 18% and one sample from 14% . Each sample was examined using the miracidium hatching test as described above . Due to the rapid hatching and short survival of miracidia in bovine stool , samples were examined one , three and five hours after preparation and 99% of samples were processed within one day of collection . Bovines were classified as infected if at least one hatching test was positive for S . japonicum . Bovines with at least one positive hatching test were subsequently examined using an adaptation of the Danish Bilharziasis Laboratory ( DBL ) method in order to estimate infection intensity [25] . Briefly , 5 g of homogenized stool were washed through a series of three sieves ( mesh size: 400 µm , 100 µm and 45 µm ) . The material in the 45 µm sieve was washed into a sedimentation tube , two drops of formalin were added and the suspension was left in the dark to sediment . The solution was centrifuged and the top half of the liquid gently decanted . The remaining sediment was re-suspended , adding enough water to create 10 mL of solution , re-centrifuged , and the top 80% of the solution gently decanted in order to obtain 2 mL of solution . A thick smear approach was used to count the eggs . Infection intensity , expressed in EPG , was calculated as the total number of eggs divided by the sample weight . In April 2007 , all irrigation ditches in the study villages were surveyed for O . hupensis . Teams of trained IPD and county Anti-Schistosomiasis Control Station staff with extensive experience conducting snail surveys collected samples at 10 m intervals along irrigation ditches . At each location , a square frame ( kuang ) measuring 0 . 11 m2 was placed at the waterline and all O . hupensis snails within the frame were collected . In addition , snails were sampled from 10 terrace walls per village ( or all terrace walls if there were fewer than 10 terraces ) , since the lower portions of terrace walls accumulate moisture and may provide suitable habitat for snails . The sampling frame was placed at the base of the terrace walls in three locations: the middle and both ends of the terrace and all snails within the frame were collected . Collected snails were deposited in paper envelopes and brought to the laboratory where they were crushed between two glass slides and inspected for cercariae using a dissecting microscope . All adult participants provided written , informed consent before participating in this study . All children provided assent and their parents or guardians provided written , informed permission for them to participate in this study . The research protocol was approved by the Sichuan Institutional Review Board and the University of California , Berkeley , Committee for the Protection of Human Subjects . Each person who tested positive for S . japonicum was provided treatment with 40 mg per kg of praziquantel tablets by the county Anti-Schistosomiasis Control Station . Because bovine stool samples were collected after they were excreted , the Animal Care and Use Committee at the University of California , Berkeley determined the protocol was exempt from review . All bovines testing positive were referred to the county veterinary station for treatment with praziquantel . The reporting of this cross-sectional study was evaluated using the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) checklist ( Checklist S1 ) . In order to identify subpopulations at high risk of S . japonicum infection in reemerging areas , human infection prevalence and intensity were examined across 11 demographic variables: age , sex , occupation , educational attainment , socioeconomic status , time spent out of the village , bovine ownership and whether members of the household plant rice , corn , wheat or rapeseed . Similarly , bovine infection prevalence was examined across species , sex and age ( as reported by owner ) . Variables were selected based on their potential to affect exposure to S . japonicum , and the ease with which surveillance teams could identify individuals based on the selected characteristics . For each demographic variable , we estimated an odds ratio ( OR ) and 95% confidence interval ( CI ) using logistic regression , modeling infection status as a function of the demographic variable , adjusting for county of residence and village HR status . For humans , we also tested the hypothesis that , among the infected , infection intensity is predicted by these same 11 demographic variables . We estimated the arithmetic mean EPG for each subpopulation . We modeled infection intensity , in EPG , as a negative binomial distribution because even among the infected , infection intensity was overdispersed as is common for helminthic infections ( note that 55% of those who tested positive for S . japonicum had zero detectable eggs , testing positive by the miracidium hatching test only ) . Logistic and negative binomial models accounted for correlation of infections within villages using generalized estimating equations ( GEE ) with exchangeable correlation and with inference from robust variance estimates [26] , [27] . We evaluated five types of schistosomiasis surveillance methods for their ability to identify villages where human infections were present: acute schistosomiasis case reports , surveys for the presence of O . hupensis , and surveys for S . japonicum infections in O . hupensis , bovines and high-risk human populations . The sensitivity and specificity of each method was calculated using the human infection survey results as the gold standard: villages where at least one person tested positive for S . japonicum were classified as true positives , villages where all people tested negative for S . japonicum were classified as true negatives . Bootstrapping was used to estimate the variability around each point estimate [28] . We drew 1 , 000 random samples of the 53 villages with replacement , estimated the sensitivity and specificity for each sampled population , and the 2 . 5th and 97 . 5th percentile values were used to generate nonparametric 95% CIs . The sensitivity and specificity of testing high-risk human populations were estimated for characteristics that were significant predictors of human infection status and that defined less than 50% of the population . The village selection procedure involved an oversampling of villages where surveillance records indicated reemergence generally and where acute cases had been reported , in particular . Failure to account for this selection procedure can lead to biased estimates of the sensitivity and specificity of acute case reporting . We therefore applied differential weights in estimating the sensitivity and specificity of acute case reporting . Villages where acute cases were present were weighted by where n is the total number of villages formerly endemic for schistosomiasis and na is equal to 25 , the number of villages where one or more acute schistosomiasis cases were reported in the three counties . Villages where acute cases were not present were divided into two subgroups: HR villages where no acute cases were found , which were weighted using , and NHR villages , weighted using where np is equal to 112 , the number of formerly endemic villages where surveillance records indicated reemergence . Both np and na were directly measured through our examination of surveillance records . The number of previously endemic villages , n , is not known precisely and was estimated , based on conversations with county schistosomiasis control officers , to be 1 , 000 . Given the uncertain value of n , we conducted a sensitivity analysis , estimating sensitivity and specificity setting n to lower and upper bound estimates ( 500 and 5 , 000 villages , respectively ) . Tests of statistical significance were conducted setting α = 0 . 05 . Data analysis was conducted using Stata , version 10 ( StataCorp , College Station , TX , USA ) and R , version 2 . 9 . 2 ( www . r-project . org ) .
Beginning in June 2007 , we enrolled 4 , 399 study participants in 53 villages and 1 , 784 heads of household completed the household questionnaire . There were , on average , 83 participants per village ( range: 30 , 169 ) . Most adults ( ≥18 years ) were farmers ( 96 . 5% ) as shown in Table 2 . Rice and corn were the principal crops planted in the summer months ( typically , May to September ) , whereas rapeseed and wheat were the major crops planted during the winter growing season ( typically , October to April ) . Socioeconomic status was modest: 61 . 3% of individuals reported their household owned no more than two of eight specified household assets . The most commonly reported asset was a television , owned by 94 . 4% of households , followed by a washing machine ( 55 . 5% ) , motorcycle ( 33 . 1% ) and refrigerator ( 16 . 5% ) . Tractors ( 2 . 8% ) , air conditioners ( 2 . 0% ) , cars ( 1 . 7% ) and computers ( 0 . 5% ) were rare . There were few teenagers and young adults among the study population relative to other age groups ( Figure 1 ) . Residents reported many people , particularly younger populations , had left their villages to find work in urban areas . There is also a dip in the population corresponding with the birth years 1959 to 1961 , the years of the Chinese famine . Travel outside of the village was common among study participants . Most young adults ( aged 18–29 years ) reported spending at least one month out of their village in the past year , as did 40% of adults aged 30–39 years and 26% of adults aged 40–49 years . Most of these individuals left to work as laborers . Some teenagers ( aged 12–17 years ) also reported living outside of their village for more than one month in the past year ( 21% ) , primarily to attend school . Schistosomiasis control was ongoing in the study regions and occurred in both HR and NHR villages . Praziquantel treatment in 2006 or 2007 ( prior to the infection survey ) was reported by 45% of residents in HR villages and 40% of residents in NHR villages . County records indicated that the molluscicide , niclosamide , was applied to snail habitats in 23 of the 25 HR villages and 25 of the 28 NHR villages in 2006 . We tested 3 , 009 people from the 53 villages for S . japonicum infection . Participation in the infection surveys ranged from 45% to 97% by village and varied by county , age , occupation and travel out of the village ( Table 2 ) . Participation was lowest for individuals aged 12–29 years . In 2007 , 195 people ( 6 . 5% ) tested positive for S . japonicum , including 159 who tested positive using the miracidium hatching test and 88 who tested positive using the Kato-Katz thick smear procedure . Human infections were detected in 35 villages , including 18 of the 25 HR villages , and in 17 of the 28 NHR villages . Human infection prevalence ranged from 0 to 42 . 9% by village ( Figure 2 ) , and varied by county and HR status ( Table 3 ) . Mean infection intensity was 1 . 6 EPG , and the maximum village infection intensity was 10 . 6 EPG . S . japonicum eggs were clustered in a few individuals: 24% of all eggs detected were excreted by two individuals . We identified several groups at high risk of infection on the basis of demographic characteristics ( Table 3 ) . Infection prevalence was highest in adults , aged 50 years and older , individuals living in households planting rapeseed , individuals in households with one or more bovines and adults with no formal schooling beyond elementary school . These variables were significant predictors of infection status , controlling for county and village HR status . Because most residents in our study region lived in households planting rapeseed ( 86% ) and were adults with less than an elementary school education ( 58% ) , only bovine ownership and older age were examined as target populations for human surveillance ( comprising 37% and 39% of the population , respectively ) . Human S . japonicum infections did not vary by sex , occupation , household asset score or time spent out of the village . Among the infected , infection intensity did not vary significantly by any of the demographic characteristics examined . Infection intensity did increase with age but was highest in adults aged 40 to 49 years , rather than the oldest , most frequently infected age group . We identified 821 cows and water buffalo present in 50 of the 53 villages . We tested 537 ( 65 . 4% ) bovines from 44 villages for S . japonicum infection . The six villages where bovines were present but infection surveys were not conducted had fewer bovines ( mean 5 . 8 bovines , range: 3 , 8 ) compared to villages where infection surveys were conducted ( mean 17 . 9 bovines , range: 2 , 42 ) . Bovine infections were detected in 23 villages , including 15 of the 19 HR villages where bovines were tested and 8 of the 25 NHR villages where bovines were tested . Mean bovine infection prevalence was 13 . 4% , with a maximum village infection prevalence of 65 . 4% ( Figure 2 ) . Like human infection prevalence , bovine infection prevalence varied by county and village HR status ( Table 4 ) . We measured infection intensity in 67 of the 72 bovines positive by the miracidium hatching test: 11 had detectable eggs . While bovine infection prevalence was higher than human infection prevalence , mean bovine infection intensity was lower , ranging from 0 to 0 . 11 EPG by village . Infection prevalence was modestly but not significantly higher in cows compared to water buffalo , adjusting for county and village HR status ( Table 4 ) . Bovine sex did not predict infection status . Water buffalo infections declined with age whereas cow infections increased with age ( Figure 3 ) . We surveyed a total of 15 , 054 locations along irrigation ditches and 2 , 498 locations at the base of terrace walls for snails . O . hupensis were present in 38 of the 53 villages surveyed , found along irrigation ditches in 34 villages and terrace walls in 17 villages . Mean snail density by village was 2 . 3 snails/m2 in irrigation ditches ( range: 0 , 24 . 8 snails/m2 ) and 0 . 6 snails/m2 in terrace walls ( range: 0 , 8 . 5 snails/m2 ) . The density of snails in terraces was not strongly correlated with snail densities in irrigation ditches ( Spearman's rho 0 . 302 ) . Of the 7 , 325 snails collected from irrigation ditches , one infected snail was found . None of the 190 snails collected from terrace walls were infected . Acute case reporting and surveys for S . japonicum-infected snails yielded very low sensitivity: 1% and 3% , respectively ( Table 5 ) . Setting n to 500 and 5 , 000 yielded sensitivities of 3% and 0 . 3% , respectively for acute case reporting . The presence of snails in irrigation ditches yielded higher sensitivity ( 69% ) and sensitivity was improved further when terraces were also sampled ( 74% ) ; however specificity was low ( 44% and 33% , respectively ) . Surveys for S . japonicum-infected bovines had modest sensitivity ( 59% ) and specificity ( 67% ) . Testing only individuals who belong to high-risk groups provided the most accurate indicator of the presence of human infections in a village . Testing individuals aged 50 years and older or individuals in households that own bovines correctly identified 77% and 71% of villages where human infections were present , respectively . Specificity was 100% , as a single infected human was sufficient to designate a village as infected . The use of only one of the two human infection testing methods led to decreased sensitivity . Testing all village residents using three hatching tests from three stool samples yielded a sensitivity of 97% . Testing all village residents by preparing three Kato-Katz slides from a single stool sample yielded a sensitivity of 80% .
Human S . japonicum infections were present in 35 of the 53 villages surveyed , with infection prevalence exceeding 20% in six villages , indicating widespread reemergence of schistosomiasis in areas where it had previously been controlled . Schistosomiasis was also detected in 13% of bovines , suggesting non-human mammalian reservoirs may play a role in the reemergence of schistosomiasis . Two key surveillance strategies , acute schistosomiasis reporting and surveys for S . japonicum-infected snails , grossly underestimated the number of villages where schistosomiasis had reemerged , misclassifying over 90% of villages where human infections were present . Alternative surveillance strategies , including surveys for the presence of the intermediate host snail or S . japonicum infections in bovine populations , yielded modest sensitivity and specificity . Testing high-risk human populations for S . japonicum infection appears to be the most reliable currently available strategy for monitoring the reemergence of human schistosomiasis , apart from testing all at-risk populations . The reemergence of schistosomiasis documented here , in Sichuan province , and elsewhere underscores the challenge of sustaining reductions in human infections . Local elimination of human infections has proven difficult: a rise in S . japonicum infection prevalence was detected in endemic regions of China in the 2004 national infection survey [29] . The dramatic declines in S . haematobium and S . mansoni infection after widespread administration of praziquantel in West Africa were followed by an increase in infections in some areas [30] . China has developed a rigorous process for certifying reductions in infection , a process that accounts for spatial heterogeneity in transmission , the clustering of infections in few individuals and temporal variations in infection prevalence . To attain transmission control every endemic village in a county seeking certification must demonstrate that human infection prevalence is below 1% by testing at least 95% of the at-risk population . Villages are randomly selected for re-testing to confirm first-round results . All of the counties in this study had previously met transmission control criteria and therefore , every village in this study had , at one point , reduced infection prevalence below 1% . The fact that human infections were detected in 35 of these villages , at prevalences reaching 43% , highlights the instability of S . japonicum control , even when very low infection thresholds are attained , and the need for ongoing monitoring for reemergence . The presence of at least 40 competent mammalian S . japonicum hosts , the potential for the parasite to be transported from endemic to controlled areas and the absence of a vaccine or lasting immunity contribute to the difficulty of local schistosomiasis elimination [10] . Bovine infections were present in 25 villages and bovine infection prevalence exceeded human infection prevalence in 18 of these villages , suggesting non-human reservoir species may be an important source of S . japonicum eggs in reemerging regions . The importance of different reservoir hosts appears to vary regionally . While previous studies have not found bovines to be important reservoirs in the endemic , hilly regions of China [31] , [32] or in the Philippines [33] , [34] , in the hilly regions we studied , they may contribute to reemergence risk . Current efforts to replace bovines with tractors may reduce the risks posed by bovines [8] , but , as rodents and dogs have been shown to play an important role in transmission in regions where bovines are absent [31] , bovine removal may lead to a shift in the importance of other mammalian reservoirs . Connections between villages can sustain endemic schistosomiasis transmission and may promote the reintroduction of the parasite through social or hydrological networks [35] . In this study , schistosomiasis reemergence was detected the same year in two adjacent counties , and several villages with high human infection prevalence were located near each other on either side of the county line , suggesting infections may have spread across village and county boundaries . In addition , 34% of residents reported traveling outside of their villages in the past year , providing a possible mechanism for parasite import and export . Treatment of human populations with praziquantel and the application of molluscicides were ongoing in the study region , likely prompted by the discovery of reemergence in these areas . This may explain why no human infections were detected in 7 of the 25 villages with historical evidence of reemergence and why snail populations were lower than observed elsewhere in Sichuan province [32] . However , the number of human and bovine infections detected in the region , despite ongoing treatment , underscores the need for improved surveillance . Given the ongoing threat of schistosomiasis reemergence in controlled areas , timely and accurate detection of lapses in control are necessary to sustain disease control . Several types of surveillance methods have been used to monitor emerging and reemerging infections generally . First , infections may be detected through reporting of an acute presentation of a disease by health care providers . This has been a central strategy in monitoring poliomyelitis: acute flaccid paralysis , like acute schistosomiasis , is severe , rare – seen in approximately 1% of those infected with poliovirus – and occurs shortly after infection , providing a timely marker of renewed transmission [36] . China has a sophisticated disease reporting system [37] , making surveillance for acute schistosomiasis appealing as it requires little additional capital or labor , although underreporting remains a concern . However , acute schistosomiasis reporting yielded poor sensitivity , identifying only 1% of villages where human S . japonicum infections were present . Second , infections may be detected by monitoring non-human reservoirs , vectors or intermediate host populations . Non-human surveillance methods present the opportunity to detect the return of the parasite before human infections have occurred , a benefit that has been recognized in the design of surveillance systems to monitor emerging arboviruses [38] , [39] . Snails , water buffalo , cows and other mammalian reservoirs have the potential to provide a key source of S . japonicum in reemerging regions and therefore may serve as sentinel populations . In our study regions , infected snails were rare , and therefore were a poor indicator of reemergence . Because we found only one infected snail out of over 7 , 000 examined in April 2007 , the snail survey was repeated in half of the villages in September 2007 . Again , only one infected snail was detected . Surveys for the presence of O . hupensis yielded modest sensitivity , but the method yielded numerous false positives . Given the low specificity , surveys for the presence of O . hupensis could be the first step in a two-part screening method that uses a more sensitive method to screen villages where snails are detected . The performance of surveys for the presence of O . hupensis may be different in regions where transmission has been controlled and snail control activities have been halted . Monitoring for the presence of S . japonicum infections in bovines offered modest sensitivity and specificity . Third , surveillance may involve direct testing of human populations at high-risk of infection . Focusing human testing on a high-risk sample of the population as is currently being done to monitor the progress of lymphatic filariasis elimination [40] , can increase the efficiency of surveillance in human populations , reducing the number of samples that need to be collected and tested . In this study , we identified two sentinel populations based on characteristics that can be readily identified by village leaders: older age and bovine ownership; that composed less than 40% of the total population . As this is the first population-level study of human S . japonicum infection in reemerging areas , research in other regions is needed to assess the generalizability of the high-risk characteristics we identified . However , our finding that infection prevalence was highest in older age groups is consistent with other studies of S . japonicum in China and the Philippines [29] , [41] , and supports our conclusion that children make poor surveillance targets for S . japonicum . In contrast , S . haematobium and S . mansoni infections typically concentrate in children and teenagers [42] suggesting that sentinel age groups may vary across schistosome species . Bovines have been shown to play a key role in endemic schistosomiasis transmission in the lakes and marshland regions of China [43] , [44] , suggesting bovine ownership may also indicate human reemergence risk in other regions , but this may not be the case in the Philippines [33] . Schistosomiasis is strongly associated with poverty , thus it is not surprising that higher educational attainment appeared protective against S . japonicum infection [45] , [46] . However , as with rapeseed planting , the highest risk groups comprised over 50% of the population , leading to minimal gains in efficiency . High-risk human population monitoring was the most accurate alternative to testing all at-risk people , outperforming bovine and snail-based surveillance as well as acute schistosomiasis reporting . Longitudinal studies , currently underway , will aid in identifying appropriate sampling intervals , as will mathematical models of reemergence over time and space . An analysis of the costs-effectiveness of different surveillance strategies can further aid in the development of an effective and feasible surveillance protocol in areas approaching schistosomiasis elimination . Sentinel human populations may also be defined by geographic and environmental characteristics . Even within a relatively homogeneous region in terms of demographic characteristics and disease control measures , infection prevalence and intensity may vary widely between villages , as observed in this study and elsewhere [32] , [47] . Local variations in factors such as rainfall , sanitation and intermediate host habitat may promote or impede the acquisition of human parasitic infections and ultimately , the reemergence of human infections [48] , [49] . The characterization of local environments that are at high risk of schistosomiasis reemergence can further refine the definition of human surveillance targets . As schistosomiasis infections decline , highly sensitive diagnostic tests will be needed to identify remaining infections . We used two stool-based testing methods , the Kato-Katz thick smear procedure and the miracidium hatching test to identify human infections . These methods are highly specific but , due to variability in egg excretion by infected individuals , the sensitivities of these tests decline when infection intensity is low [50] , [51] . The use of multiple diagnostic tests and the collection of stool samples on multiple days increase the likelihood of detecting infections . Nonetheless , the infection prevalences detected here are likely lower bound estimates . PCR-based methods to detect schistosome eggs in the stool of human and other mammalian hosts show promise and may provide the sensitivity needed to diagnose very low intensity S . japonicum infections in regions approaching schistosomiasis elimination [52] , [53] . Similarly , PCR-based methods to identify the presence of cercariae in water may aid in the identification of environments where the parasite remains endemic [54] . Pooled analysis of snails , water samples or even mammalian stool for S . japonicum may increase the efficiency of population-based surveillance at low infection intensities [55] . The sensitivity and specificity estimates presented here are calculated for the methods as we performed them . For example , surveys for the presence of O . hupensis in irrigation ditches were conducted by sampling all irrigation ditches in a village at 10 m intervals – if fewer locations are sampled , the probability of finding the snail host , and therefore the sensitivity of the test , may be diminished . Similarly , immunoassays are often used to screen humans for S . japonicum infection in China , and , due to the low specificity of this assay , individuals with a positive immunoassay result are then asked to provide a stool sample for examination using the miracidium hatching or Kato-Katz test , as was done in the 2004 national schistosomiasis survey [29] . As individuals with a negative immunoassay are not screened using coprological methods , this two-step screening produces false negatives from both diagnostic methods , yielding an overall sensitivity lower than that of the immunoassay alone . Targeted sampling of high-risk populations , using an immunoassay based method will yield different sensitivities than those calculated here . Similarly , relative distributions of S . japonicum infections in bovine , snail and human populations may vary regionally , a factor that should be considered when adopting post-control surveillance plans . We attempted to test all humans and bovines in the 53 selected villages for infection , but approximately 30% of the population did not participate . Estimating the true population of each study village , and therefore the true infection survey participation percentage , is difficult . Due to residency requirements , government population registers in rural areas often include families that have moved to urban areas without registering such moves . Conversations with village leaders suggest almost all residents who spent most of their time in the village were captured by the demographic and household surveys . Participation in the infection surveys was lowest among people who spent time out of their village in the past year and young adults . Among those tested , neither young adults nor people who left their village had high infection prevalence . Nonetheless , it is possible that some infections were missed , leading some villages where infections were present only among non-participating residents to be misclassified . The number of villages with human or bovine infections may be greater than reported here . The dramatic reduction of schistosomiasis in China and elsewhere has prompted consideration of the next phase of schistosomiasis control , motivating public health leaders to look beyond morbidity control toward the elimination of human schistosomiasis [1] , [8] , [21] . While this transition marks progress in controlling schistosomiasis , new challenges arise when approaching elimination . The reemergence of schistosomiasis as documented here highlights the transience of reductions in schistosomiasis in some areas . Before the introduction of praziquantel , schistosomiasis control focused on environmental modifications to reduce snail habitat and improve sanitation [2] , [56] . The long-term interruption of schistosomiasis transmission in China and elsewhere will require the integration of praziquantel treatment and alterations to local environments to reduce their potential to sustain the parasite lifecycle [49] , [57] . In addition , it will require a surveillance system that can detect the reemergence of infections with sufficient speed and accuracy to allow interventions to halt renewed transmission and prevent the further spread of infections . | Schistosomiasis has reemerged in China in regions where it was previously controlled . As reductions in schistosomiasis , a water-born parasitic infection , prompt consideration of schistosomiasis elimination , surveillance strategies that can signal reemergence and prevent further lapses in control are needed . We examined the distribution of Schistosoma japonicum , the species that causes schistosomiasis in China , in 53 villages . The villages were located in regions of Sichuan province where schistosomiasis reemergence had been documented by public health authorities . We tested three key reservoirs , humans , cows and water buffalo , and freshwater snails for S . japonicum infection in an effort to identify high-risk populations and evaluate their ability to signal reemergence . Human and bovine infections were common , detected in 35 villages and 23 villages , respectively , but infected snails were rare , found in only one village . Two commonly used surveillance methods , hospital reports of acute schistosomiasis and surveys for S . japonicum-infected snails , grossly underestimated the number of villages where human infections were present . Schistosomiasis was widespread in the region we studied , highlighting the danger reemergence poses to disease elimination programs . Surveillance systems that monitor high-risk populations such as older adults or bovine owners should be considered to promote detection of reemergence . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"public",
"health",
"and",
"epidemiology/epidemiology",
"public",
"health",
"and",
"epidemiology/global",
"health",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] | 2011 | Evaluation of Mammalian and Intermediate Host Surveillance Methods for Detecting Schistosomiasis Reemergence in Southwest China |
The reservoir and mode of transmission of Mycobacterium ulcerans , the causative agent of Buruli ulcer , still remain a mystery . It has been suggested that M . ulcerans persists with difficulty as a free-living organism due to its natural fragility and inability to withstand exposure to direct sunlight , and thus probably persists within a protective host environment . We investigated the role of free-living amoebae as a reservoir of M . ulcerans by screening the bacterium in free-living amoebae ( FLA ) cultures isolated from environmental specimens using real-time PCR . We also followed the survival of M . ulcerans expressing green fluorescence protein ( GFP ) in Acanthameoba castellanii by flow cytometry and observed the infected cells using confocal and transmission electron microscopy for four weeks in vitro . IS2404 was detected by quantitative PCR in 4 . 64% of FLA cultures isolated from water , biofilms , detritus and aerosols . While we could not isolate M . ulcerans , 23 other species of mycobacteria were cultivated from inside FLA and/or other phagocytic microorganisms . Laboratory experiments with GFP-expressing M . ulcerans in A . castellani trophozoites for 28 days indicated the bacteria did not replicate inside amoebae , but they could remain viable at low levels in cysts . Transmission electron microscopy of infected A . castellani confirmed the presence of bacteria within both trophozoite vacuoles and cysts . There was no correlation of BU notification rate with detection of the IS2404 in FLA ( r = 0 . 07 , n = 539 , p = 0 . 127 ) . This study shows that FLA in the environment are positive for the M . ulcerans insertion sequence IS2404 . However , the detection frequency and signal strength of IS2404 positive amoabae was low and no link with the occurrence of BU was observed . We conclude that FLA may host M . ulcerans at low levels in the environment without being directly involved in the transmission to humans .
Mycobacterium ulcerans is a slow growing environmental pathogen responsible for a necrotizing cutaneous infection called Buruli ulcer ( BU ) . The disease has been reported in over 30 countries worldwide mainly in tropical and subtropical climates and emerged as an increasing cause of morbidity in endemic rural communities in some West and Central African countries with Benin , Côte d'Ivoire and Ghana bearing the highest burden of disease [1] . Most BU endemic areas are found close to slow flowing or stagnant water bodies and it is therefore assumed that the aquatic ecosystem may be a source of M . ulcerans from which the bacterium is transmitted to humans . This is supported by several studies that have detected M . ulcerans DNA sequences in a variety of environmental specimens including fish , snails , detritus , biofilms , soil , water filtrands , insects and protozoa [2]–[5] . Recently in Australia , M . ulcerans DNA has been detected in mosquitoes , faecal matter and skin lesions of small terrestrial mammals ( ringtail and brushtail possums ) that are thought to harbor and vector the bacterium [6] , [7] . However , the main reservoir and modes of transmission of BU outside Australia still remain unknown . Since the discovery that Legionella pneumophila is able to infect and replicate in free-living amoebae ( FLA ) [8] , there has been an increasing number of studies on the role of FLA in the survival of pathogenic organisms [9] . Also , several species of mycobacteria ( M . shottsii , M . pseudoshottsii , M . tuberculosis , M . leprae , M . ulcerans , M . marinum , M . bovis , M . avium subsp paratuberculosis and M . avium ) have been shown to survive within protozoa [4] , [10]–[16] . M . ulcerans bears characteristic genomic signatures that are typical of host restricted pathogens suggesting that M . ulcerans is unlikely to be free-living in the environment but is instead undergoing or has undergone adaptation to a specific ecological niche [17] . Internalization of infectious agents inside other parasites is a recurring theme in biology and represents an evolutionary strategy for survival that may sometimes enhance pathogenesis or transmissibility [18]: Bacteria “hidden” in their protozoan hosts may more easily infect vertebrate end hosts , multiplying within protozoans to escape immune reactions [15] , [18] . Water bodies in areas of high BU endemicity have been reported to contain significantly more FLA than in low endemic areas [19] . Recently , we demonstrated that M . ulcerans can be phagocytosed in vitro by Acanthamoeba polyphaga and persist for at least 2 weeks [4] . This study also showed a higher detection frequency of the IS2404 target in FLA cultures as compared to crude samples from the environment . The aim of the present study was to further explore FLA as a reservoir for M . ulcerans by screening M . ulcerans in FLA from aquatic environment sampled for 10 months and relating this to the BU notification rate in the same endemic area . Furthermore , we experimentally investigated the ability of M . ulcerans to survive and replicate within A . castellanii by infecting these amoebae with M . ulcerans expressing green fluorescence protein ( GFP ) .
The study was carried out in five endemic communities ( with recorded human BU cases ) : Ananekrom , Nshyieso , Serebouso , Dukusen and Bebuso , and two non-endemic communities ( no recorded human BU cases ) : Mageda and Pataban in the Asante Akim North Municipal of Ghana ( Table 1 , Fig . 1 ) . These communities are on average 18 km apart and were selected based on number of BU cases reported at the Agogo Presbyterian Hospital ( APH ) , the Municipal health facility serving all communities ( Fig . 1 ) . Week-long monthly field visits were made for 10 months between October 2008 and July 2009 to randomly collect environmental specimens: water , biofilm from plants and tree trunks , detritus and aerosols . The specimens were taken between 6:00 am and 8:00 am , the peak period of human contact activities in the water bodies . Biofilms ( n = 428 ) were taken by scraping surfaces of tree trunks , floating logs and tree stumps with sterile scalpels and cotton swabs into 50 mL sterile Falcon tubes . Detritus ( n = 45 ) were scooped by hand into 50 mL sterile Falcon tubes . Water specimens ( n = 53 ) were taken from mid column with buckets of which 3–10 liters were concentrated via 0 . 45 µm membrane filters ( Sartorius Stedim Biotech GmbH , Germany ) depending on the turbidity of the water . During the last two months of sampling , non-nutrient agar ( NNA ) plates ( n = 13 ) seeded with Escherichia coli were exposed for 30 minutes for isolation of FLA from aerosols generated next to the water bodies . Seven milliliters phosphate buffered saline ( PBS ) were added to the membrane filters ( cut into smaller pieces ) , swabs and scalpels contained in 50 mL Falcon tubes and shaken vigorously to dislodge the substrate and biofilms from the surface . For detritus , specimens were processed as described by Gryseels et al . [4] . A piece of the membrane filter and 2 to 3 drops of the suspensions ( biofilms and detritus ) were inoculated at the centre of 1 . 5% NNA plates seeded with E . coli for cultivation of FLA [20] at 28 . 5°C . The inoculated NNA plates were examined daily for the presence of trophozoites and cysts using the 10× objective of a bright field microscope . When FLA were observed , they were subcultured on new NNA plates seeded with E . coli [21] . After 3 or 4 subcultures , the FLA were harvested by scraping the surface with an inoculating loop and suspending them in 1 . 5 mL sterile distilled water . The modified Boom method was used for the extraction of DNA from FLA cultures as described previously [22] , [23] . Two multiplex real-time PCR assays were performed on the DNA extracts to test the presence of three distinct sequences: IS2404 , IS2606 and the ketoreductase B ( KRB ) domain in the M . ulcerans genome as described by Fyfe et al . [24] . All DNA extracts were first screened for the IS2404 target multiplexed with an internal positive control to check for PCR inhibitors such as humic and fulvic acids ( commonly found in environmental specimens ) [24] . The second PCR assay for detecting the presence of IS2606 and KRB was done on FLA cultures that turned out positive for the IS2404 target . Amplification and detection was carried out using the 7500 real-time PCR system ( Applied Biosystems ) . The identification of FLA of the genera Acanthamoeba , Naegleria and Vahlkampfiidae was confirmed using the primer sets JDP1/JDP2 , ITSfw/ITSrv and JITSfw/JITSrv respectively as described by Gryseels et al . [4] and Eddyani et al . [19] . Specimens were processed to kill extracellular mycobacteria as described by Gryseels et al . [4] , decontaminated using the oxalic acid method [25] , inoculated on LJ medium , and incubated at 30°C with weekly examination for a year . Supernatants containing extracellular mycobacteria were cultured for week at the same temperature to monitor the effectiveness of the isolation method . Direct smear examination ( DSE ) by ZN staining for acid fast bacilli ( AFB ) was performed on single colonies grown on LJ media . Cultures positive for AFB were subjected to a nested PCR specific for the mycobacterial 16S rRNA gene [26] , [27] after heat-inactivation of mycobacterial suspensions in TE for 10 minutes . Amplicons were sequenced by the VIB Genetic Service Facility ( Antwerp , Belgium ) . The sequenced data were compared with known sequences in the GenBank database and interpreted using the BlastN algorithm ( available on http://www . ncbi . nlm . nih . gov/BLAST/ ) . The 16S rRNA sequences were also matched against entries in the RIDOM ( Ribosomal Differentiation of Medical Microorganisms ) database ( http://www . ridom-rdna . de/ ) . Direct detection of mycobacterial DNA in FLA cultures was done using the same 16S rRNA PCR assay . M . ulcerans strains JKD8083 ( which expresses GFP ) and 04126204 ( which does not express GFP ) were grown in 7H9 broth or 7H10 agar supplemented with OADC ( Difco ) . Real-time PCR was performed on M . ulcerans strains to estimate cell numbers as previously described [28] . Primers targeting the 16S rRNA gene were used for detection of mycobacteria . Known amounts of M . ulcerans Agy99 genomic DNA were used to construct a standard curve and cell numbers were estimated based on the predicted mass of an M . ulcerans chromosome [24] . A . castellanii was cultured in peptone-yeast extract-glucose ( PYG ) medium at 22°C in the dark as described by Moffat and Tompkins [29] . Trophozoites were harvested at 400× g for 10 minutes ( Eppendorf , 5810R ) and adjusted to a final concentration of 106 cells ml−1 in Acanthamoebae ( AC ) buffer as described [29] . Bacterial strains were concentrated by centrifugation at 6000× g for 15 minutes at room temperature and then resuspended in AC buffer . A preparation of 1×106 cells of A . castellanii was mixed with 1×106 cells of either M . ulcerans ( JKD8083 ) or ( 04126204 ) in 20 mL PYG broth and incubated at 22°C for 30 minutes . Co-cultures were washed three times in AC buffer and treated with amikacin ( 150 µg/ml ) as described [30] to kill extracellular bacteria . Trophozoites were then washed and resuspended in 50 mL AC buffer . Three milliliters samples were taken at 1 , 2 , 7 , 14 , 21 and 28 days for analysis . At each time point , samples were washed with FACS buffer six times before a final resuspension in 500 µl of FACS buffer . FACS was carried out using uninfected amoebae to identify trophozoite populations . The subsequent infected samples were gated only on these relevant populations . All samples , including the controls ( uninfected amoebae and bacteria only ) were analyzed with a FACS ( Becton Dickinson ) equipped with a 488 nm argon laser . At each time point 50 , 000 events were counted . Background fluorescence was determined by using infections of A . castellanii with non-fluorescent M . ulcerans ( 04126204 ) . Percentages of fluorescing amoebae were then calculated using Flowjo ( v8 . 7 ) . Aliquots of infected amoebae in AC buffer were again pelleted and washed in 1× PBS before DAPI staining according to the manufacturer's instructions ( Invitrogen ) . Samples were imaged using a LAS700 confocal microscope ( Zeiss ) with a 100× oil immersion lens . At different time points 1 mL aliquots were used to examine for M . ulcerans within A . castelanii as described previously [31] using the electron microscope . Statistical analyses were performed in SPSS 18 . 0 ( SPSS Inc . , Chicago , IL ) software . Standard multiple regression was used to investigate whether the isolation frequency of FLA in communities ( water bodies ) was related to the waterbody-specific prevalence of the IS2404 target and mycobacterial DNA ( in those FLA ) . Hierarchical multiple regression was also used to assess the ability of some parameters ( detection of IS2404 positive FLA , detection of mycobacterial DNA in FLA , and isolation of intracellular mycobacteria ) to predict BU notifications ( number of reported cases/number of inhabitants/month ) , after controlling for the influence of time . Logistic regression was performed to assess the influence of time ( months ) on the detection of IS2404 in FLA , isolation of intracellular mycobacteria and frequency of isolated FLA . The relationship between the isolation of FLA and the type of specimen and the communities sampled was investigated using the Pearson product-moment and Spearman Rank Order correlation ( rho ) coefficient . Kruskal-Wallis and Mann-Whitney U Tests were used to compare the isolation frequency of FLA and detection frequency of IS2404 between the different types of specimen and communities . P values <0 . 05 were considered significant .
Five hundred and thirty nine environmental specimens were collected from October 2008 to July 2009 . FLA were cultured from 405 ( 75 . 10% ) specimens . Confirmation using three different PCR primer sets permitted the classification into three genera of FLA from 370 ( 68 . 65% ) specimens with some cultures harboring more than one genus of FLA ( Acanthamoeba [n = 157] , Vahlkamfiidae [n = 306] and Naegleria [n = 118] ) ( Table S2 ) . FLA were isolated from all specimen types ( Table 2 ) , and showed a statistically significant difference across the specimen types , ( χ2 ( 4 , n = 539 ) = 14 . 532 , p = 0 . 006 ) with aerosols recording the highest mean rank ( 354 . 50 ) compared to the other specimen types . A Mann-Whitney U Test also showed that FLA were more frequently isolated from aerosols than plant biofilm ( p = 0 . 006 ) ( Bonferroni adjustment alpha level = 0 . 008 ) . FLA were isolated from all communities , the isolation frequency showed a significant difference across communities ( ( χ2 ( 6 , n = 539 ) = 14 . 955 , p = 0 . 021 ) with Nshyieso recording the highest mean rank ( 308 . 22 ) . A Mann-Whitney U Test showed no significant difference in FLA isolation between Mageda and Nshyieso ( p = 0 . 878 ) but showed a difference between Nshyieso and the communities Dukusen , Ananekrom and Serebouso ( p = 0 . 000 , p = 0 . 002 , p = 0 . 006 ) . Twenty five out of 370 FLA cultures obtained from 539 specimens ( 4 . 64% ) tested positive for the IS2404 target ( Table S1 ) . CT values ranged from 29 . 46 to 38 . 05 corresponding to ≤1–10 genomes µl−1 DNA extract of M . ulcerans . All IS2404 positive FLA cultures tested negative for the IS2606 and KRB targets . The IS2404 target was detected significantly more often in Acanthamoeba and Vahlkampfiidae ( χ2 ( 1 , n = 539 ) = 5 . 532 p = 0 . 019 , phi = 0 . 111 and χ2 ( 1 , n = 539 ) = 4 . 814 p = 0 . 028 , phi = 0 . 103 ) than in Naegleria ( χ2 ( 1 , n = 539 ) = 0 . 259 , p = 0 . 611 , phi = 0 . 033 ) . None of the mycobacterial cultures isolated intracellularly from six of these specimens tested positive for IS2404 . IS2404 positive FLA were detected in all endemic and non-endemic communities ( Table 3 , Fig . 1 ) . There was no significant difference in detection of the IS2404 target from FLA across the specimen types ( χ2 ( 4 , n = 539 ) = 6 . 715 , p = 0 . 152 ) . Some of the IS2404 positive cultures harbored more than one genus of FLA ( Acanthamoeba [n = 13 ( 2 . 4% ) ] , Vahlkampfiidae [n = 20 ( 3 . 7% ) ] and Naegleria [n = 7 ( 1 . 3% ) ] ) . The amoebae had similar ITS sequences to those of Acanthamoeba sp . , A . lenticulata , A . castellanii , Naegleria sp . strain WTP29 , N . lovaniensis , N . philippinensis and V . avara , V . inornata , Acanthamoeba sp . T11 genotype and Acanthamoeba spp . T4 genotype as reported by Gryseels et al . [4] . Three of the positive cultures could not be identified with the primers used ( Table S1 ) . We could not cultivate M . ulcerans from the FLA , however , other mycobacteria were cultured from intracellular origins in 162 ( 30 . 06% ) specimens; 109 ( 67 . 28% ) among these originated from specimens from which we also isolated FLA . All isolates were confirmed by DSE for AFB and partial sequencing of the 16S rRNA gene . There was no growth of bacteria after the supernatants containing the killed extracellular mycobacteria were cultured for a week . One hundred and thirty one isolates showed >99% sequence similarity match with the available database in GenBank ( NCBI and RIDOM 16S rDNA ) comprising a total of 23 mycobacterial species ( Table S3 ) . Eight of the remaining sequence data were too short to be identified and 23 ( 14 . 20% ) isolates had mixed growth , which made identification impossible . The most frequently isolated species were M . arupense ( 39 . 69% ) , M . fortuitum ( 7 . 63% ) and M . lentiflavum ( 4 . 58% ) . After screening the FLA cultures for the mycobacterial 16S rRNA gene , 159 ( 42 . 97% ) of the 370 were positive but mycobacteria were not identified to the species level . The detection of intracellular mycobacteria peaked in April 2009 followed by a peak in the detection of IS2404 positive FLA in June 2009 and the isolation of FLA in July 2009 . The highest number of BU cases was however reported four months later in November 2009 after FLA isolation peaked ( Figure 2 ) . Time accounted for a 13 . 80% variance in the BU notification rate ( new BU cases per month ) using a hierarchical multiple regression model ( F ( 4 , 534 ) = 26 . 00 , p<0 . 001 ) . The three other parameters , intracellular mycobacteria , detection of IS2404 target and detection of mycobacterial DNA in FLA , together explained an additional 2 . 5% of the variance in BU notification rate , after controlling for time , R squared change = 0 . 025 , F change ( 3 , 534 ) = 5 . 251 , p<0 . 001 . In the final model , only two parameters were statistically significant , with time recording a higher beta value ( beta = 0 . 312 , p<0 . 001 ) than detection of mycobacterial DNA in FLA ( beta = 0 . 152 , p<0 . 001 ) . The isolation frequency of FLA varied significantly through time ( ( χ2 ( 1 , N = 539 ) = 28 . 479 , p<0 . 001 ) as well . There was a positive correlation of BU notification rate with detection of mycobacterial DNA in FLA ( r = 0 . 27 , n = 539 , p<0 . 0005 ) but not with detection of the IS2404 target in FLA ( r = 0 . 07 , n = 539 , p = 0 . 127 ) . Using a direct logistic regression model , time accounted for between 5 . 1% ( Cox and Snell R square ) and 7 . 2% ( Nagelkerke R squared ) of the variance in FLA isolation but could not predict the variances in the detection of IS2404 in FLA ( ( χ2 ( 1 , N = 539 ) = 2 . 034 , p = 0 . 154 ) and isolation of intracellular mycobacteria ( ( χ2 ( 1 , N = 539 ) = 0 . 132 , p = 0 . 717 ) . M . ulcerans infections of A . castellanii were performed over a four week period and quantified using flow cytometry . Methods involving the removal of extracellular bacteria using amikacin have been reported previously [4] , [30] and were independently tested here ( Figure 3A ) . M . ulcerans JKD8083 bacteria alone ( 106–108 ) were treated with amikacin for 7 days to test the effect of the antibiotic on extracellular bacteria . Treatment with 150 µg/ml amikacin for 7 days left 18 . 7% of 108 extracellular bacteria ( 5 . 92% of 107 , 1 . 53% of 106 ) fluorescing above background ( Figure 3A ) . Using flow cytometry , 50 , 000 events were counted at each time point . Amoebae were experimentally infected with M . ulcerans by placing them in co-culture at a multiplicity of infection of 1 for 30 min , and killing remaining extracellular mycobacteria with amikacin . At day 0 , 9 . 51% of the amoebae were infected with M . ulcerans , but this percentage gradually decreased until at day 28 ( end of experiment ) only 0 . 7% of amoebae were infected with M . ulcerans ( Figure 3B ) . Figure 3 ( D–F ) shows gated trophozoites following 7 days co-incubation of amoebae alone ( D ) , M . ulcerans JKD8083 ( E ) and M . ulcerans 04126204 ( F ) . Amoebae infected with non-fluorescing bacteria however , fluoresced at levels less than 0 . 05% over the 28-day time course ( Figure 3B ) , therefore we neglected this background fluorescence . Localisation of the bacteria with respect to the amoebae was determined by both fluorescent confocal microscopy and electron microscopy ( Figure 3C; Figure 4 ) . Confocal optical sections demonstrate a mycobacterium within A . castellanii three hours post infection ( Figure 3C ) . Video S1 . shows fluorescing bacteria in A . castelanii at 24 h post infection . Examination of 1 mL aliquots of M . ulcerans-infected trophozoites and cysts by transmission electron microscopy demonstrated an intravacuolar location for M . ulcerans at 3 , 24 and 48 hours post infection ( Figure 4 , Panels C–I ) . Subsequent microscopy on an extended time series reveals the presence of intracellular bacteria within cysts at 22 days post infection ( Figure 4I–J ) .
It has been suggested that M . ulcerans persists with difficulty in the environment as a free-living organism due to its natural fragility [32] and may be maintained in a commensal or parasitic relationship with hosts that protect the bacilli against potentially unfavorable environments . This hypothesis is supported by the observation that M . ulcerans , unlike its close environmental relatives , has degraded genome , with many pseudogenes , such as a mutation in the crt locus . This locus harbors genes responsible for the production of light-inducible carotenoids that affects its ability to withstand exposure to direct sunlight and hence diminishes its capacity to live freely [17] . A previous study by our group detected the IS2404 target twice as often in FLA cultures as in environmental specimens [4] . Extending this study , we investigated the role of FLA and other phagocytic microorganisms as a reservoir of M . ulcerans in the aquatic environment for 10 months and tested the survival of M . ulcerans within A . castellanii in vitro . Acanthamoeba sp . and Vahlkampfiidae sp . were isolated more frequently than Naegleria from the sampled specimens . FLA were isolated more frequently from aerosols and detritus than from biofilms . Acanthamoeba has been implicated in a number of diseases including granulomatous amoebic encephalitis [33] , a cerebral abscess [34] and chronic keratitis [35] . We also isolated potentially pathogenic V . avara , N . canariensis , N . philippinensis and A . lenticulata . In this study , we detected the M . ulcerans IS2404 target in 4 . 64% of FLA cultures , significantly more often in Acanthamoeba sp . and Vahlkampfiidae sp . as has been reported by Gryseels et al . [4] . Our inability to detect the targets IS2606 and KR-B in IS2404 positive FLA was not surprising since most of the CT values of the IS2404 target recorded were indicative of low mycobacterial DNA concentrations as observed in other studies carried out in the same sampling sites [4] , [36] . The importance of protozoa harboring human-pathogenic bacteria has recently been given much attention , especially in the case of fragile bacteria whose environmental phase would be difficult without the protection of a protozoan host [37] . Moreover , phagocytic protozoans such as FLA strongly resemble vertebrate macrophages; and it has been shown that infection success and internal proliferation is enhanced when bacteria such as Legionella and M . avium had previously resided inside protozoans [15] , [38] . A number of intracellular Mycobacterium sp . were isolated from unknown hosts in the specimens , which previously have been shown to live/survive intracellularly in amoebae: M . simiae , M . fortuitum , M . septicum , M . peregrinum , M . terrae , M . gordonae , M . intracellulare and M . lentiflavum [16] , [39] . The most frequently isolated species were: M . arupense , M . fortuitum and M . lentiflavum are potentially pathogenic species . These species were isolated from the environment [40]–[42] and M . arupense was isolated from wild African rodents [43] . Intracellular mycobacteria were more frequently isolated from specimens from which we also isolated FLA that may indicate their role as a reservoir for these mycobacteria . Thomas et al . [37] also reported a significant association between the presence of amoebae and the presence of mycobacteria . FLA have the additional advantage that they can form cysts , which allow them to persist through harsh periods and be dispersed via the air . It has been suggested that some infections can be acquired by inhaling aerosols containing FLA cells filled with bacteria [44] , for example in the case of Legionella [45] . The detection of the IS2404 target in two of the thirteen aerosolized FLA suggests that they may act as vehicles for these mycobacteria . The aerosol transmission hypothesis of M . ulcerans was first postulated by Hayman [46] , but received little attention , due to the unlikelihood of M . ulcerans being airborne as a free-living organism . The possibility that IS2404 positive mycobacteria including M . ulcerans are carried by aerosolized protozoan cysts changes this perspective . More research is , however , needed to explore this transmission route further , and in a subsequent study we investigated the presence of M . ulcerans on the skin of healthy inhabitants in the same endemic communities ( manuscript in preparation ) . BU notification rates varied significantly through time with the highest number of cases recorded in November 2009 . BU prevalence has increased during the last quarter of the year in this locality as well as in some endemic regions of Ghana ( data not shown ) ; in this case there was no community awareness during this period . BU notification rates correlated positively with detection frequency of mycobacterial DNA in FLA cultures but not with detection of IS2404 in FLA cultures , suggesting that detection of IS2404 in FLA cannot predict concurrent BU incidence . However , the time series of this data set was not long enough to test for a potential lagging phase between peaks of IS2404 detection in FLA and BU incidence . Similar to our previous study [4] , over the time course of experimental infection of A . castellanii with M . ulcerans , M . ulcerans is present within amoebae for up to 28 days albeit at low levels . In addition , both electron microscopy and standard fluorescence microscopy revealed the presence of intracellular bacteria within cysts at 22 days post infection ( Figure 4I–J ) . This is not unexpected due to the previously demonstrated presence and survival of a variety of environmental mycobacteria in cysts [39] . The persistence of strong GFP fluorescence of M . ulcerans within A . castellanii throughout the experiment indicated that the mycolactone polyketide synthases genes are abundantly expressed intracellularly , as GFP gene expression is under the control of the mlsA1 promoter [26] . These data also suggest that mycolactone may be produced by the bacteria within the vacuole . While this study was not designed to test the effect of M . ulcerans on A . castellanii , our observations of fluorescing M . ulcerans persisting through 28 days within intact A . castellanii suggest that A . castellanii is not adversely affected by mycolactone or the presence of the bacteria as was also shown for A . polyphaga [4] . The ability of M . marinum to persist within amoebae is widely documented [39] , [47] , [48] . Following the initial time points a decrease in M . ulcerans-infected amoebae as reported previously for M . ulcerans , M . shottsii and M . pseudoshottsii [4] , [10] was seen which suggests that M . ulcerans does not replicate within amoebae and is not as well adapted as M . marinum to resist initial amoebic digestion , but is perhaps able to persist once within the vacuolar compartment by preventing lysosomal maturation of the vacuole by as yet undetermined mechanisms . This study showed the occurrence of the IS2404 marker in FLA , especially in the genera Acanthamoeba and Vahlkampfiidae . After co-culturing amoebae and M . ulcerans the pathogen persisted at low levels suggesting that it probably only transiently occupies FLA and that it is unlikely that protozoa are a long-term reservoir for this pathogen . While the data we present here confirm that FLA can host mycobacteria that harbor the IS2404 marker ( including M . ulcerans ) , the lack of predictive power of detection of IS2404 positive FLA in predicting BU notifications suggests FLA are not directly involved in transmission of M . ulcerans to humans . We suggest future work should focus on reservoirs that act as M . ulcerans amplifiers that link protozoans with humans . | Mycobacterium ulcerans , the causative agent of Buruli ulcer ( BU ) is an environmental pathogen known to reside in aquatic habitat . However , the reservoir and modes of transmission to humans still remain unknown . M . ulcerans can probably not live freely due to its natural fragility and inability to withstand exposure to direct sunlight . This study investigated the hypothesis that free-living amoebae ( FLA ) can serve as a reservoir of M . ulcerans by testing for its presence in amoebae isolated from water bodies in BU endemic and non-endemic communities and whether the pathogen can remain viable when experimentally infected in amoebae in the laboratory . We detected only one ( IS2404 ) of the three ( IS2606 and KRB ) targets for the presence of M . ulcerans in amoebae cultures and found no correlation between its presence in the environment and BU notification rate . M . ulcerans remained viable at low levels in amoebae for 28 days in vitro . We therefore conclude that FLA may host M . ulcerans at low levels in the environment without being directly involved in the transmission to humans . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"ecology",
"biology",
"and",
"life",
"sciences",
"microbiology",
"molecular",
"biology",
"parasitology"
] | 2014 | Investigating the Role of Free-living Amoebae as a Reservoir for Mycobacterium ulcerans |
Elucidating gene regulatory network ( GRN ) from large scale experimental data remains a central challenge in systems biology . Recently , numerous techniques , particularly consensus driven approaches combining different algorithms , have become a potentially promising strategy to infer accurate GRNs . Here , we develop a novel consensus inference algorithm , TopkNet that can integrate multiple algorithms to infer GRNs . Comprehensive performance benchmarking on a cloud computing framework demonstrated that ( i ) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and ( ii ) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction . These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network . Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks , i . e . , if expression-data associated with known regulatory network is similar to that with unknown regulatory network , optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network . Based on this observation , we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that , if similarity between the two expression datasets is high , TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset . The consensus framework , TopkNet , together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks .
Most genes do not exert their functions in isolation [1] , but make their functions through regulations among them . Such regulatory interactions are in the same cell , between different cells , and even between different organs , forming large-scale gene regulatory networks ( GRNs ) . The impact of genetic abnormality can spread through regulatory interactions in GRNs and alter the activity of other genes that do not have any genetic defects [2] . Analyses of GRNs are key to identify disease mechanisms and possible therapeutic targets for the future [1] . Therefore , reconstruction of accurate and comprehensive GRNs from genome-wide experimental data ( e . g . , gene expression data from DNA microarray experiments ) is one of the fundamental challenges in systems biology [3] , [4] . A plethora of algorithms have been developed to infer GRNs from gene expression data , i . e . , mutual-information ( MI ) based algorithms [5]–[12] , correlation-based algorithms [5] , Bayesian networks ( BNs ) [13]–[17] , regression-based algorithms [18]–[22] , graphical gaussian model ( ggm ) [23] , meta predictors that combine several different methods [24] , [25] , and several other approaches that were recently proposed [26]–[32] , i . e . , random forests based algorithm [26] ( GENIE3 ) and two-way ANOVA based algorithm [27] ( ANOVA ) . Each network-inference algorithm generates a confidence score for a link between two genes from expression data and assumes that a predicted link with higher confidence score is more reliable . Systematic and comparative assessment of the performance of these inference algorithms remains a major challenge in network reconstruction [33] . Several studies compared performances of the network-inference algorithms [7] , [8] , [9] , [34] . Especially , the DREAM5 ( Dialogue on Reverse Engineering Assessment and Methods ) challenge evaluated performances of many and diverse network-inference algorithms ( 29 algorithms submitted by challenge participants and 6 commonly used “off-the-shelf” algorithms ) by using benchmark dataset composed of large-scale Escherichia coli , Saccharomyces cerevisiae , and in silico regulatory networks and their corresponding expression datasets [35] . The evaluation demonstrated that no single individual algorithm performs optimally across all the three expression-datasets , i . e . , GENIE3 and ANOVA perform optimally for E . coli dataset , while two algorithms based on regression techniques are optimal for in silico dataset . Further , algorithm-specific biases influence the recovery of different regulation patterns , i . e . , MI and correlation based algorithms can recover feed-forward loops most reliably , while regression and BNs can more accurately recover linear cascades than MI and correlation based algorithms [35] . Above observations suggest that different network-inference algorithms have different strengths and weaknesses [33] , . A natural corollary to the observations is that combining multiple network-inference algorithms may be a good strategy to infer an accurate and comprehensive GRN . Recently , Marbach et al . proposed a new network-inference algorithm , “Community Prediction” , by combining several network-inference algorithms that were submitted to DREAM5 challenge [35] . The Community Prediction combining 29 algorithms ( “off-the-self” algorithms are not used ) shows higher or at least comparable performance to the best among the 29 algorithms across all DREAM5 datasets . Further , performance of community prediction increases as the number of integrated algorithms increases . Thus , community prediction based on integration of many algorithms can be a robust approach to infer GRNs across diverse datasets and will provide a powerful framework to reconstruct unknown regulatory networks . Analysis of DREAM5 results [35] reveal that algorithms complement each other in a context-specific manner and harnessing the combined strengths and weaknesses of diverse techniques can lead to high quality inference networks . Thus , it is important to analyze the anatomy of diversity and quantify it . This is particularly important to systematically evaluate the characteristics of individual techniques and leverage their diversity in finding an optimal combination set for a specific experimental data context . Recently , Marbach et al . showed that integration of algorithms with high-diversity outperform that with low-diversity [35] . However , their diversity analysis is qualitative and , to our knowledge , there is no measure to quantify algorithm diversity . Analysis of small in silico datasets of the DREAM3 challenge demonstrated that integration of the best five algorithms outperforms integration of all algorithms submitted to the challenge [33] . Selection of optimal algorithms for a given expression data and integration of the selected optimal algorithms may be more powerful strategy to reconstruct accurate GRNs than using many algorithms . Development of a method to determine optimal algorithms is a key to reconstruct accurate and comprehensive GRNs , although it is difficult to identify beforehand optimal algorithms for reconstruction of an unknown regulatory network because of biological and experimental variations among expression datasets . A measure to quantify similarity among gene-expression datasets could be a clue to determine the optimal algorithms for reconstruction of unknown regulatory networks . This is because , if expression-data associated with known regulatory network ( e . g . , the DREAM5 datasets ) is similar to that with unknown regulatory network , optimal algorithms for data with known regulatory network could be also optimal for data with unknown regulatory network . Motivated by the above observations and issues , this paper focuses on four strategies towards building a comprehensive network reconstruction platform – To investigate these possible strategies , we first develop a novel network-inference algorithm that can combine multiple network-inference algorithms . Second , to evaluate inference performances of the algorithms precisely , we used the DREAM5 datasets composed of E . coli and S . cerevisiae transcriptional regulatory networks and their corresponding expression data from large-scale microarray experiments , together with synthetic network and corresponding expression datasets ( http://wiki . c2b2 . columbia . edu/dream/index . php/D5c4 ) . A cloud-based computing framework was developed on the Amazon Web Services ( AWS ) system to systematically benchmark the large data-sets and compute-intensive algorithms . Third , we define a mathematical function quantifying diversity between algorithm pairs to analyze the anatomy of diversity and its role in improving the performance of reverse engineering techniques . Finally , we present a similarity measure among expression-datasets and its potential to identify optimal algorithms for reconstruction of unknown regulatory networks .
Network inference algorithms have increased following Moore's Law ( doubling every two years ) [33] , [37] . Consequently , it has become increasing important to develop comprehensive performance benchmarking platforms to compare their relative strengths and weaknesses and leverage them to improve quality of inferred network . Two key components fundamental to performance assessment are representative metrics to quantify performance and standardized data sets on which to evaluate them . In this section , we first outline these components employed in this study on the basis of which the performance of TopkNet is evaluated . To evaluate how TopkNet leverages diversity amongst the candidate algorithms to infer consensus network , we used the DREAM5 benchmarking data comprised of large scale synthetic and experimental gene regulatory networks for E . coli and S . cerevesiae as outlined in Table 1 and computed different performance metrics on them . As seen in the PR and ROC curves for in silico , E . coli , and S . cerevisiae datasets ( Supplementary figures S2 and S3 ) , Top6net shows constantly higher performance compared to community prediction . Other three performance metrics ( AUC-PR , AUC-ROC , and Max f-score ) of TopkNet with k = 5–8 are also higher than those of community prediction for all the three datasets ( see Figures 2B–J ) . Thus , overall score of TopkNet with k = 5–8 is significantly higher than that of community prediction ( see Figure 2B ) . However , the performance metrics of TopkNet is only comparable to the best individual algorithms and not significantly better . Community prediction also showed significantly lower performances than the best individual algorithm . These results indicate that , while TopkNet would provide better strategy to integrate multiple algorithms than community prediction , such a strategy does not always significant increase in performance compared to the cost of integration . As seen in this section , the overall score of the best individual algorithm ( 40 . 279 ) is comparable to that of TopkNet with k = 5–7 ( 40 . 110–41 . 251 ) and is much higher than that of community prediction and TopkNet with k = 1 ( 30 . 228 and 10 . 432 , respectively ) . This is because , for the DREAM5 datasets , several low-performance algorithms assign high confidence scores to many false-positive links and such false links could decrease the performance of TopkNet ( especially , with k = 1 ) and community prediction algorithms . Thus , by integrating only high-performance algorithms that tend to assign high confidence score to true-positive link , TopkNet ( especially , with k = 1 ) and community prediction may show much higher performances than the best individual algorithms . To investigate this issue , we evaluate TopkNet ( and community prediction ) based on integration of 10 optimal algorithms ( algorithms within top 10 highest AUC-PR ) for each of the in silico , E . coli . and S . cerevisiae datasets . As seen in Supplementary figures S4 and S5 , PR and ROC curves of Top1Net are constantly over those of the best individual algorithm and community prediction for in silico and E . coli datasets , although , for S . cerevisiae , PR-curve of the best individual algorithm slightly over that of Top1Net . Other three metrics ( AUC-PR , AUC-ROC , and Max f-score ) of TopkNet with low k ( k = 1 for in silico and E . coli and k = 2 for S . cerevisiae ) are significantly higher or at least comparable to those of the best individual algorithm and community prediction ( see Figures 3B–J ) . Therefore , the overall score of TopkNet with k = 1 and 2 ( 74 . 935 and 73 . 261 , respectively ) are significantly higher than that of the best individual algorithm ( 40 . 279 ) and community prediction ( 56 . 158 ) ( see Figure 3A ) . These results highlight that integration of multiple high-performance algorithms by Top1Net or Top2Net consistently reconstructs the most accurate GRNs for different datasets . As demonstrated in this section , selection of optimal algorithms for a given expression data and Top1Net , Top2Net , and community prediction based on integration of the selected optimal algorithms could be a powerful approach to reconstruct high-quality GRNs . However , currently , to our knowledge , there is no method to determine beforehand optimal algorithms for expression data associated with an unknown regulatory network . Development of a method to determine optimal algorithms is a key to reconstruct unknown regulatory networks ( We investigate this issue in the next section ) . Different network-inference algorithms employ different and often complementary techniques to infer gene regulatory interactions from an expression dataset . Therefore , a consensus driven approach , which leverages diversity in network-inference algorithms , can infer more accurate and comprehensive GRNs than a single network-inference algorithm . However , as demonstrated in this study , a simple strategy of increasing the number of algorithms may not always yield significant performance gains compared to the cost of consensus , i . e . , the computation cost ( CPU time and memory usage ) . It is pertinent to analyze the anatomy of diversity between different algorithms in a theoretical framework to answer the questions of - For the purposes , Marbach et al . conducted principal component analysis ( PCA ) on confidence scores from 35 network-inference algorithms [35] . They mapped 35 algorithms onto 2nd and 3rd principal components and grouped the algorithms into four clusters by visual inspection . The analysis demonstrated that integration of three algorithms from different clusters shows higher performance than that from the same cluster . It indicates that the diversity signature of the selected algorithms , and not just the number of algorithms , plays an important role in the performance of the network reconstruction techniques . However , their algorithm diversity is qualitative and , to our knowledge , there is no quantitative measure for algorithm diversity . In order to quantify diversity among the individual algorithms employed in this study , we developed two quantitative measures of diversity which calculates distance between algorithms pairs on the basis of confidence scores of regulatory interactions inferred by the algorithms . One is based on simple Euclidean distance ( EUC distance ) and the other is based on EUC distance on 2nd and 3rd components from PCA analysis ( PCA distance ) ( see Materials and Methods for details ) . In Figure 4 , we provide a toy model to explain how diversity among network-inference algorithms is defined . By using the diversity measures , we calculated distance among 10 optimal algorithms for each of the DREAM5 datasets to examine whether bringing quantified algorithm diversity into Top1Net ( and Community prediction ) improves the performances of network reconstruction . Based on the calculated distances , we defined high-diversity pairs as top 10% of algorithm pairs with highest distance , while low-diversity pairs are defined as bottom 10% of algorithm pairs with lowest distance . In this study , we have 45 algorithm pairs among 10 optimal algorithms and thus top 5 algorithm pairs with highest distance are high-diversity pairs , while bottom 5 algorithm pairs with lowest distance are low-diversity pairs . Next , we evaluated the performances of Top1Net ( or community prediction ) based on integration of high-diversity pairs and those of low-diversity pair . As seen in Figures 5B–J and Supplementary figures S6B–J , AUC-PR , AUC-ROC , and max f-score of high-diversity pairs by EUC distance are higher or at least comparable to those of low-diversity pairs by EUC distance across all datasets . Especially , for in silico and E . coli datasets , AUC-PR and Max f-score of high-diversity pairs by EUC distance are significantly higher than that of low-diversity pairs by EUC distance . Thus , the overall score of high-diversity pairs is also significantly higher than that of low-diversity pair ( P<0 . 05 ) ( see Figure 5A and Supplementary figure S6A ) . The performances of high-diversity pairs by PCA distance are also higher or at least comparable to those of low-diversity pairs by PCA distance ( see Supplementary figures S7 and S8 ) . Furthermore , median value of the overall score of high-diversity pairs ( 47 . 725 and 50 . 250 by Top1Net , for EUC and PCA distances , respectively ) are much higher than that by the best individual algorithms ( 40 . 279 ) and that by community prediction that integrates 38 network-inference algorithms ( 30 . 228 ) . In summary , these results indicate that - , Top1Net or Top2Net based on integration of highest-performance algorithms consistently reconstruct the most accurate GRNs , as demonstrated in the previous section ( see Figure 3 ) . However , as Marbach et al . mentioned , “Given the biological variation among organisms and the experimental variation among gene-expression datasets , it is difficult to determine beforehand which methods will perform optimally for reconstruction an unknown regulatory network” [35] , and , to our knowledge , there is no method to select the optimal network-inference algorithms . Development of a method to select optimal network-inference algorithms for each of the expression datasets remains a major challenge in network reconstruction . A measure to quantify similarity among expression datasets can be a key to select optimal network-inference algorithms for each of the datasets , because , if similarity between expression-data associated with known regulatory network ( e . g . , DREAM5 datasets ) and that with unknown regulatory network is high , optimal algorithms for the known dataset can be repurposed to infer regulatory network from unknown dataset . Driven by this observation , we developed a similarity measure among gene-expression datasets based on algorithm diversity proposed in previous section . First , we briefly explain the overview of the procedure to calculate similarity among expression datasets ( see Figure 6 and Materials and Methods for the details ) . The procedure is composed of 4 steps . ( 1 ) The expression datasets were split into a dataset for which optimal algorithms are unknown ( e . g , Data1 in Figure 6A ) and datasets for which optimal algorithms are known ( e . g . , Data2 and Data3 in Figure 6A ) . ( 2 ) For each of the datasets , confidence scores of links were calculated by network-inference algorithms . In the example shown in Figure 6B , each of 5 algorithms calculates 6 confidence scores for 6 links . ( 3 ) By using the confidence scores calculated in the step ( 2 ) , diversity among algorithms was calculated based on a distance measure proposed in the previous section ( EUC and PCA based distances , see Figure 6C and Materials and Methods for details ) , for each of the datasets . In the example shown in Figure 6C , we have 10 algorithm pairs among 5 algorithms and thus , as shown in matrices in the figure , we have 10 distances between two algorithms for each of the three datasets . ( 4 ) By using algorithm diversity calculated in the step ( 3 ) , we calculated correlation coefficient of the algorithm distances between two datasets ( see Figure 6D ) . In terms of algorithm diversity , the correlation coefficient is regarded as similarity measure between the two datasets . In the example shown in Figure 6D , Data1 is more similar to Data2 than Data3 . Thus , optimal algorithms for Data2 are better fit than those for Data3 to infer GRN from Data1 . Next , to evaluate whether dataset similarity can be used to govern optimal selection of inference algorithms , we calculated the similarity among the DREAM5 gene-expression datasets and compared the performance of the algorithms across the datasets . As seen in Figure 7 and Table 2 , correlation between S . cerevisiae and E . coli datasets ( Spearman's correlation coefficient ρ = 0 . 99 ) is much higher than that between E . coli and in silico ( ρ = 0 . 87 and 0 . 81 by EUC and PCA distances , respectively ) and that between S . cerevisiae and in silico ( ρ = 0 . 83 ) . In terms of algorithm diversity , similarity between E . coli and S . cerevisiae datasets is much higher than that between E . coli and in silico and that between S . cerevisiae and in silico . Further correlation of algorithm performances between dataset pair with high similarity ( e . g . , E . coli and S . cerevisiae pair ) is higher than that between dataset pair with low similarity ( e . g . , in silico and E . coli pair and in silico and S . cerevisiae pair ) ( see Supplementary figure S7 and Supplementary Table S2 ) . These results indicate that , for dataset pair with high similarity , optimal network-inference algorithms for one dataset also tend to be optimal for the other dataset . From above observations ( observations in Figure 7 , Supplementary figure S9 , Table 2 , and Supplementary table S2 ) , we hypothesized that , if similarity between the two expression-datasets is high , integration of algorithms that are optimal for one dataset could perform well on the other dataset . To examine this issue in more detail , we integrated algorithms that are optimal for S . cerevisia dataset ( algorithms with 10 highest AUC-PR values on the dataset ) and those for the in silico dataset and evaluated their performance of these two integrations against E . coli dataset . As seen in Figures 8A , B , and C , against the E . coli dataset , performances ( AUC-PR , AUC-ROC , and max f-score ) of optimal integration from S . cerevisiae dataset ( green lines ) are generally higher than those from in silico dataset ( red lines ) . Further , against the S . cerevisiae dataset , we evaluate performances of optimal-algorithm integration from E . coli dataset and that for in silico dataset and found that optimal integration from E . coli dataset ( green lines ) generally outperform that from in silico dataset ( red lines ) ( see Figures 8D , E , and F ) . Because similarity between S . cerevisiae and E . coli datasets are much higher than that between E . coli and in silico datasets and that between S . cerevisiae and in silico datasets ( see Figure 7 and Table 2 ) , these results support the above hypothesis . Further , as shown in Figure 8 , performance of Topknet integrating optimal algorithms from a dataset with high-similarity ( green lines ) is comparable to that integrating top 10 highest-performance algorithms ( blue lines ) . Thus , data-similarity based optimal algorithm selection together with TopkNet ( or community prediction ) based integration of the selected optimal algorithms can be a powerful strategy to reconstruct unknown regulatory network .
With an increasing corpus of inference algorithms , leveraging their diverse and sometimes complementary approaches in a community consensus can be a promising strategy for reconstruction of gene regulatory networks from large scale experimental data . A computational platform to systematically analyze , assess and leverage these diverse techniques is essential for the successful application of reverse engineering in biomedical research . This study presents a reverse engineering framework which can flexibly integrate multiple inference algorithms , based on TopkNet - a novel technique for building a consensus network based on the algorithms . It is pertinent to note here that the consensus framework based on TopkNet can be flexibly extended to include various types of network-inference algorithms . Comparative evaluation on the DREAM5 datasets showed that , although TopkNet based on 38-algorithm integration shows lower or at most comparable performance to the best individual algorithms , Top1Net based on integration of top 10 highest performance algorithms significantly outperforms the best individual algorithm as well as community prediction . The results demonstrated that ( i ) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and ( ii ) selection of high-performance algorithms for a given expression dataset and Top1Net based on integration of the selected high-performance algorithms could be a powerful strategy for reliable reverse engineering . Why does Top1net algorithm integrating 10 optimal algorithms perform quite well and outperform the best individual method ? This is because 10 optimal algorithms tend to assign high-confidence scores to true-positive links and Top1net method can recover many true-positive links that are with the highest confidence scores from 10 optimal algorithms . Furthermore , 10 optimal algorithms are based on different techniques ( e . g . , mutual information , regression , and other statistical techniques ) and Top1net can leverage diversity from the optimal algorithms . For example , the optimal algorithms based on mutual-information and regression techniques can accurately recover true positive links in feed-forward loops and linear cascade modules , respectively [35] , while Top1net could integrate the algorithms and accurately recover both feed-forward loops and linear cascade module in a GRN . Therefore , Top1net shows higher inference performance than the best individual algorithms . Why , then , Top1net outperforms community prediction and Topknet with higher k ? Community prediction and Topknet with larger k recover links with lower confidence scores than Top1net , i . e . , community prediction uses mean among confidence scores from 10 optimal algorithms and Topknet uses kth highest confidence score from the algorithms . Links with lower confidence scores from optimal algorithms are more likely to be false-positive links and thus Top1net shows higher inference performance than community prediction and Topknet with higher k . A key to reconstruct accurate GRNs is development of a method to determine optimal algorithms for a given expression dataset associated with unknown regulatory network . As mentioned in results , if similarity between expression-data associated with known regulatory network ( i . e . , DREAM5 datasets ) and that with an unknown regulatory network is high , optimal algorithms for data with known regulatory network may be also optimal for reconstruction of the unknown regulatory network . Based on this observation , we developed a measure to quantify similarity among the expression datasets based on algorithm diversity and demonstrated that , if similarity between the two expression-datasets is high , integration of algorithms that are optimal for one dataset could perform well on the other dataset . Thus , the similarity measure proposed in this study can be a good clue to identify optimal algorithms for reliable reconstruction of an unknown regulatory network . The consensus framework outlined in this paper , TopkNet , together with analysis of similarity among expression datasets , provide a powerful platform towards harnessing the wisdom of the crowds approach in reconstruction of large scale gene regulatory networks .
We used the DREAM5 datasets ( http://wiki . c2b2 . columbia . edu/dream/index . php/D5c4 ) to evaluate performance of network-inference algorithms . The DREAM5 dataset composed of an in-silico network ( 1 , 643 genes ) , the real transcriptional regulatory network of E . coli ( 4 , 511 genes ) , that of S . celecisiae ( 5 , 950 genes ) , and corresponding expression dataset ( 805 , 805 , and 536 samples for the in-silico , E . coli , and S . celevisiae networks , respectively ) . The expression dataset of E . coli and that of S . celevisae are composed of hundreds of experiments , i . e . , genetic , drug , and environmental perturbations . The in-silico network is generated by extracting a subnetwork composed of 1 , 643 genes from the E . coli transcriptional network . The expression datasets of the in-silico network was simulated by software GeneNetWeaver version 2 . 0 [38] . For the DREAM5 datasets , in the same manner to Marbach et al . [35] , we used the links with the top 100 , 000 highest confidence scores by each network-inference algorithm to evaluate performance of the algorithm . To evaluate performance of inference algorithms for the DREAM5 datasets , DREAM organizers provide a matlab software ( http://wiki . c2b2 . columbia . edu/dream/index . php/D5c4 ) . The software calculates 4 metrics for each network , i . e . , AUC-PR , AUC-ROC , AUC-PR p-value , and AUC-ROC p-value . AUC-PR ( AUC-ROC ) p-value is the probability that a given or greater AUC-PR ( AUC-ROC ) is obtained by random scoring of links . Furthermore , the software calculates an overall score that was used to evaluate the overall performance of the algorithms for all three networks ( the large synthetic network , large real E . coli , and S . celevisiae GRNs ) of the DREAM5 network inference challenge . The overall score ( OS ) is defines as OS = 0 . 5 ( p1+p2 ) , where p1 and p2 are the mean of the log-transformed AUC-PR p-values and that of the log-transformed AUC-ROC p-values taken over the three networks of the DREAM5 challenge , respectively . We obtained confidence scores between two genes by 35 algorithms ( 29 algorithms are from DREAM5 participants and 6 algorithms are commonly used “off-the shelf” algorithms ) from supplementary file of Marbach et al . [35] . For c3net , ggm , and mrnet algorithms , we calculated confidence scores of regulatory link by using GeneNet package [39] , c3net R package [9] , [10] , and minet R package [40] , respectively . Because Marbach et al . used links with top 100 , 000 highest confidence scores from each of 35 algorithms for analyses [35] , we used top 100 , 000 links from c3net , ggm , and mrnet for analyses in this study . For a given threshold value of confidence level , network-inference algorithms predict whether a pair of genes have regulatory link or not . A pair of genes with a predicted link is considered as a true positive ( TP ) if the link is present in the underlying synthetic network , while the pair is a false positive ( FP ) if the synthetic network does not have the link . Similarly , a pair of genes without a predicted link is considered as a true negative ( TN ) or false negative ( FN ) depending on whether the link exists or not in the underlying synthetic network , respectively . By using the values of TP , FP , TN , and FN , we can calculate several metrics to evaluate performances of network-inference algorithms . One representative metric is precision/recall curve where the precision ( p ) and recall ( r ) are defined as and , respectively . By using many threshold values , we obtained a precision/recall curve that is a graphical plot of the precision vs . the recall and is a straight forward visual representation of performances of network-inference algorithms . The area under the precision/recall curve ( AUC-PR ) is a summary metric of precision/recall curve and measures the average accuracy of network-inference algorithms . Another representative metric is ROC curve that is a graphical plot of the true-positive rate vs . the false-positive rate . The area under the ROC curve ( AUC-ROC ) also represents the average inference performance of algorithms . On the other hand , max f-score [41] evaluates optimum performance of network-inference algorithms where f-score is defined as harmonic mean of the precision and recall ( ) . As predictions of network-inference algorithms become more accurate , the value of AUC-PR , AUC-ROC , and max f-score becomes higher . We used AUC-PR , AUC-ROC , and max f-score for performance evaluation . To obtain these three metrics , we used package provided by the DREAM5 team [35] ( PR curve , ROC curve , AUC-PR , AUC-ROC , and overall score ) and perl script provided by Küffner et al . ( max f-score ) [27] . By using confidence scores among genes by network-inference algorithms , we calculated , DEUC ( X , Y ) , the simple Euclidean distance between two network-inference algorithms ( EUC distance ) X and Y for expression datasets with given number of genes and given sample size . Before giving a definition for DEUC ( X , Y ) , let us first define some notations . Let n be number of genes in the expression dataset and CS ( i , j , X ) be confidence value between genes i and j by algorithm X on the expression dataset . G = { ( 1 , 2 ) , ( 2 , 3 ) , … ( i , j ) … ( n-1 , n ) } represents the list of all possible combinations of two genes for n genes . We defined the EUC distance between the two algorithms as . Further , we calculated , DPCA ( X , Y ) , the distance between two network-inference ( X and Y ) on 2nd and 3rd principal components ( PCA distance ) from PCA analysis on confidence scores of 38 algorithms . Let PC2 ( X ) and PC3 ( X ) be the 2nd and 3rd components of X , respectively . We defined the PCA distance between two algorithms as . For the PCA analysis , we used R code , pricomp2 . R , obtained from http://aoki2 . si . gunma-u . ac . jp/R/src/princomp2 . R . By using distances among algorithms , we calculated , S ( da1 , da2 ) , similarity between two expression datasets da1 and da2 . Before giving definition of S ( da1 , da2 ) , let us first define some notation . Let k and A = {a1 , a2 , … , ai , … , ak} be the number of algorithms and the list of the algorithms , respectively . AC = { ( a1 , a2 ) , ( a2 , a3 ) , … , ( ai-1 , ai ) , … , ( ak-1 , ak ) } represents all possible combinations of two algorithms among k algorithms ( k ( k-1 ) /2 algorithm combinations ) . For example , in this study , we examined 38 algorithms and have 38*37/2 = 703 algorithm combinations . D ( ai , aj , da1 ) represents distances between two algorithms a1 and a2 for da1 . Dda1{AC} = {D ( a1 , a2 , da1 ) , D ( a2 , a3 , da1 ) , … , D ( ai-1 , ai , da1 ) , … D ( ak-1 , ak , da1 ) } represents a vector of k ( k-1 ) /2 algorithm distances for da1 ( in this study , we have a vector of 703 algorithm distances for each of DREAM5 datasets ) . We defined S ( da1 , da2 ) as Spearman's correlation coefficient between two vectors , Dda1{AC} and Dda2{AC} . To infer GRNs from the large-scale expression data of DREAM5 ( expression data of E . coli and S . cerevisiae ) , we built a cloud computing infrastructure on Amazon EC2 “High-memory double” instances ( 34 . 2 GB memory and 4 virtual cores with 3 . 25 EC2 Compute Units each ) with Redhad linux and R version 2 . 15 . 0 [42] . We placed all the input data on the ephemeral storage disk ( 850 GB ) of the Amazon EC2 instances and TopkNet output results ( e . g . , a listing of confidence scores between genes ) to files on the storage disk . | Elucidating gene regulatory networks is crucial to understand disease mechanisms at the system level . A large number of algorithms have been developed to infer gene regulatory networks from gene-expression datasets . If you remember the success of IBM's Watson in ”Jeopardy ! „ quiz show , the critical features of Watson were the use of very large numbers of heterogeneous algorithms generating various hypotheses and to select one of which as the answer . We took similar approach , “TopkNet” , to see if “Wisdom of Crowd” approach can be applied for network reconstruction . We discovered that “Wisdom of Crowd” is a powerful approach where integration of optimal algorithms for a given dataset can achieve better results than the best individual algorithm . However , such an analysis begs the question “How to choose optimal algorithms for a given dataset ? ” We found that similarity among gene-expression datasets is a key to select optimal algorithms , i . e . , if dataset A for which optimal algorithms are known is similar to dataset B , the optimal algorithms for dataset A may be also optimal for dataset B . Thus , our “TopkNet” together with similarity measure among datasets can provide a powerful strategy towards harnessing “Wisdom of Crowd” in high-quality reconstruction of gene regulatory networks . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks |
Chikungunya and dengue viruses emerged in Gabon in 2007 , with large outbreaks primarily affecting the capital Libreville and several northern towns . Both viruses subsequently spread to the south-east of the country , with new outbreaks occurring in 2010 . The mosquito species Aedes albopictus , that was known as a secondary vector for both viruses , recently invaded the country and was the primary vector involved in the Gabonese outbreaks . We conducted a retrospective study of human sera and mosquitoes collected in Gabon from 2007 to 2010 , in order to identify other circulating arboviruses . Sample collections , including 4312 sera from patients presenting with painful febrile disease , and 4665 mosquitoes belonging to 9 species , split into 247 pools ( including 137 pools of Aedes albopictus ) , were screened with molecular biology methods . Five human sera and two Aedes albopictus pools , all sampled in an urban setting during the 2007 outbreak , were positive for the flavivirus Zika ( ZIKV ) . The ratio of Aedes albopictus pools positive for ZIKV was similar to that positive for dengue virus during the concomitant dengue outbreak suggesting similar mosquito infection rates and , presumably , underlying a human ZIKV outbreak . ZIKV sequences from the envelope and NS3 genes were amplified from a human serum sample . Phylogenetic analysis placed the Gabonese ZIKV at a basal position in the African lineage , pointing to ancestral genetic diversification and spread . We provide the first direct evidence of human ZIKV infections in Gabon , and its first occurrence in the Asian tiger mosquito , Aedes albopictus . These data reveal an unusual natural life cycle for this virus , occurring in an urban environment , and potentially representing a new emerging threat due to this novel association with a highly invasive vector whose geographic range is still expanding across the globe .
Zika virus ( ZIKV ) is a mosquito-borne flavivirus phylogenetically related to dengue viruses . Following its first isolation in 1947 from a sentinel monkey placed in the Zika forest in Uganda [1] , serological surveys and viral isolations ( reviewed in [2] ) suggested that ZIKV ( i ) ranged widely throughout Africa and Asia , and ( ii ) circulated according to a zoonotic cycle involving non-human primates and a broad spectrum of potential mosquito vector species . In Africa , ZIKV has been isolated from humans in western and central countries such as Senegal , Nigeria , Central African Republic and Uganda [3]–[7] . Serological surveys ( reviewed in [2] ) suggested that its geographic range might extend not only to other West and Central African countries ( Sierra Leone , Cameroon , Gabon ) , but also to eastern ( Ethiopia , Kenya , Tanzania and Somalia ) and northern Africa ( Egypt ) . ZIKV has also been isolated from mosquitoes collected in Senegal , Ivory Coast , Burkina Faso , Central African Republic and Uganda [1] , [6] , [8] , [9] . These mosquitoes mainly belonged to sylvan or rural species of the genus Aedes , and more precisely to the Aedimorphus , Diceromyia and Stegomyia subgenera . The virus has also been isolated in West Africa ( Burkina Faso , Senegal and Ivory Coast ) [6] , [9] and Asia [10] from Aedes aegypti , a species being considered the main ZIKV epidemic vector outside Africa [11] . Moreover , Ae . aegypti was shown experimentally to be an efficient ZIKV vector [12]–[14] . Despite its apparent broad geographic distribution in Africa and Asia , only sporadic cases of human ZIKV infection have been reported . This virus received little attention until its sudden emergence in Yap Island ( Micronesia ) in 2007 , which involved about 5000 persons [15] , [16] , revealing its epidemic capacity . Patients develop a mild dengue-like syndrome , including fever , headache , rash , arthralgia and conjunctivitis . This clinical similarity with other , more commonly diagnosed arboviral infections such as chikungunya ( CHIKV ) and dengue ( DENV ) , might delay the diagnosis and/or lead to underestimation of ZIKV infections . Here , we report the first direct evidence of ZIKV epidemic activity in Central Africa , and its occurrence in an urban environment during concomitant CHIKV/DENV outbreaks in Libreville , the capital of Gabon , in 2007 . We also report the first detection of ZIKV in the Asian tiger mosquito , Ae . albopictus . These findings , together with the global geographic expansion of this invasive species and its increasing importance as epidemic vector of arboviruses as exemplified by CHIKV adaptation , suggest that the prerequisites for the emergence and global spread of Zika virus may soon be satisfied .
In 2007 and 2010 , Gabon recorded simultaneous outbreaks of CHIKV ( genus Alphavirus ) and DENV ( genus Flavivirus ) infections . The 2007 outbreaks primarily affected Libreville , the capital of Gabon , and subsequently extended northwards to several other towns [17] , while the 2010 outbreaks occurred in the south-eastern provinces [18] . To detect other circulating arboviruses , we conducted a retrospective study based on molecular screening of 4312 sera from symptomatic patients presenting to healthcare centers; 24 . 7% of the samples were obtained during the 2007 outbreaks , 9 . 7% during the inter-epidemic period , and 65 . 5% during the 2010 outbreaks ( data not shown ) . We also analyzed a collection of 4665 mosquitoes captured during the same period and split into 247 pools according to the species , date and sampling site ( Table 1 , see [18] and [19] for the details of the methodology used for mosquito trapping ) . The Centre International de Recherches Médicales de Franceville ( CIRMF ) and the Gabonese Ministry of Health cooperated in the 2007 and 2010 outbreak response and management , that included blood sampling for laboratory diagnostic and epidemiological survey . The study was approved by our Institutional review board ( Conseil scientifique du CIRMF ) . Symptomatic patients presented to health care centers for medical examination . All patients were informed that blood sampling was required for laboratory diagnosis of suspected acute infections , such as malaria , dengue or chikungunya fever . During the two outbreaks , given the urgency of diagnosis , only oral consent was obtained for blood sampling and was approved by the institutional review board . However during the active surveillance study that was performed between the two outbreaks ( described in reference [18] ) , written consent could be obtained . Primary molecular screening was based on hemi-nested reverse-transcription PCR ( hnRT-PCR ) with the generic primers PF1S/PF2Rbis/PF3S targeting highly conserved motifs in the flavivirus polymerase ( NS5 ) gene ( 280-bp ) [20] . Yellow fever virus RNA ( vaccinal strain 17D ) was used as a positive control . A second screening was performed with a ZIKV-specific real-time PCR method using the primers-probe system ZIKV-1086/ZIKV-1162c/ZIKV-1107-FAM [16] , also targeting a short sequence ( 160 bp ) of the NS5 gene . Virus isolation was attempted on the Vero and C6/36 cell lines but was unsuccessful , presumably because of low viral titers ( despite two patients presenting only 1 and 4 days after symptom onset ) , and unsuitable initial storage conditions . To further characterize the Gabonese ZIKV strains , partial envelope ( E ) ( 841 bp ) and NS3 ( 772 bp ) gene sequences were amplified by conventional nested RT-PCR with specific primers derived from published ZIKV sequences . The primer pairs targeting the E gene were ZIK-ES1 ( TGGGGAAAYGGDTGTGGACTYTTTGG ) /ZIK-ER1 ( CCYCCRACTGATCCRAARTCCCA ) and ZIK-ES2 ( GGGAGYYTGGTGACATGYGCYAAGTT ) /ZIK-ER2 ( CCRATGGTGCTRCCACTCCTRTGCCA ) . The primer pairs for NS3 amplification were ZIK-NS3FS ( GGRGTCTTCCACACYATGTGGCACGTYACA ) /ZIK-NS3FR ( TTCCTGCCTATRCGYCCYCTCCTCTGRGCAGC ) and ZIK-X1 ( AGAGTGATAGGACTCTATGG ) /ZIK-X2 ( GTTGGCRCCCATCTCTGARATGTCAGT ) . The E and NS3 sequences obtained from one Gabonese patient were concatenated and analyzed using a set of previously published ZIKV sequences . Phylogenetic relationships were reconstructed with the maximum likelihood algorithm implemented in PhyML [21] ( available at http://www . atgc-montpellier . fr/phyml/ ) with best of NNI ( Nearest Neighbor Interchange ) and SPR ( Subtree Pruning and Regrafting ) criteria for tree topology searching , and the GTR model of nucleotide substitutions . The Gamma distribution of rate heterogeneity was set to 4 categories , with a proportion of invariable sites and an alpha parameter estimated from the dataset . Branch support was assessed from 100 bootstrap replicates . Tree reconstructions were also performed by Bayesian inference with MrBayes v3 . 2 [22] under the GTR+I+G model of nucleotide substitutions , and with the distance neighbor-joining method [23] implemented in MEGA5 [24] with confidence levels estimated for 1000 replicates . To test for phylogenetic discrepancies , tree reconstructions were also performed independently from the envelope dataset and the NS3 dataset with PhyML according to the parameters described above . The resulting trees were visualized with the FigTree software ( Available at: http://tree . bio . ed . ac . uk/software/figtree/ ) , and rooted on midpoint for clarity . The Genbank accession numbers for the Gabonese ZIKV strain are KF270886 ( envelope ) and KF270887 ( NS3 ) .
The NS5 PCR products were sequenced , resulting in the first ZIKV RNA detection in a human sample ( Cocobeach ) and in two Ae . albopictus pools ( Libreville ) collected during the 2007 outbreaks . Real-time PCR was then performed , leading to the detection of four additional positive human samples , collected in 2007 in four suburbs of Libreville ( Diba-Diba , Nzeng-Ayong , PK8 , PK9 ) ( Figure 1 ) . No ZIKV was detected during the inter-epidemic period or during the 2010 outbreaks . Clinical information was available for only one ZIKV-positive patient , who had mild arthralgia , subjective fever , headache , rash , mild asthenia , myalgia , diarrhea and vomiting . No information was available on this patient's outcome . Cycle threshold values for human blood samples were high ( >37 cycles ) , suggesting low viral loads ( data not shown ) . Aedes albopictus was the predominant species collected , accounting for 55 . 4% of the mosquito pools , while Aedes aegypti accounted for 18 . 2% ( Table 1 ) . The other mosquito species consisted of members of the Aedes simpsoni complex , Anopheles gambiae , Mansonia africana , Mansonia uniformis , Culex quinquefasciatus , Eretmapodites quinquevittatus and unidentified Culex species . Positive mosquito pools were captured from two suburbs ( Nzeng-Ayong and Alenkiri ) where Aedes albopictus was the predominant species ( Figure 1 , Table 1 ) . As isolation on the Vero and C6/36 cell lines failed , the Gabonese ZIKV strain was further characterized by partial sequencing of the E and NS3 genes . Phylogenetic analysis was performed on concatenated E and NS3 sequences from one Cocobeach serum sample . The resulting tree topology ( Figure 2 ) was similar to that previously obtained from the complete coding sequences , corroborating Asian and African distinct lineages [2] . The African lineage was further split into two groups , one containing the genetic variants of the MR766 strain ( Uganda , 1947 ) and the second including West African strains ( Nigeria , 1968; Senegal , 1984 ) and the new ZIKV sequence from Gabon , at a basal position . Phylogenetic trees derived from the E and NS3 partial sequences resulted in a similar topology , apart from the weakly supported branching pattern for the MR766 variant DQ859059 , oscillating between the two African sister groups ( Supporting Figure S1 ) . The deletions in potential glycosylation sites previously reported for the Nigerian ZIKV strain and two variants of the Ugandan strain MR766 ( sequences AY632535 and DQ859059 ) [2] were absent from the Gabonese ZIKV sequence .
Evidence of human ZIKV infections in Central Africa is limited to one isolate from RCA in 1991 [6] and two serological surveys performed 50 years ago in Gabon [25] , [26] . No report of human ZIKV infections was made in other countries of the Congo basin forest block , despite probable circulation through a sylvan natural cycle . We provide here the first direct evidence of human ZIKV infections in Gabon , as well as its occurrence in an urban transmission cycle , and the probable role of Ae . albopictus as an epidemic vector . Our phylogenetic results are in agreement with the tree topology previously obtained with complete coding sequences of ZIKV strains , showing an African lineage and an Asian lineage [2] . The branching pattern obtained here suggests that ZIKV emergence in Gabon did not result from strain importation but rather from the diversification and spread of an ancestral strain belonging to the African lineage . The identification of ZIKV in two different localities of Gabon ( Cocobeach and Libreville ) suggests that the virus was widespread rather than restricted to a single epidemic focus . The simultaneous occurrence of human and mosquito infections in Libreville also suggests that the virus circulated in 2007 in an epidemic cycle rather than as isolated cases introduced from sylvan cycles . Of note , ZIKV transmission occurred here in a previously undocumented urban cycle , supporting the potential for urbanization suggested in 2010 by Weaver and Reisen [27] . While some mosquito species ( including Ae . aegypti ) previously found to be associated with ZIKV , were captured and tested here , only Ae . albopictus pools were positive for this virus . Moreover , this species largely outnumbered Ae . aegypti in the suburbs of Libreville where human cases were detected , suggesting that Ae . albopictus played a major role in ZIKV transmission in Libreville . The ratio of ZIKV-positive Ae . albopictus pools is similar to that reported for DENV-positive pools , suggesting that these two viruses infect similar proportions of mosquitoes . The small number of recorded human ZIKV cases , by comparison with DENV cases , may be due to the occurrence of subclinical forms of ZIKV infections that did not required medical attention . Thus , an underlying ZIKV epidemic transmission might have been masked by concomitant CHIKV/DENV outbreaks . The natural histories of CHIKV and ZIKV display several similarities . Before the large Indian Ocean outbreaks in 2005–2007 , chikungunya fever was a neglected arboviral disease . Both viruses are phylogenetically closely related to African viruses [28]–[30] suggesting they probably originated in Africa , where they circulated in an enzootic sylvan cycle involving non-human primates and a wide variety of mosquito species , human outbreaks presumably being mediated by Ae . aegypti [5] , [31] . In Asia , both viruses are thought to circulate mainly in a human-mosquito cycle involving Ae . aegypti [11] , [14] , [31] . Together with the recent Yap Island outbreak , this prompted some researchers to re-examine the susceptibility of Ae . aegypti to ZIKV infection [14] . However , it must be noted that the vector of the Yap Island outbreak was not definitely identified since the predominant potential vector species Aedes hensilli remained negative [15] , and that ZIKV has been isolated only once from Ae . aegypti in Asia [10] , so that its vector status in natura is not confirmed . Additionally , a ZIKV enzootic transmission cycle involving non-human primates in Asia and sylvatic vectors cannot be ruled out as suggested by serologic studies carried on orangutang [32] , [33] . Finally both CHIKV and ZIKV have shown their ability to adapt to a new vector , Ae . albopictus , upon introduction in an environment where their primary vector was outnumbered . This mosquito species being native to South-East Asia , our findings may help to explain human ZIKV transmission in Asia . Aedes albopictus was first introduced in Africa in 1991 [34] and detected in Gabon in 2007 , where its invasion likely contributed to the emergence of CHIKV and DENV in this country [17]–[19] , [35] . Multiple lines of evidence supporting its increasing impact as an arboviral vector have also been obtained during CHIKV outbreaks in the Indian Ocean region ( 2005–2007 ) and in Italy ( 2007 ) [36] , [37] through viral evolutionary convergence of Ae . albopictus adaptive mutations [38]–[41] . Whether or not the transmission of ZIKV in Central Africa was also link to such an adaptative mutation of ZIKV to Ae . albopictus cannot be answered at this stage . Wong and colleagues [42] have just demonstrated experimentally that Ae . albopictus strains from Singapore were orally receptive to the Ugandan strain of ZIKV sampled in 1947 , suggesting that this virus-vector association in Africa may have been previously prevented because the required ecological conditions did not yet exist . However , given the relatively low ZIKV viral loads previously reported in patients - with an order of magnitude of 105 copies/ml compared to 107 to 109 copies/ml for CHIKV [16] , [18] , [40] - the oral infectivity for Ae . albopictus may seem at least as critical as it was for CHIKV in establishing this new human-mosquito cycle . Why ZIKV has not yet been detected in the areas where DENV and CHIKV have already spread via Ae . albopictus is unclear , but it may be an ongoing process which we are just starting to detect . The spread of CHIKV reflects the ability of arboviruses to adapt to alternative hosts , and the resulting public health concerns in both developed and developing countries . Is ZIKV the next virus to succeed CHIKV as an emerging global threat ? The increasing geographic range of Ae . albopictus in Africa , Europe , and the Americas [34] , [36] , [43] , [44] , together with the ongoing ZIKV outbreak in French Polynesia at the time of writing [45] suggest this possibility should be seriously considered . Analysis of sylvan and urban transmission cycles , together with viral genetics and vector competence studies , are now required to assess ( i ) how ZIKV is able to establish a sustainable transmission cycle involving this new vector in Central Africa , ( ii ) vector ( s ) -virus relationships in Asia , and ( iii ) the risk of importation and spread to new areas where Ae . albopictus occurs as well . | Not previously considered an important human arboviral pathogen , the epidemic capacity of Zika virus ( ZIKV , a dengue-related flavivirus ) was revealed by the Micronesia outbreak in 2007 , which affected about 5000 persons . Widely distributed throughout tropical areas of Asia and Africa , ZIKV is transmitted by a broad range of mosquito species , most of which are sylvatic or rural , Aedes aegypti , an anthropophilic and urban species , being considered the main ZIKV epidemic vector . In a context of emerging arbovirus infections ( chikungunya ( CHIKV ) and dengue ( DENV ) ) in Gabon since 2007 , we conducted a retrospective study to detect other , related viruses . In samples collected during the concurrent CHIKV/DENV outbreaks that occurred in the capital city in 2007 , we detected ZIKV in both humans and mosquitoes , and notably the Asian mosquito Aedes albopictus that recently invaded the country and was the main vector responsible for these outbreaks . We found that the Gabonese ZIKV strain belonged to the African lineage , and phylogenetic analysis suggested ancestral diversification and spread rather than recent introduction . These findings , showing for the first time epidemic ZIKV activity in an urban environment in Central Africa and the presence of ZIKV in the invasive mosquito Aedes albopictus , raise the possibility of a new emerging threat to human health . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"mosquitoes",
"rna",
"viruses",
"emerging",
"infectious",
"diseases",
"vector",
"biology",
"virology",
"viral",
"classification",
"emerging",
"viral",
"diseases",
"biology",
"microbiology",
"viral",
"vectors"
] | 2014 | Zika Virus in Gabon (Central Africa) – 2007: A New Threat from Aedes albopictus? |
As part of the Global Programme to Eliminate Lymphatic Filariasis ( LF ) , American Samoa conducted mass drug administration ( MDA ) from 2000–2006 , and passed transmission assessment surveys in 2011–2012 . We examined the seroprevalence and spatial epidemiology of LF post-MDA to inform strategies for ongoing surveillance and to reduce resurgence risk . ELISA for LF antigen ( Og4C3 ) and antibodies ( Wb123 , Bm14 ) were performed on a geo-referenced serum bank of 807 adults collected in 2010 . Risk factors assessed for association with sero-positivity included age , sex , years lived in American Samoa , and occupation . Geographic clustering of serological indicators was investigated to identify spatial dependence and household-level clustering . Og4C3 antigen of >128 units ( positive ) were found in 0 . 75% ( 95% CI 0 . 3–1 . 6% ) of participants , and >32 units ( equivocal plus positive ) in 3 . 2% ( 95% CI 0 . 6–4 . 7% ) . Seroprevalence of Wb123 and Bm14 antibodies were 8 . 1% ( 95% CI 6 . 3–10 . 2% ) and 17 . 9% ( 95% CI 15 . 3–20 . 7% ) respectively . Antigen-positive individuals were identified in all ages , and antibody prevalence higher in older ages . Prevalence was higher in males , and inversely associated with years lived in American Samoa . Spatial distribution of individuals varied significantly with positive and equivocal levels of Og4C3 antigen , but not with antibodies . Using Og4C3 cutoff points of >128 units and >32 units , average cluster sizes were 1 , 242 m and 1 , 498 m , and geographical proximity of households explained 85% and 62% of the spatial variation respectively . High-risk populations for LF in American Samoa include adult males and recent migrants . We identified locations and estimated the size of possible residual foci of antigen-positive adults , demonstrating the value of spatial analysis in post-MDA surveillance . Strategies to monitor cluster residents and high-risk groups are needed to reduce resurgence risk . Further research is required to quantify factors contributing to LF transmission at the last stages of elimination to ensure that programme achievements are sustained .
Lymphatic filariasis ( LF ) is a neglected tropical disease of global importance , with an estimated 1 . 4 billion people in 73 countries at risk of infection . Over 120 million people worldwide are currently affected by lymphatic filariasis and 40 million are disfigured and disabled [1] . Infection is transmitted by mosquito vectors including Anopheles , Aedes , Culex and Mansonia species . The Pacific Programme for Elimination of Lymphatic Filariasis ( PacELF ) was formed in 1999 , and as part of the Global Programme to Eliminate LF ( GPELF ) , aimed to eliminate the disease as a public health problem in 22 Pacific Island countries and territories ( PICTs ) by 2020 [2] . The Programme in the Pacific covers over 3000 islands and 8 . 6 million people , and consists of two strategies: firstly , to interrupt transmission through mass drug administration ( MDA ) using albendazole and diethycarbamazine ( DEC ) and secondly , to control morbidity and disability of affected persons [2] . Baseline surveys conducted in 1999 and 2000 determined that 11 PICTs were endemic for LF , five partially endemic , and six non-endemic [2] . Since then , variable progress has been made towards reducing prevalence and interrupting transmission on different islands [3] , but significant success has been achieved in the Samoan Islands , particularly in American Samoa . Before the 1960s , both Samoa ( formerly called Western Samoa ) and American Samoa had high prevalence ( ∼20% ) of lymphatic filariasis [4] , [5] . Multiple rounds of MDA in the 1960s had considerable impact and reduced the prevalence of microfilaraemia to less than 2% , but neither Samoa nor American Samoa managed to achieve sustained interruption of transmission at that time [6]–[9] . By 1999 , antigen prevalence of 16 . 5% ( N = 3018 ) was recorded in American Samoa and 4 . 5% ( N = 7006 ) in Samoa . In American Samoa , after seven rounds of MDA from 2000–2006 , antigen prevalence dropped to 2 . 3% ( N = 1881 ) in 2007 in a community cluster survey that involved all age groups [10] . Current WHO guidelines [11] recommend that in areas where W . bancrofti is endemic and Aedes is the principal vector , the target threshold for post-MDA transmission assessment surveys ( TAS ) is <1% antigenaemia . Based on this target and sample sizes , critical cutoff values are calculated so that evaluation units have at least a 75% chance of passing if the true prevalence of antigenaemia is 0 . 5% , and no more than 5% of passing ( incorrectly ) if the true prevalence is ≥1% . For evaluation units where the number of antigen-positive individuals is below the critical cutoff value , no further MDA is recommended because of the low risk of continuing transmission . For areas where Anopheles or Culex is the principal vector , the target threshold is <2% antigenaemia . American Samoa passed transmission assessment surveys ( TAS ) in 2011–12 , designed to determine whether antigen prevalence using the ICT card test in 6 to 7 year old children was less than 1% [11] . The surveys found two ICT-positive children ( N = 949 , included 25 of 26 schools , critical cutoff of 6 ) on the main island of Tutuila and the adjacent island of Aunu'u , and 0% ( N = 37 , census at all schools ) in the remote Manu'a islands [12] . In Samoa , TAS conducted in 2013 in three evaluation units ( N = 3 , 585 ) found that while two units passed established targets , one unit ( northwest Upolu ) failed with 19 positives ( N = 1 , 271 , critical cutoff of 7 ) and further MDA was recommended [13] . The historical high risk of resurgence in the Samoan islands is likely to be related to a combination of factors , including poor MDA coverage and low compliance [14]; both day and night-biting mosquito vectors ( including Aedes polynesiensis and Aedes samoanus ) that are highly efficient at transmitting LF [15] , [16]; and intense environmental drivers for transmission such as the tropical climate , high rainfall , abundance of suitable mosquito breeding sites , and outdoor lifestyle . Samoa and American Samoa were historically and ethnically one nation , divided into two separate political entities in 1899 . There are continuing strong family , cultural , and economic links between the Samoan islands , with associated frequent high-volume travel ( often for extended periods ) and cross-migration . Reintroduction of parasites by infected travellers could therefore play an important role in potential resurgence [8] , [17] . Previous studies in Samoa and Haiti found significant micro-spatial clustering of infection in areas of low prevalence , suggesting the potential for small residual foci of transmission at the neighbourhood scale even though overall average prevalence in an evaluation unit might be less than 1% [18] , [19] . Such findings could potentially have significant implications for post-MDA surveillance strategies in certain epidemiological settings . The WHO and GPELF have identified a number of knowledge gaps , key challenges and operational research priorities for LF elimination , including: i ) the significance of residual microfilaraemia and antigenaemia in communities where the target threshold level has been achieved through MDA; ii ) rapid identification of high-prevalence areas and development of strategies for dealing with them; and iii ) development and standardization of cost-effective strategies for post-MDA surveillance [20] . While the initial phases of the programme focused mostly on developing guidelines , initiating and implementing country activities , and scaling up MDA rapidly to aim for full coverage , the latter phases require more attention towards ensuring a successful ending [21] . The sustained success of elimination programs depends on careful monitoring for potential resurgence post-MDA , particularly where there are residual foci with high prevalence , and where resurgence has historically proven to be a problem despite achieving low prevalence rates . The priorities in the endgame phase of LF elimination therefore include: i ) intensive targeted studies of transmission thresholds , ii ) new tools and strategies to accurately verify when transmission has been interrupted; and iii ) effective post-intervention surveillance , monitoring , and evaluation to ensure timely detection of resurgence [21] . Additionally , as countries move closer to elimination , increasingly specific strategies and technical support are needed because of differences in local settings , needs , resources , constraints , and challenges [21] . Furthermore , as prevalence drops to very low levels after successful MDA , increasingly statistically robust surveillance strategies will be required to identify any ongoing transmission , particularly if confined to small geographic foci . Currently , TAS typically include young children and do not provide any information on adults . Adults are the main reservoirs of any residual infections and are also susceptible to new infections , so surveillance of adults in the post-MDA phase of elimination programs could provide valuable information for identifying residual foci and detecting early resurgence . The current WHO guidelines encourage cost-efficient methods for post-MDA surveillance , such as the integration of LF surveillance activities with other population-based surveys as well as opportunistic screening of groups such as military recruits , hospital patients , and blood donors for microfilaraemia , antigenaemia , or antibodies [11] . Such activities will become increasingly important for GPELF as more countries reach elimination targets and move into the surveillance phase , but there is currently a paucity of evidence-based guidelines for conducting these activities or interpreting the findings . We examined the seroprevalence of LF antigens and antibodies in American Samoan adults in 2010 to complement the results of TAS conducted in young children in 2011–2012 , with the goal of providing a more complete picture of the status of LF in American Samoa after successful MDA . We used a serum bank and associated geo-referenced database to determine the seroprevalence of LF antigen and antibodies in adults , examine the spatial epidemiology of infection post-MDA , and identify any possible residual foci of infection and/or high-risk populations that might require targeted surveillance and monitoring . Our study aimed to address some of the knowledge gaps identified by WHO and GPELF by improving understanding of LF transmission in an area of low prevalence , explore the value of adult serological data for surveillance after successful MDA , develop new tools and strategies to more accurately verify interruption of transmission , and provide evidence-based guidance for future surveillance strategies in American Samoa .
American Samoa consists of a group of remote islands in the South Pacific: the main island of Tutuila , the adjacent island of Aunu'u , and the remote Manu'a group of islands ( Ta'u , Ofu , and Olosega ) . The census population in 2010 was approximately 56 , 000 [22] , with over 90% residing on Tutuila , mostly in coastal villages . American Samoa has a tropical climate and is one of the wettest inhabited places in the world ( average annual rainfall of over 3 , 000 mm ) , with rugged islands that include mountains , valleys , tropical rainforests , wetlands , fringing reefs , and lagoons . Wuchereria bancrofti is the only filarial worm species found in American Samoa , and mosquito vectors include the highly efficient day-biting Aedes polynesiensis and night-biting Aedes samoanus . A serum bank was collected for a leptospirosis study in American Samoa in 2010 ( four years after the last effective round of MDA for LF ) , and detailed description of the study design has been previously reported [23] , [24] . Briefly , in Tutuila and Aunu'u , the study used a spatial sampling method that systematically selected households from a geo-referenced database of all houses on the islands . Sampling was designed to ensure maximum spatial dispersion over the study area to optimise geospatial analysis . In the very sparsely populated Manu'a Islands , the spatial sampling method was impractical , and non-random convenience sampling was used . The study included 807 adults ( aged 18 to 87 years , 52 . 4% males ) from 659 households in 55 villages on all five inhabited islands; 721 ( 89 . 3% ) lived on the main island of Tutuila , and 555 ( 68 . 8% ) had lived in American Samoa for their entire life . During the serum bank collection , the primary place of residence of each participant was geo-located using detailed village maps obtained from the American Samoa GIS User Group [25] . Questionnaires were used to obtain demographic data from participants , and were conducted by a team of interviewers who were fluent in both English and Samoan . The serum bank was highly representative of the adult population of American Samoa in both age and geographic distribution . Table 1 provides a summary of the demographics of the study population . For the original leptospirosis study , approvals were obtained from the American Samoa Institutional Review Board ( ASIRB ) , the Medical Research Ethics Committee of The University of Queensland ( MREC-UQ ) , and the Queensland Health Forensic and Scientific Services Human Ethics Committee . The study was conducted in collaboration with the American Samoa Department of Health , and permissions for village visits were sought from the Department of Samoan Affairs and village chiefs and/or mayors . The study included only adult participants , all of whom provided written informed consent . For the current study , additional approvals to use the serum bank for lymphatic filariasis research were obtained from ASIRB and MREC-UQ . All serological analyses were conducted at the WHO Collaborating Centre for Lymphatic Filariasis , Soil-transmitted Helminths , and other Neglected Tropical Diseases at James Cook University , Cairns , Australia . For all assays , sera were tested in duplicate . For any samples where the duplicates showed greater than 15% coefficient of variation in optical density ( OD ) reading , tests were repeated , as were those on plates with unsatisfactory standard curves or OD of less than 1 . 0 in the highest standard group . All plates were read using a VersaMax tunable ELISA reader ( Molecular Devices ) using Softmax Pro v5 . 3 software . Outcome measures used for statistical analyses were ELISA test results for each LF antigen and antibody . For Og4C3 antigen , statistical analyses were performed using two different cutoff points: >128 units ( positive results ) and >32 units ( equivocal and positive results ) . Independent variables assessed included age , sex , years lived in American Samoa , occupation , household income , and island of residence . The number of years lived in American Samoa was categorized into <5 years ( to reflect those who did not live in American Samoa during local MDA activities from 2000 to 2006 ) , 5–10 years ( those who lived in Am Sam during some of the local MDA activities ) , and >10 years ( those who lived in Am Sam during all of the MDA activities ) . Occupation groups were categorized into those who worked i ) predominantly indoors , ii ) predominantly outdoors , iii ) tuna cannery workers ( the largest non-government employer in American Samoa; >90% of employees are migrant workers ) , and iv ) others ( including unemployed , unknown occupation , and those who have jobs that include both indoor and outdoor work ) . Data on household income was available in four categories . Island of residence was categorized into Tutuila and other islands . The serum bank consisted of samples and data on 807 participants . There was sufficient serum in 805 samples to perform ELISA for Og4C3 antigen , and in 806 samples for Wb123 and Bm14 antibodies . Data on gender were available for 803 participants , on age for 798 , on years lived in American Samoa for 800 , and on household income for 679 . Island of residence and geo-locations of households were available for all participants . Chi-squared or Fisher exact tests were used to compare outcomes for categorical independent variables . Variables with p<0 . 1 were selected for further analyses using univariate logistic regression , and odds ratios ( OR ) were calculated . STATA v11 . 1 software ( StataCorp , College Station , Texas ) was used for all analyses , and p values of <0 . 05 were considered to indicate statistical significance . During the serum bank collection , the primary place of residence of each participant was geo-located using village maps produced using geo-referenced data from the American Samoa GIS User Group [25] . Data available included island/village boundaries , and the location of houses , schools , churches and major infrastructure . For spatial analyses , only data from the main island of Tutuila were included . Populations and inhabited areas on Aunu'u and the Manu'a islands were too small for geospatial analysis to be meaningful . Maps were produced to show the distribution of participants' households , and locations of participants with positive ELISA for each antigen and antibody . The spatial distribution of participants based on years lived in American Samoa was also examined to determine if migrants were concentrated in any villages . All geo-spatial data were collated , stored , linked and mapped using ArcMap v10 . 0 ( Environmental Systems Research Institute , Redlands , CA ) . Spatial dependence in the positive serological results for each antigen and antibody was investigated using a semivariogram in the statistical software R , using the geoR package version 2·14·1 ( The R foundation for statistical computing ) . A semivariogram is a graphical representation of the spatial variation which allows for the quantification of spatial cluster size and the tendency for geographical clustering within a region . The semivariogram is characterized by three parameters: the sill , which is the spatially structured component of the semivariance ( indicative of the tendency for geographical clustering ) ; the nugget , which is the spatially unstructured component of the semivariance ( representing random variation , very small-scale spatial variability or measurement error ) ; and the range , which is the distance at which locations can be considered independent ( indicative of the size of geographical clusters ) . To estimate the proportion of the variation that was spatially structured we divided the partial sill by the sum of the partial sill and nugget .
Og4C3 antigen levels of >128 units ( positive result ) were found in 0 . 75% ( 6 persons , 95% CI 0 . 3–1 . 6% ) of participants , and levels of >32 units ( equivocal plus positive results ) in 3 . 2% ( 26 persons , 95% CI 0 . 6–4 . 7% ) . The seroprevalence of Wb123 and Bm14 antibodies were 8 . 1% ( 65 positives , 95% CI6 . 3–10 . 2% ) and 17 . 9% ( 144 positives , 95% CI 15 . 3–20 . 7% ) respectively . Table 1 provides a summary of the associations between demographic variables and the presence of LF antigen and antibodies . Our results show that both antigen and antibody prevalence were higher in males compared to females ( Table 1 ) . Figure 1 shows the age distribution of participants , and the prevalence of antigen ( Og4C3>128 and Og4C3>32 ) and antibodies ( Wb123 and Bm14 ) in each age group . Antigen-positive individuals were identified in all age groups , with no significant difference between ages . Prevalence of both Wb123 and Bm14 antibodies were higher in the older age groups . In participants aged 30 years and older , Bm14 prevalence was two to three times higher than Wb123 prevalence in all age groups . Antibody and antigen prevalence were inversely associated with the number of years lived in American Samoa ( Figure 2 and Table 1 ) . Of all study participants , 68 . 8% ( n = 555 ) had lived in American Samoa for all of their lives . Compared to individuals who had lived in American Samoa for over 10 years , new migrants who had lived there for <5 years had odds ratios of 13 . 7 ( 95% CI: 2 . 4–78 . 4 ) of having Og4C3 antigen of >128 units , and odds ratio of 6 . 1 ( 95% CI: 1 . 9–19 . 4 ) of having Og4C3 antigen of >32 units ( Table 1 ) . New migrants also had higher prevalence of Wb123 and Bm14 antibodies compared to those who had lived in American Samoa for >10 years , but differences were not statistically significant . The prevalence of antibodies and antigen were higher in residents on the main island of Tutuila compared to those who lived in smaller islands , but differences were not statistically significant . Tuna cannery workers had significantly higher prevalence of Wb123 antibodies , but there were no other associations between occupational groups and seroprevalence . Our study did not find any association between income and seroprevalence . For reference , a kernel density map of population distribution in American Samoa is shown in Figure 3 ( reproduced from [23] ) . The household locations of individuals with positive and negative Bm14 and Wb123 antibodies are shown in Figure 4a and 4b , and positive/equivocal Og4C3 levels shown in Figures 5a and 5b . High resolution maps of the villages of Fagalii ( Figure 6a ) and Ili'ili ( Figure 6b ) show the locations of participants' households , those with positive/equivocal results for Og4C3 , and the location of the elementary school where two ICT-positive children were identified during the 2011 Transmission Assessment Survey . While the semivariograms for Wb123 and Bm14 antibodies did not reveal any significant small-scale spatial variation , the semivariograms for antigen ( both Og4C3>128 units and Og4C3>32 units ) showed considerable residual spatial variation ( Figure 7 and Table 2 ) . Our results indicate that the average size of a cluster for Og4C3>128 units was 1 , 242 metres and the proportion of the variation in Og4C3>128 units explained by geographical proximity was 85% . The average size of a cluster for Og4C3>32 units was 1 , 498 meters and the proportion of the variation in Og4C3>32 units explained by geographical proximity was 62% . Migrants who had lived in American Samoa for <5 years and 5–10 years were dispersed throughout the territory , and no significant clustering was found .
Our study demonstrates that high-risk populations for LF in American Samoa include adult males and recent migrants . The results also suggest the possible existence of residual foci of antigen-positive individuals in American Samoa . Although our findings do not provide conclusive evidence of recent transmission , further investigation is recommended to confirm ( or otherwise ) the possible high-risk populations and locations , and determine whether ongoing targeted surveillance of these groups is warranted , particularly in the Samoan Islands where there is a history of resurgence despite achieving very low prevalence [4] . The prevalence of Wb123 and Bm14 antibodies differed significantly , and further research is required to understand the role of each laboratory test in post-MDA surveillance . There was a sharp rise in Bm14 antibody prevalence from age 30–39 years , which was also observed by Mladonicky et al in 2006 in three villages of American Samoa [9] . We found that Wb123 antibody prevalence peaked in participants aged between 30 and 40 years , but at much lower prevalence than Bm14 antibody . Wb123 antibody is a relatively new assay , and the indicative cutoff point used in this study could have contributed to the differences between the prevalence of Wb123 and Bm14 antibodies . Neither Wb123 nor Bm14 antibody prevalence declined with age , but at present we cannot distinguish long-term persistence of antibodies from ongoing transmission . Other studies have noted persistence of Wb123 antibodies in adults for many years after MDA , although significant decline was observed in those who were antigen-negative [28] . Positive Og4C3 antigen was found in all age groups and did not show any age-specific patterns . The presence of Og4C3 is not necessarily associated with microfilaraemia and does not provide evidence of ongoing transmission . Antigen prevalence drops dramatically after MDA , but it is not possible to unequivocally distinguish between recent or past infection based on Og4C3 alone . However , for W . bancrofti areas , the WHO currently supports the use of circulating filarial antigen prevalence ( measured by ICT card test ) as an indicator of LF infection , and it is one of the options of diagnostic tests used for measuring the prevalence of infection at each stage of the elimination process ( pre-MDA mapping , sentinel and spot check sites , and TAS ) . The Og4C3 antigen has also been used in a similar study in Haiti that investigated clustering of residual antigen-positive persons in low endemic areas [19] . In our study , three aspects of the Og4C3-positive individuals raised suspicion about the possibility of recent transmission . Firstly , one cluster of Og4C3-positive adults was located in very close proximity to the two ICT-positive children found during the 2011 TAS . Secondly , Og4C3 prevalence in our study was higher in migrants ( mostly from Samoa ) even though baseline antigen prevalence in 1999 was much lower in Samoa ( 4 . 5% ) than American Samoa ( 16 . 5% ) . If positive Og4C3 in our sample predominantly reflected infections in the remote past , prevalence would be expected to be lower in the migrants . Thirdly , we found significant spatial clustering of Og4C3 antigen , but not of Wb123 or Bm14 antibodies . If the Og4C3-positive adults in our study were predominantly infected in the remote past , clustering would have been much less likely , as demonstrated by the absence of clustering of antibody-positive adults . Data on microfilaraemia would have helped determine the presence of ongoing transmission , but this was not possible with a serum bank . Despite this limitation , we believe that our seroprevalence study of adults provided valuable information about potential residual infections in American Samoa . Similar studies should be considered elsewhere for post-MDA surveillance and for identifying high-risk populations and/or locations that might warrant more intense targeted surveillance . Higher LF seroprevalence in males corroborates findings from some of the previous studies in Samoa [7] , [18] and American Samoa [29] , and could be explained by more time spent outdoors for work and recreation compared to females . Interestingly , LF prevalence was found to be equivalent in males and females in 1999 prior to MDA in American Samoa , but Liang et al . reported a shift toward higher prevalence in males in sentinel site surveys conducted during and after MDA [29] . Our study ( using a much larger and more representative sample of the adult population ) confirms the higher prevalence among males post-MDA in American Samoa . Our results also indicate higher antigen prevalence in new migrants , who were mostly from a neighbouring LF-endemic country where transmission is still occurring in some areas . This suggests that human movement could be an important pathway for parasite reintroduction and subsequent resurgence of LF in American Samoa . Visitors and migrants travel for family , work , and economic reasons and usually live and work in close proximity to local American Samoans . Prolonged visits and cross migration are also common , and further increase the chances of parasite reintroduction . In addition , American Samoans also travel frequently to Samoa and other neighbouring Pacific Islands , and could be at significant risk of infection if staying for extended periods in areas of high prevalence . In 2012 , there were a total of 67 , 979 international arrivals to American Samoa ( with a local population of ∼56 , 000 ) . Of these 44 , 830 were citizens of other Pacific Islands , including 22 , 600 arrivals of returning citizens of American Samoa . A total of 20 , 082 arrivals were Samoan citizens , with 158 travelling for business , 4 , 158 for employment , 7 , 123 returning residents , and 8 , 757 visiting relatives [22] . Further research is required to improve understanding of the role of human movement in parasite reintroduction into American Samoa , and the consequent risk of resurgence based on travel patterns between Samoa and American Samoa , and LF prevalence at places of origin of visitors and new migrants . Cross-border strategies to coordinate efforts between Samoa and American Samoa for LF elimination and surveillance should also be considered . American Samoa's population mostly live on ancestral land , and most of the study participants had lived in the same village for most or all of their lives , thus providing an excellent opportunity to examine disease transmission patterns . Our results indicate that most of the spatial distribution of antigenaemia could be accounted for by geographical proximity of place of residence . Geo-spatial analysis provided some evidence of possible micro-spatial clustering of antigen-positive adults at the neighborhood level at two villages . Clustering at the household level suggests that the home environment is important in transmission even though one of the major vectors is day-biting . The close proximity between the elementary school attended by the two ICT-positive young children identified during the 2011 TAS and one of the possible village clusters suggests possible ongoing transmission . Our results indicate an average cluster size of 1 , 200 meters to 1 , 500 meters for antigenaemia , and the estimate of cluster size provides important information for the design of further studies to identify local transmission foci . Our study demonstrates the potential value of geospatial databases in post-intervention surveillance , monitoring , and evaluation for identifying possible micro-spatial clusters that might not be captured by routine TAS alone . Early detection of such clusters could be essential for timely intervention to reduce the risk of resurgence . Geospatial analysis could therefore potentially be used as an additional tool for verifying elimination status and for confirming that transmission has been interrupted . Changes in the spatial distribution of serological markers over time would also potentially be useful for identifying focal transmission , but unfortunately results of previous surveys in American Samoa were only located to the village rather than household level , and not of sufficiently high spatial resolution for the types of analyses conducted in this study or for comparing changes over time . Further operational research could also explore the use of geospatial data for informing programme delivery ( e . g . by identifying the size of clusters and delineating areas that might warrant targeted surveillance and monitoring ) ; calculating the distance of influence on infection risk that antigen-positive persons have on their near neighbours; and determining transmission threshold targets that include a spatial component rather than just a simple average prevalence for an entire evaluation unit . The accuracy of prevalence estimations in evaluation units will also depend on spatial heterogeneity within the boundaries of the unit . Risk of LF and drivers of transmission are unlikely to be entirely uniform within any evaluation units , and be determined by many factors such as climatic conditions , population density , urban versus rural areas , MDA coverage , and vector species and density . The average prevalence in an evaluation unit could therefore mask focal areas of high prevalence ( hotspots ) if they are surrounded by large areas of low prevalence . Consequently , estimations of average prevalence in an area could vary greatly depending on how evaluation units were determined . Hotspots are more likely to be missed if they are small , in evaluation areas with greater spatial heterogeneity in risks and drivers , and when prevalence is very low such as in the post-MDA surveillance phase . Careful definition of evaluation units will therefore be crucial for optimising the probability of identifying any residual hotspots of transmission or early resurgence . One of the challenges pertaining to geospatial methods of cluster detection when utilising point location data is that such data are prone to random error and random variation in the presence of rare disease events and/or inadequate representation of the population at risk . We therefore used a robust geostatistical method to identify the presence of geographical clustering in our point location data by partitioning the variation in data that was due to random error and the variation that is due to spatial clustering . Semivariography ( as utilized in this study ) demonstrated that spatial clustering was present in the study area ( Tutuila ) but does not identify the location of clusters . The location of clusters could be further investigated by using model-based geostatistics that account for diagnostic uncertainty and variation in factors such as climate , population , and entomological parameters to produce predictive risk maps of LF . Spatial decision support systems are being used for malaria elimination programs , and similar tools could also be useful for LF [30] . A geospatial platform could also be used to integrate environmental and entomological data with human surveillance data , and used to explore possible environmental drivers of disease transmission , the impact of vector control on elimination programs , and the potential for using xenomonitoring to enhance post-MDA surveillance . This study also demonstrates the usefulness of high-quality serum banks for investigating multiple diseases ( a dengue seroprevalence study was also conducted using the same serum bank [31] ) , and provides an example of successful collaboration between researchers of different diseases to improve the cost-effectiveness of field epidemiology investigations , which are often expensive and logistically challenging . We believe that the WHO's recommendations of integrating of LF surveillance activities with other population-based surveys are logistically feasible and practical . Our findings should be interpreted in light of potential limitations . First , the serum bank used for the study was collected for a leptospirosis study , and we could not ascertain whether participants had previously been diagnosed with or treated for LF , or participated in MDA in American Samoa or elsewhere . Only 28 participants ( 3 . 9% ) were recent migrants ( lived <5 years in American Samoa ) , and although they would not have been living in American Samoa during MDA activities , some might have received MDA in Samoa or other home countries . However , we have no reason to believe that our participants were biased with respect to MDA compliance locally or elsewhere . Second , there were only six participants with Og4C3 of >128 units , and 26 participants with Og4C3 of >32 units , and small numbers could have affected the accuracy of spatial analyses . Small numbers generally reduce the likelihood of identifying statistically significant associations , but despite this , we found significant results using robust tests and geospatial analyses . Third , participants in the serum bank included adults of all ages , but did not include children or adolescents . Results of TAS conducted at about the same time provided antigen prevalence data in 6–7 year old children , but data on antigen and antibody levels in children of all ages would help improve understanding of the application of diagnostic tests for post-MDA surveillance . Finally , participants were geo-located to place of residence , but LF infection could occur elsewhere , particularly in the presence of efficient day-biting vectors . If vectors were predominantly night-biting , clustering of infections could potentially be even more readily defined around household locations . This study provides preliminary results to support the importance of further research designed to specifically focus on improving understanding of disease transmission at the last stages of elimination when prevalence is very low; answering operational questions in LF elimination programs , especially the role of migration; developing tools to enhance the effectiveness of post-MDA surveillance and monitoring; and providing an evidence base for elimination strategies and targets . Follow up studies are being conducted in American Samoa to determine whether hotspots truly exist , develop models to quantify the significance of migrants in LF elimination , and explore the use of molecular xenomonitoring in the Pacific Island setting . The study also highlights the importance of assessing locally relevant risks for infection , which could vary significantly between places depending on cultural , societal , and environmental factors , as well as filarial species and mosquito vectors . The approach and results of this study are specifically relevant for the Samoan islands , but could also provide insight into LF transmission in other LF-endemic areas , and be pertinent to other Pacific Islands with similar vectors , lifestyle , culture , climate , environmental conditions , and migration patterns . | Lymphatic filariasis ( LF ) is caused by infection with filarial worms that are transmitted by mosquito bites . Globally , 120 million people are affected , and 40 million are disfigured and disabled by complications such as severe swelling of the legs ( elephantiasis ) . The Global Programme to Eliminate LF ( GPELF ) aims to interrupt disease transmission through mass drug administration ( MDA ) , and to control illness and suffering in affected persons . In American Samoa , significant progress has been made towards LF elimination , and antigen prevalence has dropped from 16 . 5% in 1999 to <1% in 2011/2012 after seven rounds of MDA . Current challenges include identification of any residual hotspots of ongoing transmission , and effective strategies for early identification of any resurgence . Our study examined the prevalence and spatial distribution of LF antigens and antibodies in American Samoan adults to improve understanding of LF transmission in an area of low prevalence , develop tools and strategies to more accurately verify interruption of transmission , and provide evidence-based guidance for future elimination strategies in American Samoa . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"american",
"samoa",
"medicine",
"and",
"health",
"sciences",
"infectious",
"disease",
"epidemiology",
"disease",
"eradication",
"spatial",
"epidemiology",
"vector-borne",
"diseases",
"tropical",
"diseases",
"geographical",
"locations",
"parasitic",
"diseases",
"health",
"care",
"plant",
"science",
"filariasis",
"global",
"health",
"plant",
"pathology",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"lymphatic",
"filariasis",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"environmental",
"epidemiology",
"epidemiology",
"people",
"and",
"places",
"infectious",
"disease",
"surveillance",
"helminth",
"infections",
"environmental",
"health",
"oceania",
"disease",
"surveillance",
"biology",
"and",
"life",
"sciences",
"disease",
"informatics"
] | 2014 | Seroprevalence and Spatial Epidemiology of Lymphatic Filariasis in American Samoa after Successful Mass Drug Administration |
Vector-borne diseases represent a threat to human and wildlife populations and mathematical models provide a means to understand and control epidemics involved in complex host-vector systems . The disease model studied here is a host-vector system with a relapsing class of host individuals , used to investigate tick-borne relapsing fever ( TBRF ) . Equilibrium analysis is performed for models with increasing numbers of relapses and multiple hosts and the disease reproduction number , R0 , is generalized to establish relationships with parameters that would result in the elimination of the disease . We show that host relapses in a single competent host-vector system is needed to maintain an endemic state . We show that the addition of an incompetent second host with no relapses increases the number of relapses needed for maintaining the pathogen in the first competent host system . Further , coupling of the system with hosts of differing competencies will always reduce R0 , making it more difficult for the system to reach an endemic state .
An important development in the study of infectious diseases is the application of mathematical models to understand the interplay between various factors that determine epidemiological processes . Many systems show a rich variety of dynamics that arise from nonlinear interactions ( due to the mixing of different infectious populations ) or temporal forcing ( caused by changes in the average contact rate ) [1] . Vector-borne diseases are additionally complex with interactions between multiple host and vector species [2–4] . Compartmental models , such as susceptible , infectious , and removed models ( SIR ) [5] , have been applied to many disease systems in an effort to examine system dynamics . In these epidemic models , susceptible individuals pass into the infective class , from which they transition to the removed class . For some diseases , recovered individuals may relapse with a reactivation of infection and revert back to an infective class . An example of such a system is found in van den Driessche et al . [6] , which included a relapsing rate between the susceptible and the same infected compartment . Adding additional infected compartments simulates disease systems in which there is a relapsing component , leading to a prolonged infectious period , presumed to be important to disease persistence . To our knowledge , the addition of a relapsing component has not been applied to a host-vector system . Noteworthy vector-borne relapsing diseases include tick-borne relapsing fever ( TBRF ) and malaria . An advantage of these types of models is the ability to vary parameters , while monitoring the overall effect on the disease system , allowing for the exploration of characteristics of the system that may not be well understood . Tick-borne relapsing fever ( TBRF ) is a cryptic disease that still causes significant morbidity and mortality worldwide , especially in African countries [7–10] . TBRF is a vector-borne zoonotic disease endemic to central Asia , Africa , and the Americas [11] , and is caused by infection with Borrelia spirochetes . All but one species of relapsing fever spirochetes are vectored by soft ticks ( Ornithodoros spp . ) [12] . Relapsing fever is characterized by recurring febrile episodes and generalized symptoms including headache , chills , myalgia , nausea , and vomiting[13] . There is a rapid onset of disease symptoms , with a febrile episode lasting 3–6 days , after which symptoms subside , only to return in 7–10 days . Symptoms are associated with large number of spirochetes present in the bloodstream ( spirochetemia ) , and subside when the host generates an antibody response against the variable major proteins ( Vmps ) . The Vmps are involved with antigenic variation , and relapsing fever Borrelia produce a new variant during infection , subsequently attaining high densities [14 , 15] . Little is known regarding the number of relapses in natural hosts , but studies have shown a range from 1 to 5 in experimentally infected animals [16] . In humans , there is an average relapse rate of three febrile episodes without treatment , but up to 13 relapses have been observed [17] . Ornithodoros spp . ticks are long-lived , fast feeding vectors that are known to live > 10 years , and have been shown to survive for up to five years without feeding [18] . Ornithodoros ticks are nidicolous ticks that rarely leave the confines of the host nest or burrow and are able to obtain a blood meal and detach from the host in < 90 min . Additionally , soft ticks only obtain a blood meal about once every 3 months; even when presented with the opportunity to feed daily . Ornithodoros ticks require several months between feedings and can survive years between feeding . The longevity of these ticks means that they outlive their rodent hosts , affording the potential to infect several cohorts of rodents over the course of the tick lifespan . Once infected ticks remain infected and infectious for the duration of their lifespan . Here , we model TBRF caused by infection with B . hermsii and vectored by O . hermsi . We parameterize the model with field-derived values from hosts on Wild Horse Island in Montana and a single genomic group I ( GGI ) strain of B . hermsii . The overall goal of this study was to develop a SIR model using TBRF dynamics to describe a host-vector system with a relapsing class of host individuals . First , using specific information from a TBRF system located on Wild Horse Island , MT , a model for the dynamics of a single host-vector interaction was developed . For models with increasing numbers of relapses and multiple hosts , equilibrium analysis was performed and R0 was generalized . Parameter values were considered in the model to provide theoretical criteria for population stability and to determine the parameters that would result in elimination of the disease . Finally , single and coupled host-vector systems were explored , focusing on the addition of less competent hosts and the number of relapses needed in order to maintain an endemic equilibrium . We use the model to ask several important biological questions pertaining to the TBRF system determining effect adding relapsing classes has on pathogen persistence and the effect of multiple host species with varying competency for acquiring and transmitting B . hermsii .
We sought to develop a model based on disease dynamics on Wild Horse Island ( WHI ) , Flathead Lake , Lake County , MT . WHI is the largest island ( ~2100 acres ) on Flathead Lake and like other islands on the lake has a limited diversity of rodent host species . WHI is almost exclusively inhabited by deer mice ( Peromyscus maniculatus ) and pine squirrels ( Tamiasciurus hudsonicus ) as the terrestrial rodents and provided an important opportunity to develop and parameterize a model including only two hosts . Although there are two genomic groups ( GGI and GGII ) of B . hermsii present on WHI , we parameterize the model using estimates for only GGI B . hermsii , as host competency experiments have primarily been performed with GGI B . hermsii [16] . A key assumption for host-vector disease modeling is the definition of the transmission term , which represents the contact between hosts and vectors . The formulation of the transmission term affects the reproduction number , R0 , which is a central predictor of disease systems [19] . For host-vector disease models , the transmission term includes the vector biting rate . This rate controls the pathogen transmission both from the vector-to-host and from the host-to-vector . The TBRF model follows frequency-dependent transmission assumptions through the biting rate , since a blood meal is only required approximately once every three months regardless of the host population density . Following this framework , hosts would likely experience an increasing number of bites as the vector population increased . Given a mathematical model for disease spread , R0 is an essential summary parameter . It is defined as the average number of secondary infections produced when one infected individual is introduced into a completely susceptible host population [20] . When R0 < 1 , the disease free equilibrium ( DFE ) at which the population remains in the absence of disease is locally asymptotically stable . However , if R0 > 1 , then the DFE is unstable and invasion is always possible ( see [21] ) and a new endemic equilibrium ( EE ) exists . For this study , R0 was extracted following the methodology developed in van den Driessche et al . [22] ( see also [23 , 24] ) for general compartmental disease models , which can be extended to more complicated host-vector disease systems [25 , 26] . Specific parameter values for this system have not yet been determined , but can be estimated from similar studies and from data collected on O . hermsi from laboratory experiments . The units of the rates are individuals per month . Table 1 summarizes the notation for all system parameters and variables . See Table 2 for specific model values used in all of the host-vector models . Note that parameters denoted with additional subscripts of ps and dm refers to values specific to the pine squirrel and deer mouse host-vector systems , respectively . The birth rates for host and vector are each set to a constant value ( β and βv , respectively ) and the compartmental death rates ( for host and vector ) are identical and set equal to birth rate . Then the death rates must be μs=μi1=⋯=μij=μr=βj+2 ( 1 ) and μsv=μiv=βv2 . ( 2 ) The growth rate of pine squirrels ( βps = 0 . 33 individuals per month ) is an average of the rates found in the literature , i . e . , four individuals per litter at 1 litter per year [27] . The growth rate of deer mice is also taken from average estimates from the literature; we estimate growth rate based on an average of three litters per year and four young per litter , ( βdm = 1 individual per month ) [28] . The death rates are determined from Eq ( 1 ) , which depends on the number of relapses in the system . For example , for a pine squirrel host-vector system with one relapse , all death rates would be 0 . 0825 . Life history dynamics of O . hermsi are not well documented and virtually nothing is known about the reproductive behavior and survival of these ticks in nature . Conservative estimates from the laboratory show that soft-bodied ticks lay on average five clutches over their approximately 10 year lifespan with roughly 50 eggs per clutch [29] ( T . Schwan personal communication ) . Thus , the vector birth rate is βv = 2 . 08 individuals per month . Following Eq ( 2 ) , we get death rates of μsv = μiv = 1 . 04 for the vector compartments . The rate at which an individual transitions among infected compartments and to the removed compartment is fixed and is assumed to be the same for all compartments . As more infected compartments are added to the system , the corresponding constant rates are γ = α = α1 = … = αj-1 , for j infected compartments . Field parameter estimates have not yet been made for these transition rates ( i . e . , relapse and recovery rates ) . Laboratory results from three pine squirrels indicate a transition rate of approximately 4 . 35 individuals per month for a single compartment ( Burgdorfer and Mavros 1970 ) . Then γ = α = α1 = … = αj-1 = 4 . 35 . Ticks are assumed to bite a host once every three months ( i . e . , f = 0 . 33 ) . Competency values are between 0 and 1 and thus modify the transmission rate of the infection by multiplying the biting rate . Burgdorfer and Mavros [16] observed a high competency in pine squirrels successfully infecting 3/3 animals by tick bite or injecting them with triturated ticks . Using the same methods , they challenged deer mice with B . hermsii and were unsuccessful in establishing infection . Thus , we used competency values cv = 0 . 95 for the probability of transmission for vectors , cps = 0 . 90 for pine squirrels , and cdm = 0 . 10 for deer mice . The carrying capacity for the pine squirrel and deer mouse system is determined specifically for WHI . On WHI there are approximately 425 ha of suitable habitat for pine squirrels with up to a maximum of 2 individuals per suitable habitat patch and approximately 850 ha of suitable deer mouse habitat with a conservative estimate of just less than 12 mice per ha [28] . Thus , the total number of pine squirrels ( Nps ) is estimated at 850 and total number of deer mice ( Ndm ) is estimated at 10 , 000 . The soft bodied tick population ( Nv ) is virtually unknown , however , we assume that they are limited to the nests of their hosts . Initial field collections have found as many as 14 ticks in one nest on the island [30]; other collection efforts show > 300 ticks can be collected from a single nest or snag [31] . Because the estimates of ticks per nest vary largely between our limited collection on WHI and the literature we chose a conservative number of ticks . We estimate that each squirrel has less than one nest ( because of juveniles in the system ) , and each nest is inhabited by 14 ticks . We found no ticks in nest material collected from deer mice , however , nest material collected during the human outbreak in 2002 yielded 14 O . hermsi; the carcasses of two deer mice were found nearby and American Robins ( Turdus migratorius ) had been nesting there [30] . Thus it is nearly impossible to estimate the average number of ticks in a deer mouse nest , or if in fact they are coming in contact with ticks while visiting other nests . We used an estimate of 20 , 000 total ticks on the island split equally among host systems . We chose a conservative estimate of 1% of all ticks are infected , as none of 12 of 14 field collected ticks were found to be infected [30] . Thus , we used Sv ( 0 ) = 9 , 900 ticks for the single host-vector system and Sv ( 0 ) = 19 , 800 ticks for the coupled host-vector system .
A model for the dynamics of TBRF in a single host-vector system is considered ( see Fig 1A ) . The following assumptions are used to establish a model that is appropriate for the WHI TBRF system for the host pine squirrel and soft tick vector , O . hermsi . ( 1 ) The only sources of infection occur between the bite of an infective vector and susceptible host and between a bite of a susceptible vector and infective host ( i . e . there are no horizontal or vertical transmission events ) . ( 2 ) The vector becomes infected and infectious for life immediately upon biting an infectious host . ( 3 ) The transmission terms are frequency-dependent through the biting rate , f . ( 4 ) The hosts relapse to different infected compartments ( i . e . different serotypes within the hosts caused by antigenic variation ) at rate α and recover from the disease at rate γ . ( 5 ) Though mortality rates are noted to differ for each compartment , we assume a constant total population for both hosts and vectors ( N and Nv , respectively ) . Thus , recruitment ( or birth ) and the sum of the removal ( or death ) rates from each compartment must be equal ( Eqs 1 and 2 ) . The generalized system for the infection dynamics in a single host-vector system with j—1 relapsing rates for j = 1 infected compartments describes the number of susceptible hosts S ( t ) , infectious hosts Ik ( t ) , removed hosts R ( t ) , susceptible vectors Sv ( t ) , and infected vectors Iv ( t ) , where the total host population is N=S+∑k=1jIk+R and the total vector population is Nv = Sv + Iv ( see Fig 1A for a compartmental diagram and Table 1 for parameter definitions ) . The equations are Host equations: S•=βS−fcvIvSN−μsSI•1=fcvIvSN−α1I1−μi1I1I•2=α1I1−α2I2−μi2I2 . . . I•j−1=αj−2Ij−2−αj−1Ij−1−μi ( j−1 ) Ij−1I•j=αj−1Ij−1−γjIj−μijIjR•j=γjIj−μrR . ( 3 ) Vector equations: S•v=βvSv−fcSvN∑i=1jIi−μsvSvI•v=fcSvN∑i=1jIi−μivIv . ( 4 ) To evaluate the invasiveness of the disease in this system , we extracted R0 following the techniques developed by van den Driessche and Watmough [22] by sequentially adding infected compartments ( see S1 for equilibrium analysis and derivations ) . The form of R0 was then inferred for j—1 relapsing rates between j infected compartments as R0=fccvμivSv ( 0 ) N ( 0 ) [1α1+μi1[1+α1α2+μi2[⋯[1+αj−1γ+μij]]]] . ( 5 ) R0 is directly proportional to the biting rate ( f ) , competency values ( c and cv ) , and the ratio of initial vectors to initial hosts ( Sv ( 0 ) N ( 0 ) ) and inversely proportional to the vector death rate ( μiv ) and the rate that moves individuals out of the infected compartments ( α α1 , … . , αj-1 , μi1 , … , μij , and γ ) . In addition , a pattern emerges as more infected compartments are added: a nesting sequence of terms that increase the value of R0 and potentially contribute to a change in stability of the DFE . To illustrate this concept , we used the pine squirrel host parameters ( Table 2 ) for increasing number of infected compartments and plotted R0 . R0 crosses 1 at between j = 4 and j = 5 infected compartments ( i . e . , four relapses; Fig 2 ) . Here , the single host-vector model is expanded to include two hosts , namely pine squirrels and deer mice . Fig 1B is a compartmental diagram for the two systems with no relapses . The first host-vector system ( Sps , I1 , ps , Rrs , Sv , ps , Iv , ps ) is coupled with the second system ( Sdm , I1 , dm , Rdm , Sv , dm , Iv , dm ) through ticks biting either host species , with parameter f , and is further controlled by competency values of either the ticks ( cv ) or hosts ( cps or cdm for pine squirrel and deer mice , respectively ) . Transmission occurs through three mechanisms: 1 ) fcv , which is the biting rate modified by the tick competency through which an infected tick bites a host from each system , 2 ) fcps , which is the biting rate modified by the pine squirrel competency in that a susceptible tick bites an infected pine squirrel , and 3 ) fcdm , which is the biting rate modified by the deer mouse competency , such that a susceptible tick bites an infected deer mouse . The parameters remain as in the single host vector system , denoted with additional subscripts to represent the respective host-vector system ( either ps or dm ) , and are explained in Tables 1–2 . The generalized system for the infection dynamics in a coupled host-vector system with j—1 relapsing rates for j = 1 infected compartments describes the pine squirrel system with the number of susceptible hosts Sps ( t ) , infectious hosts Ik , ps ( t ) , and removed hosts Rps ( t ) . The total pine squirrel host population is Nps=Sps+∑k=1jIk , ps+Rps . Likewise , the deer mouse host system consists of susceptible hosts Sdm ( t ) , infectious hosts Ik , dm ( t ) , and removed hosts Rdm ( t ) with a total deer mouse host population of Ndm=Sdm+∑k=1jIk , dm+Rdm . The vector compartments are susceptible vectors Sv ( t ) , infected vectors Iv ( t ) and a total vector population of Nv = Sv + Iv . The equations are Pine squirrel host system: S•ps=βSps−fcvIvSpsNps−μs , psSpsI•1 , ps=fcvIvSpsNps−α1 , psI1 , ps−μi1 , psI1 , psI•2 , ps=α1 , psI1 , ps−α2 , psI2 , ps−μi2 , psI2 , ps⋮I•j−1 , ps=αj−2 , psIj−2 , ps−αj−1 , psIj−1 , ps−μi ( j−1 ) , psIj−1 , psI•j , ps=αj−1 , psIj−1 , ps−γj , psIj , ps−μij , psIj , psR•j , ps=γj , psIj , ps−μr , psRps . ( 6 ) Deer mouse host system: S•dm=βSdm−fcvIvSdmNdm−μs , dmSdmI•1 , dm=fcvIvSdmNdm−α1 , dmI1 , dm−μi1 , dmI1 , dmI•2 , dm=α1 , dmI1 , dm−α2 , dmI2 , dm−μi2 , dmI2 , dm⋮I•j−1 , dm=αj−2 , dmIj−2 , dm−αj−1 , dmIj−1 , dm−μi ( j−1 ) , dmIj−1 , dmI•j , dm=αj−1 , dmIj−1 , dm−γj , dmIj , dm−μij , dmIj , dmR•j , dm=γj , dmIj , dm−μr , dmRdm . ( 7 ) Coupled vector system: S•v=βvSv−fcpsSvNps∑i=1jIi , ps−fcdmSvNdm∑i=1jIi , dm−μsvSvI•v=fcpsSvNps∑i=1jIi , ps+fcdmSvNdm∑i=1jIi , dm−μivIv . ( 8 ) As with the single host-vector system , we performed equilibrium analysis ( S2 ) and the form of R0 was inferred for j—1 relapsing rates between j infected compartments . Where PS=cpsNps ( 0 ) [1 ( ∝1 , ps+μi1 , ps ) [1+∝1 , psα2 , ps+μi2 , ps[⋯[1+∝j−1 , psγ+μij , ps]⋯]]]andDM=cdmNdm ( 0 ) [1 ( ∝1 , dm+μi1 , dm ) [1+∝1 , dmα2 , dm+μi2 , dm[⋯[1+∝j−1 , dmγ+μij , dm]⋯]]] . ( 10 ) From the coupled host-vector system it is apparent that R0 has the additional dependency for both the host competency values ( cps and cdm ) . Since competency values are probabilities between 0 and 1 , then they will always decrease the value of R0 as they decrease . Like the single host-vector system , a pattern emerges as more infected compartments are added to each host system ( Eqs 9 and 10 ) : a nested sequence of terms that increase the value of R0 and potentially contribute to a change in stability of the DFE . To compare the results of the number of relapses needed for R0 > 1 in the coupled host-vector system with the single host-vector , we added an incompetent deer mouse host system ( cdm = 0 . 2 ) and increased the number of relapses in a pine squirrel host system until R0 reached 1 . R0 crosses 1 at between j = 7 and j = 8 infected compartments ( seven relapses; Fig 3 ) .
Incorporating a relapsing component into a host-vector SIR modeling framework represents a step towards a better understanding and representation of complex disease systems . We investigated the disease dynamics of TBRF and used the model to better understand the underlying dynamics and interactions among spirochetes , rodent hosts , and tick vectors that contribute to pathogen persistence . Disease models were presented that describes ( 1 ) a single host-vector system with a single relapsing class of host individuals , and generalized to j-1 relapsing host classes and ( 2 ) a coupled host-vector model generalized as above to j -1 relapsing host classes . Analytical techniques allowed for the generalization of R0 with increasing numbers of relapses , and parameters were identified that affect the elimination or persistence of the pathogen ( e . g . , biting rates , competency values , and population numbers ) . In the single host-vector system , R0 is directly proportional to the biting rate ( f ) , competency values ( c and cv ) , and the ratio of initial vectors to initial hosts ( Sv ( 0 ) N ( 0 ) ) . An inverse relationship exists between R0 and the vector death rate ( μiv ) and the rate that moves individuals out of the infected compartments ( α1 , … . , αj-1 , μi1 , … , μij , and γ ) . When additional relapsing classes are added to the system , R0 always increases because of the addition of a nested sequence of terms that is always > 1 ( Eq 5 ) . The coupled host-vector system has similar dependencies with additional interesting dynamics that may be very important to understanding pathogen persistence and host diversity . Coupling of the system with hosts of lower competencies will always reduce R0 ( Eqs 9 and 10 ) . As the number of incompetent hosts available as blood meals for infected ticks increases , an effect comparable to the dilution effect occurs and R0 always decreases , leading to DFE . The dilution effect states that in the presence of a second , less competent species , competent host-vector encounters leading to transmission events may be replaced by incompetent host-vector encounters that do not end in a pathogen transmission event , thus decreasing R0 [3 , 4] . The model presented here addresses the presence of multiple hosts with varying competencies and a single pathogen , however , the model can be extended to address not only differences in host species diversity but also the presence of > 1 pathogen strain . The genetics of B . hermsii have been well characterized and isolates have been shown to fall into two distinct genomic groups , referred to as genomic group I and II ( GGI and GGII ) [32 , 33] . The presence of both genomic groups of B . hermsii has been documented on WHI , while only GGII B . hermsii has been found to date on the mainland around Flathead Lake where host species diversity is greater than that of the WHI . Field investigations of rodents on WHI confirmed infection in a single deer mouse ( Peromyscus maniculatus ) infected with GGII B . hermsii ( Johnson et al . In . Prep . ) . This prompted a laboratory experiment in which we infected deer mice with both GGI and GGII B . hermsii and monitored them for infection . We challenged deer mice with infection via needle inoculation and infectious tick bite and observed that deer mice show no susceptibility to GGI but are highly susceptible to GGII spirochetes ( Johnson et al . In . Prep . ) . These findings were in contrast with Burgdorfer and Mavros [16] who were unable to establish infection in deer mice , however , they used infected ticks from a TBRF outbreak near Spokane , WA , U . S . A . , which resulted in isolation of GGI B . hermsii . The coupled system presented here could be used to examine the effects of not only host species with varying competencies , but also diverse host communities in the presence of B . hermsii GGI and GGII . The presence of both genomic groups simultaneously may result in a dampening of the dilution effect if GGII is able to infect a diverse array of host species even though GGI is more species limited . Rodent trapping and tick collection on WHI showed one squirrel and one tick infected with GGI and three squirrels infected with GGII . On WHI , 95% of all pine squirrels captured were seropositive for relapsing fever spirochetes while only 4% of deer mice possessed antibodies ( Johnson et al . In Prep . ) . All infected individuals at mainland sites with diverse host species were infected with GGII spirochetes ( Johnson et al . In Prep . ) . Although there are limitations to the model presented here , the model is an important first step in understanding a relapsing host-vector disease system . All known complexities of the system were not addressed at this time , including incorporation of GGII strains of B . hermsii which can infect deer mice and possibly a wide range of other potential hosts ( Johnson et al . In Prep . ) . Although there is conflicting evidence at the rate which transovarial transmission of B . hermsii occurs in O . hermsi , we can see from the R0 calculation that Iv does not appear in the equation and therefore will have little impact on disease persistence in the presence of hosts . However , the existence of transovarial transmission may provide insight into the implication of O . hermsi serving as the reservoir for B . hermsii , i . e . , the ability to maintain infectious ticks in a prolonged absence of competent hosts and/or hosts in general . Additionally , the model could be used to explore drivers in the host and vector communities and prevention/intervention strategies may be explored to identify the effectiveness of host control versus vector control . Further , this may provide insight into human protective measures and the effectiveness of control strategies such as host vaccination; simulations could be run to assess the efficacy of control programs such as vaccination regimes and vector control . Ecological factors including biotic and abiotic interactions may play a primary role in the emergence and persistence of infectious diseases [34–39] . Understanding the complete epidemiology of a disease is crucial to advancing the ability to predict and control outbreaks in human and wildlife populations , however , this is rarely an attainable goal . Sonenshine [40] outlines the sequence of steps typically undertaken when attempting to understand the epidemiology of a given system . The pathway typically begins with the identification of a clinical syndrome , followed by discovery of the causative disease agent , and then the identification of the source of the agent in nature . The final step includes investigating the often complex biology and ecology of the hosts and/or vectors involved . Given the difficulty frequently encountered when attempting to study a disease in nature , the last step is often the most difficult . The application of advanced modeling techniques to poorly understood systems is often the only way to begin to understand the drivers of these systems . The ecological dynamics of relapsing fever systems around the world are poorly understood . Here we use a North American system of relapsing fever caused by B . hermsii; however , information gathered from this modeling exercise can be applied to TBRF systems around the world . TBRF remains a major public health threat in Africa [41] . In addition to other TBRF systems , the ideas presented here may provide the groundwork for relapsing components to be included in other disease systems with greater public health implications such as malaria . | An important development in the study of infectious diseases is the application of mathematical models to understand the interplay between various factors that determine epidemiological processes . Vector-borne diseases are additionally complex with interactions between multiple host and vector species . Understanding the transmission dynamics of vector-borne diseases is an important step towards controlling outbreaks and mitigating human infection risk . Identifying the biotic and abiotic interactions and mechanisms that may contribute to disease emergence , establishment and persistence is necessary for assessing current and future disease risk , as well as developing effective control strategies . Tick-borne relapsing fever ( TBRF ) is found around the world and is caused by several species of Borrelia spirochetes , which are vectored by soft ticks of the genus Ornithodoros . TBRF is a cryptic disease that still causes significant morbidity and mortality , especially in some African countries . Here , we develop and adapt a compartmentalized mathematical model ( SIR ) with a relapsing component to investigate the dynamics of TBRF . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"death",
"rates",
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"deer",
"ixodes",
"pathology",
"and",
"laboratory",
"medicine",
"ruminants",
"infectious",
"disease",
"epidemiology",
"demography",
"vector-borne",
"diseases",
"vertebrates",
"animals",
"mammals",
"population",
"biology",
"ticks",
"infectious",
"disease",
"control",
"infectious",
"diseases",
"epidemiology",
"pathogenesis",
"disease",
"vectors",
"arthropoda",
"people",
"and",
"places",
"population",
"metrics",
"rodents",
"arachnida",
"squirrels",
"host-pathogen",
"interactions",
"biology",
"and",
"life",
"sciences",
"organisms"
] | 2016 | Modeling Relapsing Disease Dynamics in a Host-Vector Community |
Icosahedral double-stranded DNA viruses use a single portal for genome delivery and packaging . The extensive structural similarity revealed by such portals in diverse viruses , as well as their invariable positioning at a unique icosahedral vertex , led to the consensus that a particular , highly conserved vertex-portal architecture is essential for viral DNA translocations . Here we present an exception to this paradigm by demonstrating that genome delivery and packaging in the virus Acanthamoeba polyphaga mimivirus occur through two distinct portals . By using high-resolution techniques , including electron tomography and cryo-scanning electron microscopy , we show that Mimivirus genome delivery entails a large-scale conformational change of the capsid , whereby five icosahedral faces open up . This opening , which occurs at a unique vertex of the capsid that we coined the “stargate” , allows for the formation of a massive membrane conduit through which the viral DNA is released . A transient aperture centered at an icosahedral face distal to the DNA delivery site acts as a non-vertex DNA packaging portal . In conjunction with comparative genomic studies , our observations imply a viral packaging pathway akin to bacterial DNA segregation , which might be shared by diverse internal membrane–containing viruses .
The prevailing model for genome translocations in icosahedral viruses entails a molecular motor that is localized at a single vertex and comprises a packaging ATPase and a portal complex [1–5] . The particular structural features revealed by the vertex-portal assembly have been argued to facilitate both genome delivery [6] and genome encapsidation [3 , 6 , 7] . Although the functional implications of these features have been recently challenged [8 , 9] , their apparent conservation led to the paradigm that a single vertex-portal system plays a crucial and general role in both genome injection and packaging in icosahedral viruses [6] . Vertex-portal assemblies were , however , characterized only in herpesviruses that contain an external lipid membrane [3 , 4 , 10] , and in tailed double-stranded DNA ( dsDNA ) bacteriophages in which membranes are absent [1 , 5–7 , 11] . This point is noteworthy in light of recent studies , which implied that DNA packaging machinery in viruses containing an inner membrane layer is fundamentally different from the vertex-portal apparatus of herpesviruses and bacteriophages [12–14] . Specifically , inner membrane–containing viruses were shown to contain putative DNA-packaging ATPases that , in addition to the regular Walker A and B motifs , carry a conserved motif that might act as a membrane anchor [12–14] . The structural aspects that underlie genome translocation mechanisms deployed by these viruses remain , however , largely unknown [15] . The amoeba-infecting virus Acanthamoeba polyphaga mimivirus is a member of the nucleocytoplasmic large DNA viruses ( NCLDV ) clade that comprises several eukaryote-infecting viral families such as the Phycodnaviridae , Iridoviridae , and Asfarviridae [16] . As in all members of NCLDVs , the Mimivirus is composed of a core containing a dsDNA genome , which is surrounded by a lipid membrane that underlies an icosahedral capsid [17–19] . The capsid is , in turn , covered by closely packed 120-nm-long fibers that form a dense matrix at their attachment site [17–19] . The closely packed fibers and the dense layer at the base of these fibers represent a unique feature of the Mimivirus . In addition , a single modified vertex has been detected in mature particles [18] . With a 1 . 2–mega base pair ( Mbp ) dsDNA genome and a particle size of ∼750 nm , the Mimivirus represents the largest virus documented so far , blurring the established division between viruses and single-cell organisms [17 , 18 , 20] . Prompted by these unique features , we conducted high-resolution studies of the Mimivirus life cycle within its amoeba host , focusing on genome delivery and packaging stages that remain poorly understood in all members of the NCLDV clade . By performing cryo-scanning electron microscopy and electron tomography on cryo-preserved host cells at different post-infection time points , we demonstrate that DNA exit occurs in phagosome-enclosed viral particles through a massive opening of five icosahedral faces of the capsid . This large-scale capsid reorganization , which occurs at a unique , structurally modified icosahedral vertex , allows for the fusion of the internal viral membrane with the membrane of the host phagosome . The fusion leads , in turn , to the formation of a massive membrane conduit through which DNA delivery occurs . In conjunction with single-particle reconstruction studies that indicated the presence of two successive membrane layers underlying the Mimivirus protein shell [18] , these observations raise the possibility that the Mimivirus genome is released into the host cytoplasm and is translocated toward the host nucleus enclosed within a vesicle that is derived from the viral inner membrane . We further show that DNA packaging into preformed Mimivirus procapsids proceeds through a non-vertex portal , transiently formed at an icosahedral face distal to the DNA delivery site . Along with comparative genomic studies [12 , 13] , these results imply a viral packaging pathway reminiscent of DNA segregation in bacteria , a pathway that might be common to internal-membrane–containing viruses . Taken together , the observations reported here may indicate that Mimivirus and potentially other large dsDNA viruses have evolved mechanisms that allow them to effectively cope with the exit and entry of particularly large genomes .
Extracellular Mimivirus particles were sectioned following cryo-fixation and examined by transmission electron microscopy ( TEM ) . Notably , all TEM specimens in the current study were preserved through the high-pressure freezing technique that , in sharp contrast to conventional chemical fixation protocols , allows for instantaneous immobilization of all structures in their native morphology . As such , this preservation method is generally considered to be highly reliable and hence optimal for electron tomography studies [21] . The extracellular particles reveal an unprecedented 5-fold star-shaped structure that is localized at a single icosahedral vertex and extends along the whole length of the five icosahedral edges that are centered around this unique vertex ( Figure 1A ) . Geometric considerations of an icosahedron structure modified along five icosahedral edges that is randomly sliced indicate that if all viral particles include such a massive assembly , parts of this structure should be discerned in 75%–80% of the sections used for TEM analysis , depending on the thickness ( 70–80 nm ) of the sections . In ∼500 extracellular viruses examined , the 5-fold assembly or parts thereof were detected in ∼400 particles ( 80% ) , thus demonstrating that all viral particles contain this structure . None of the examined extracellular viral particles or of the intracellular particles ( see below ) revealed more than one star-shaped structure per particle , a finding fully consistent with single-particle cryo-TEM studies in which a single modified vertex was detected [18] . The presence of the star-shaped assembly was further confirmed by cryo-TEM studies conducted on whole extracellular Mimivirus particles that were vitrified in their hydrated state . Due to the interference of the extremely dense fiber layer that surrounds the viral capsids [18] , the 5-fold structure could not be detected in mature particles , but was clearly and consistently discerned in immature , fiber-less viruses that constitute a small yet significant ( ∼10% ) population of the viruses that are released upon lysis of the amoeba cells at the completion of the infection cycle ( Figure 1B ) . To ascertain that the 5-fold assembly represents a general and genuine feature , >500 extracellular Mimivirus particles were analyzed by cryo-scanning electron microscopy ( cryo-SEM ) . These studies corroborate the presence of a massive 5-fold structure at a unique vertex of the particle . The assembly is detected in fiber-covered Mimivirus where it appears as crevices , but is particularly conspicuous and consistently revealed in immature fiber-less particles , where it takes the form of prominent ridges ( Figure 1C and 1D , respectively ) . The crevices that characterize the 5-fold structure in mature particles ( Figure 1C ) imply that this particular structure is depleted of fibers , in contrast to all other regions of the capsid . Electron tomography ( Figure 1E and Video S1 ) and volume-reconstruction analyses ( Figure 1F–1H ) were performed on viral particles within infected amoeba cells at final infection stages ( 12 hours post-infection ) , where cells are crammed with mature viruses . The analyses indicate that the Mimivirus capsid is composed of two superimposed shells characterized by conspicuously different densities . This observation , obtained from three tomography analyses conducted on different intracellular viral particles , is consistent with single-particle reconstruction studies , which indicated the presence of a protein shell surrounded by a distinct layer that corresponds to a dense base of fibers [18] . In addition to the two shells , a prominent star-shaped structure is discerned in the intracellular Mimivirus particles ( Figure 1E ) . Volume-reconstruction analysis of the star-shaped structure indicates that the outer shell adopts a partially open configuration ( corresponding to the dark star-shaped region in Figure 1F ) . This open region is , however , completely sealed by the underlying inner shell ( Figure 1G ) , an observation compatible with the ridges that delineate the 5-fold star-shaped structure in immature fiber-less particles ( Figure 1D ) . Figure 1H , which represents a superposition of the two shells , demonstrates the perfect match between the ridges at the inner shell and the regions in which the outer shell is missing . A recent study implied that the initial stages of Mimivirus infection occurs by phagocytosis [19] . Our observations support this notion by demonstrating the presence of phagosomes containing one or several viral particles within infected amoeba cells at early ( 2–3 h ) post-infection time points ( Figures 2 and 3 ) . The different morphological aspects revealed by the phagosome-enclosed viral particles are straightforwardly interpreted as a result of sectioning the viruses along different planes , as clarified in the inset in Figure 2A and demonstrated in Video S2 . Specifically , in a randomly sliced section that contains the star-shape assembly and is parallel to field of view depicted in the inset , a star-shape structure is detected , as demonstrated in Figure 1A , 1E–1H , and in Figure 2A . If , on the other hand , the TEM section is sliced along a plane parallel to that illustrated in the inset yet located below the star-shape assembly , only unmodified vertices will be detected , as indeed is the case for the viral particle shown in Figure 2B . Sections perpendicular to a single star-like assembly should reveal either one or two modified vertices , which correspond to slices along the blue and red lines in the inset , respectively . These two morphological aspects are indeed manifested by the viral particles shown in Figure 2C and in Figure 3A ( particle 2 ) , which reveal a single modified vertex , and by particle 1 in Figure 3A , in which two modified vertices ( marked by red arrowheads ) are visible . Such two modified icosahedral edges that belong to the same star-shaped assembly are particularly evident in thick sections that are sliced along the red line in the inset , as indeed shown in Figure 3B and in Video S2 . In conjunction with the geometric considerations described above , a statistical analysis conducted on more than 100 phagosome-enclosed viral particles ( which basically represent mature virions ) indicate that all intracellular Mimivirus particles contain a modified , star-shaped vertex , and that this vertex is unique , as is the case for the extracellular Mimivirus particles . Figure 3A shows a tomographic slice of a phagosome in which three viral particles were captured at three successive uncoating stages . Volume reconstruction of the particle 1 ( early uncoating ) reveals that in this virus , both the outer ( red ) and inner ( orange ) capsid layers are opened at the star-shaped assembly ( Figure 3B ) . The opening of both shells is in contrast with the morphology revealed by extracellular viruses ( Figure 1 ) , as well as by intracellular particles during early phagocytic stages ( Figure 2C ) , in which the inner shell appears to be completely sealed . This opening allows for the lipid layer underlying the capsid shell ( blue layer in Figure 3B ) to protrude and extend towards the phagocytic membrane . This stage is represented by the viral particle 2 in Figure 3A . The final uncoating stage is demonstrated by the particle 3 ( Figure 3A ) . Surface rendering analysis of this virus demonstrates a massive opening of five triangular icosahedral faces that occurs at the star-shaped vertex and results in a fusion of the viral ( light blue ) and phagocytic ( dark blue ) membranes ( Figure 3C ) . The three uncoating stages are visible in the tomogram shown in Video S2 . We interpret these observations as indicating that the star-shaped structure , which we coin “stargate” , represents a device that mediates a large-scale capsid opening , thus allowing for the protrusion of the inner viral membrane and a subsequent viral-phagosome membrane fusion . This fusion results in the formation of a massive membrane tube through which the genome core is released into the host cytoplasm . The notion that DNA delivery occurs following the formation of a membrane conduit is supported by the presence of empty capsids within phagosomes ( our observations and [19] ) . The large-scale capsid opening at the stargate site , and the membrane tube are depicted in a schematic model ( Figure 4 ) , which is based on the tomography ( Figure 3A and Video S2 ) and surface rendering ( Figure 3C ) of particle 3 in Figure 3A . To identify the factors that promote the stargate opening within the host phagosome , and in light of extensive fusion of lysosomes with phagosomes in which viral uncoating occurs ( Figure 3A and Video S2 ) , isolated Mimivirus particles were exposed to acidic conditions ( pH 6 . 5 , 5 . 5 , and 4 . 5 ) in the absence or presence of lysozyme . None of these treatments triggered stargate opening , implying that other or additional factors are involved in effecting this structural reorganization . Exposure of particles to elevated temperature ( 83 °C ) for 30 min resulted in a release of membranal structures that specifically occurred at the stargate site in ∼10% of the particles ( Figure 5A ) . While physiologically irrelevant , this finding implies that the stargate represents a structurally susceptible site , a conjecture further supported by the observation that a small population ( <1% ) of extracellular particles reveals a conspicuous 5-fold opening ( Figure 5B ) . These capsids might represent faulty viral particles , or particles that have ejected their genome and then released to the medium upon viral-induced lysis of the host cells at the completion of the infection cycle . Following release , the Mimivirus genome is imported into the host nucleus and then translocated to a cytoplasmic viral factory where viral assembly occurs [19] . TEM studies of infected and cryo-fixed amoeba cells reveal that already at 8 h post-infection , viral factories are studded with empty , fiber-less procapsids that are only partially assembled , as well as with icosahedral procapsids undergoing DNA packaging ( Figures 6 and 7 ) [19] . The occurrence of DNA packaging into procapsids at the periphery of the factories ( green arrowheads in Figures 6 and 7 ) was supported by specific DNA staining and Br-dU experiments ( unpublished data ) . Intriguingly , in some particles , the genome appeared to be translocated at a vertex ( Figure 6A ) [19] , whereas in others , DNA translocation proceeds through an aperture located at an icosahedral face ( Figure 6B and 6C ) . A statistical survey of a large number ( >50 ) of intracellular viral factories indicated that at any thin section of the factory analyzed in TEM , 20–25 viral particles are present at various stages of assembly . Out of these assembling virions , 4–5 particles were captured at the stage of DNA packaging , and within this population , packaging through a face-located aperture , as shown in Figure 6B and 6C , was consistently detected in 2–3 virions . Thus , in more than 200 analyzed particles that undergo DNA packaging , a face-centered rather than a vertex-centered packaging is visible in more than 120 ( ∼60% ) particles . Projection images derived from TEM studies of thin sections cannot provide , however , unequivocal data on the precise site of the packaging process , as such data can be masked or incorrectly interpreted due to the angle of the site within the TEM section relative to the electron beam . To obtain deeper insights into the DNA packaging process in Mimivirus , we performed electron tomography and volume reconstruction analyses on three randomly chosen procapsids during their assembly on the periphery of the viral factories . A slice of a tomogram obtained from one of these assembling procapsids ( Figure 7A; the whole tomogram is shown in Video S3 ) demonstrates that DNA packaging proceeds through an aperture that spans the outer and inner capsid shells , as well as the internal membrane , and is located at the center of an icosahedral face . The aperture , which is sealed following completion of DNA packaging as implied by the structure of mature particles , adopts a cone shape with diameters of 35 nm and 20 nm at the outer and inner shells , respectively . These features , clearly discernible in the reconstructed volume of the particle ( Figure 7B ) , are detected in all three tomograms of assembling procapsids . Notably , whenever stargates are discerned in electron microscopy sections of assembling viral particles , they are invariably detected at the distal site of the factory , pointing away from the replication center ( Figures 6 and 7 ) . This finding , which is consistent with earlier observations [19] , is particularly evident in tomograms obtained from relatively thick sections ( Figure 7 and Video S3 ) . To substantiate our TEM results , we have isolated viral factories by gentle lysis of infected amoeba cells at 8–10 h post-infection , thus capturing successive assembly stages . SEM studies of factories isolated at 8 h post-infection show immature viral particles that abut on the periphery of the factories and reveal conspicuous stargates ( Figure 8A and 8B ) . Due to the dense fiber layer , stargates are hardly discernible in SEM analysis of mature particles , which are located further away from the periphery . Notably , in viral factories isolated at a 10 h post-infection ( Figure 8C ) , only mature particles , which presumably cover and mask the immature particles , can be detected . Thus , the SEM results , obtained from >50 isolated viral factories , corroborate the TEM studies conducted on intracellular factories , and strongly imply that the stargate structures represent an early stage of the viral assembly .
Mimivirus infection is initiated by phagocytosis [19] , and genome delivery occurs upon exposure of the virus to cues within the host phagosome . While the nature of these cues remains unknown , detection of multiple lysosomes undergoing fusion with the phagosomes ( Figure 3A and Video S2 ) may imply that lysosomal activity promotes the opening of the viral capsid . The observations reported here indicate that this opening entails a unique portal , the stargate , which is located at a single icosahedral vertex ( Figures 1–5 ) , in keeping with previous single-particle studies [18] , in which a single modified vertex has been identified . These studies , as well as our electron tomography observations ( Figure 1E–1H ) revealed that the Mimivirus is composed of a protein shell surrounded by an outer layer corresponding to a dense base of fibers . Our cryo-TEM ( Figure 1B ) , cryo-SEM ( Figure 1C and 1D ) , and electron tomography of cryo-fixed specimens ( Figure 1E–1H ) indicate that the stargate is located within the protein shell , extending along the whole length of five icosahedral edges that are centered around a single icosahedral vertex , thus forming an assembly of unprecedented morphology and dimensions . The icosahedral edges appear as prominent ridges in the protein shell , which are clearly discerned in immature , extracellular viral particles that lack the dense fiber layer ( Figure 1D ) . Our observations further indicate that while the outer shell surrounds most of the inner protein shell , it is absent along the icosahedral edges that constitute the stargate ( Figure 1F ) . This fiber-less region is likely to enable the cues that trigger the opening of the stargate to reach their specific target in the inner protein shell . Notably , the stargate is detected in extracellular Mimivirus particles , in phagosome-enclosed virions , in mature intracellular viral particles present within the amoeba host cells at the final infection stage ( Figures 1 and 2 and Video S1 ) , as well as in assembling virions ( Figures 6–8 ) , thus indicating that this prominent assembly is present in the Mimivirus capsid throughout the virus life cycle . The large-scale conformational change of the capsid whereby the five icosahedral faces centered on the unique stargate vertex open up , allows the extrusion of the viral membrane that underlies the viral protein shell . This extrusion is followed by the fusion of the viral membrane with the phagosome membrane , thus resulting in the formation of a large membrane conduit ( Figures 3–5 and Video S2 ) through which the Mimivirus genome is presumably released into the host cytoplasm . The actual mode of DNA release remains unclear , as in all virus-containing phagosomes inspected in this study ( >100 ) , only mature viruses , viruses at various uncoating stages , or empty viral particles could be discerned ( Figures 2 and 3 ) . In light of the size of the Mimivirus genome , the failure to capture genome release is intriguing . On the basis of cryo-TEM studies of Mimivirus particles [18] that implied the presence of two successive membrane layers underlying the protein shell ( as is the case for at least one additional member of the NCLDV clade , the African Swine Fever Virus [22 , 23] ) , it can be hypothesized that the Mimivirus genome is released into the host cytoplasm enclosed within a vesicle . Such a vesicle might be derived from the inner membrane layer , whereas the outer membrane forms a conduit for this DNA-containing vesicle by fusing with the phagosome . This conjecture provides a rationale to the need for the massive opening of the capsid that is reported here , a possible reason for the failure to capture DNA release ( as a vesicle-mediated release would likely be a fast process ) , as well as a plausible answer to the question how is the viral genome protected against host nucleases during its transport to the host nucleus . Moreover , the notion of a vesicle-mediated exit and transport of the Mimivirus genome provides a potential and highly attractive solution to the question of how is a 1 . 2-Mbp DNA molecule translocated through the extremely crowded cytoplasm of the host , which has been shown to present a supreme barrier for translocation of long DNA molecules [24] . The notion of genome release and transportation within a vesicle that is derived from internal viral membranes is , to the best of our knowledge , unprecedented and is being currently investigated . Notably , while DNA injection and packaging in the internal-membrane–containing tail-less bacteriophage PRD1 appear to occur through a unique vertex [25] , in vitro studies implied that PRD1 delivers its genome through a membrane tube [26] . For this to occur , the PRD1 capsid must open up in a yet uncharacterized process that might be similar to the genome release process occurring in Mimivirus . High-resolution structural studies of PRD1 life cycle will be required to address this intriguing possibility . Mimivirus assembly occurs in cytoplasmic viral factories [19] . DNA is packaged into preformed procapsids located at the periphery of factories ( Figure 6 ) [19] . Studies of intracellular factories ( Figures 6 and 7 ) as well as of viral factories isolated at various post infection time points ( Figure 8 ) indicate that the formation of the stargate structure occurs at a very early stage of the viral assembly . These observations imply that in addition to acting as a DNA release portal , the stargate might be involved in the initiation of Mimivirus particles assembly . Such an initiation role is in keeping with the fact that capsids incorporate only one portal that is located at a unique vertex , and this symmetry-breaking step can only be rationalized in terms of a singular event , as is the initiation stage . Indeed , previous studies indicated that portals are involved in the initiation of capsid assembly in herpesviruses and several bacteriophages such as T4 and SPP1 [27 , 28] Our electron tomography and volume reconstruction analyses , supported by TEM and SEM studies , demonstrate that DNA packaging in Mimivirus proceeds through a transient aperture located at a distal site of the stargate site . These studies further indicate that in contrast to all heretofore-characterized viruses , Mimivirus genome packaging occurs at an icosahedral face rather than at a vertex ( Figures 4–6 and Video S3 ) . Notably , 3-nm-wide pores detected on the 3-fold axes in “open” procapsids of the α3 bacteriophage of the Microviridae family were proposed as possible DNA entry sites [29 , 30] . In the current study , such a face-centered , non-vertex , DNA packaging site is directly demonstrated . The functional significance of this finding becomes apparent in light of recent biochemical and comparative genomic studies , which indicated that inner-membrane–containing viruses such as bacteriophage PRD1 and members of the NCLDV clade ( including Mimivirus ) code for proteins that are closely homologous to ATPases of the FtsK/SpoIIIE/HerA superfamily [12–14] . These ATPases were proposed to act as membrane-anchored motors that pump DNA through a closing membranal septum during bacterial and archaean division [31 , 32] . On the basis of these findings and considerations , it has been suggested that viruses containing inner membranes package their genomes through a pumping mechanism akin to the DNA segregation pathway deployed in bacteria and archaea [12–14] . Our observations complement this conjecture . Since FtsK/SpoIIIE/HerA ATPases mediate a strictly unidirectional mode of DNA translocation [31 , 32] , this system is unlikely to be responsible for both exit and packaging of viral genomes . In keeping with this notion , we identify distinct exit and entry portals in Mimivirus . Moreover , whereas a vertex-centered motor for DNA packaging in bacteriophages and herpesviruses represents a thermodynamically sensible solution , because it minimizes vertex-portal interactions , such a setting would be incompatible with a pumping system that must rely on robust motor–membrane interactions . Such interactions can , however , be maximized when the packaging motor is located within an icosahedral face ( rather than on an icosahedral vertex ) . In addition , our conjecture that the DNA entry portal is sealed once packaging is concluded is consistent with recent studies that indicated that the DNA translocating ATPase SpoIIIE promotes membrane fusion following completion of bacterial DNA segregation [33] . Interestingly , the cone-shaped aperture through which DNA is packaged is characterized by a diameter of 20 nm at the inner shell , thus capable of accommodating more than a single DNA duplex , as indeed is implied by TEM studies ( Figures 6 and 7 ) . Of note in this context is the conjecture that several SpoIIIE rings might fuse to form a larger ATPase ring [31] . The size and genome complexity of the Mimivirus call into question the conventional division between viruses and single-cell organisms . Our findings , which support the conjecture that the DNA packaging mechanism deployed by internal-membrane–containing viruses might share structural and functional patterns with bacterial DNA segregation [12–14] , further substantiate the notion that the conventional division between viruses and single-cell organisms should be re-examined . Moreover , the observations concerning the stargate and its massive opening , the DNA packaging machinery , as well as the possibility raised here that the exit and transportation of the genome occur within a vesicle derived from a viral internal membrane , may indicate that Mimivirus and potentially other large dsDNA viruses have evolved mechanisms that allow them to effectively cope with the exit and entry of particularly large genomes . Because structure , rather than genomic sequence , represents the most reliable determinant for viral lineage [34] , the structural features underlying the Mimivirus replication cycle raise intriguing questions . The presence of distinct portals for genome exit and entry , as well as the shape of the stargate and the unprecedented face-centered location of the packaging portal , may indicate that Mimivirus represents a unique specimen . It is , however , enticing to suggest that these features , along with their functional and evolutionary implications , are shared by diverse viruses containing internal membranes . This conjecture , which is consistent with comparative genomic studies [12 , 13 , 16 , 34] , as well as with the notion that an inner membrane represents a key factor for viral evolution and classification [35 , 36] , is being currently tested by high-resolution studies of the replication cycles of various inner membrane-containing viruses . Finally , for only a small fraction of the open reading frames in Mimivirus , genome function has been attributed [17] . The observations reported here may stimulate further studies on the Mimivirus that will focus on heretofore uncharacterized structural features , including the stargate , its putative role in Mimivirus assembly , and its massive opening , as well as the face-centered DNA packaging apparatus . Such studies are likely to provide deeper insights into the unusually complex genome of this virus and into the factors that directed and dictated its evolution .
Acanthamoeba polyphaga were cultivated and infected by Mimivirus as previously described [20] . Infected cells at various post infection time points were cryo-immobilized by the high-pressure freezing technique [21] , using an HPM high-pressure freezer ( BAL-TEC ) . Samples were then freeze-substituted ( Leica EM AFS ) in dry acetone containing 2% glutaraldehyde and 0 . 1% tannic acid for 60 h at −90 °C , and warmed up to room temperature over 24 h . Following acetone rinses , samples were incubated in 0 . 1% uranyl acetate ( UA ) and 1% OsO4 for 1 h , infiltrated with increasing concentrations of Epon over 6 d , and polymerized at 60 °C . Thin sections ( 50–70 nm ) , obtained with an Ultracut UCT microtome ( Leica ) were post-stained with 1%–2% uranyl acetate and Reynold's lead citrate and examined using FEI Tecnai T12 TEM operating at 120 kV . Images were recorded on a MegaView III CCD ( SIS ) . Preparation of vitrified Mimivirus and cryo-TEM studies were as described in [18] . For electron tomography , semi-thick sections ( 170–200 nm ) decorated on both sides with 12-nm colloidal gold markers were prepared as described above , and post-stained with 2% UA . Double-tilted image series were acquired in FEI Tecnai F-20 TEM operating at 200 kV . Images were recorded on a 4kx4k TemCam CCD camera . Acquisition was performed at 1° intervals over a range of ±68° , using SerialEM program [37] . Alignment and 3D reconstruction were performed with IMOD image-processing package [38] . IMOD and Amira 4 . 1 packages were used for modeling . Viruses purified by filtration were fixed with 2% gluteraldehyde in Cacodylate buffer for 1 h . Viruses were deposited on poly-L-lysine–treated formvar-coated 200-mesh Ni grids , post-fixed with 1% OsO4 , 1% tannic acid , and 1% uranyl acetate . Dehydration in increasing ethanol concentrations was followed by critical point drying using CPD30 ( BAL-TEC ) . Samples were sputter-coated with 2-nm Cr and visualized in the high-resolution SEM FEG Ultra 55 ( Zeiss ) . For cryo-SEM experiments , samples were fixed in 2% gluteraldehyde for 1 h , washed with DDW and deposited on Aclar disk ( EMS ) . Samples were frozen by plunging in liquid ethane , freeze-dried for 1 h at −100 °C in a BAF60 freeze-fracture device ( BAL-TEC ) and rotary shadowed at 45° with 2-nm platinum-carbon and 5-nm carbon at −120 °C . Samples were transferred to Ultra 55 SEM using a VCT100 vacuum-cryo-transfer , and observed at −120 oC . Replication factories were isolated using the spheroplast methodology [39 , 40] . Specifically , Acanthamoeba polyphaga were cultivated and infected by Mimivirus . The infected cells were washed with ice-cold 20 mM potassium phosphate buffer , pH 6 . 5 , at different times post infection , and transferred into a glass tube at a concentration of 105 cells/ml . The cells were diluted with two volumes of ice-cold DDW , and incubated for 10 min on ice . The swollen cells were then incubated in ice-cold 0 . 3 M NaCl in 20 mM potassium phosphate buffer , pH 6 . 5 for 10 min . Aliquots of 10 μl were deposited on top of 40 μl fixative 1 ( 4% paraformaldehyde in 20 mM potassium phosphate , 0 . 2 M sucrose , pH 6 . 5 ) and spun onto poly-L-lysine-treated silicon chips at 4000g in a swing out rotor for 5 min . The samples were further fixed in fixative 2 ( 2% glutaraldehyde , 0 . 2% tannic acid in 20 mM potassium phosphate , 0 . 2 M sucrose , pH 6 . 5 ) for 10 min . The chips were washed in DDW and treated with 1% OsO4 ( DDW ) for 10 min , washed with DDW and stained with 1% UA ( DDW ) for 10 min . Samples were then prepared for SEM analysis by ethanol dehydration followed by critical point drying . The dried samples were coated with 2-nm chromium in a stage-rotation mode . The 5 × 5 mm silicon chips were pre-treated with 0 . 1% poly-L-lysine ( in DDW ) and incubated in a humid chamber over-night at 4 °C . | Two fundamental events in viral life cycles are the delivery of viral genomes into host cells and the packaging of these genomes into viral protein capsids . In bacteriophages and herpesviruses , these processes occur linearly along the genome , base pair after base pair , through a single portal located at a unique site in the viral capsid . We have addressed the question of whether such a linear translocation through a single portal also takes place for viruses harboring very large genomes , by studying genome delivery and packaging in the amoeba-infecting virus Acanthamoeba polyphaga mimivirus . With 1 . 2 million base pairs , this double-stranded DNA genome is the largest documented viral genome . By using electron tomography and cryo-scanning electron microscopy , we identified a large tunnel in the Mimivirus capsid that is formed shortly after infection , following a large-scale opening of the capsid . The tunnel allows the whole viral genome to exit in a rapid , one-step process . DNA encapsidation is mediated by a transient aperture in the capsid that , we suggest , may promote concomitant entry of multiple segments of the viral DNA molecule . These unprecedented modes of viral genome translocation imply that Mimivirus—and potentially other large viruses—evolved mechanisms that allow them to cope effectively with the exit and entry of particularly large genomes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [
"virology"
] | 2008 | Distinct DNA Exit and Packaging Portals in the Virus Acanthamoeba polyphaga mimivirus |
A genome-scale RNAi screen was performed in a mammalian cell-based assay to identify modifiers of mutant huntingtin toxicity . Ontology analysis of suppressor data identified processes previously implicated in Huntington's disease , including proteolysis , glutamate excitotoxicity , and mitochondrial dysfunction . In addition to established mechanisms , the screen identified multiple components of the RRAS signaling pathway as loss-of-function suppressors of mutant huntingtin toxicity in human and mouse cell models . Loss-of-function in orthologous RRAS pathway members also suppressed motor dysfunction in a Drosophila model of Huntington's disease . Abnormal activation of RRAS and a down-stream effector , RAF1 , was observed in cellular models and a mouse model of Huntington's disease . We also observe co-localization of RRAS and mutant huntingtin in cells and in mouse striatum , suggesting that activation of R-Ras may occur through protein interaction . These data indicate that mutant huntingtin exerts a pathogenic effect on this pathway that can be corrected at multiple intervention points including RRAS , FNTA/B , PIN1 , and PLK1 . Consistent with these results , chemical inhibition of farnesyltransferase can also suppress mutant huntingtin toxicity . These data suggest that pharmacological inhibition of RRAS signaling may confer therapeutic benefit in Huntington's disease .
Huntington's disease ( HD ) is a dominantly-inherited , invariably fatal , familial neurodegenerative disease caused by an expansion in the polyglutamine encoding CAG tract in the huntingtin gene ( Htt ) [1] . HD manifests with severe motor and psychiatric impairments caused by neuronal dysfunction and loss in the cortex and striatum [2] . Mutant Htt causes cellular dysfunction through mechanisms involving a toxic gain-of-function of the mutant protein . However , loss of neural-protective functions provided by the wild-type protein may also contribute to the disease phenotype [3] . Pathways and processes disrupted by mutant Htt include transcription [4] , mitochondrial bioenergetics and metabolism [5] , and proteasomal degradation [6] . Additionally , signaling cascades that have yet to be implicated may impinge on multiple defective processes in HD . There is currently no therapeutic treatment for HD , and a significant challenge is the identification of cellular drug targets for this disease . In order to comprehensively discover novel drug targets for HD , we completed a large-scale RNAi screen in a human cell-based model of mutant huntingtin toxicity . Similar approaches have been used to map modifier pathways in cancer , and infectious disease models [7] , [8] . Modifiers identified in this screen were systematically validated in higher content models including a mouse Hdh knock-in cell model [9] of cell death , and a Drosophila model of HD motor dysfunction [10] . The primary screen identified a number of pathways and biological processes known to be involved in HD , indicating that the cell-model and modifier results are generally relevant to molecular aspects of the disease . Subsequent validation of novel targets demonstrate that augmented signaling though RRAS and downstream effectors , may be a druggable pathological feature of HD .
To discover proteins and pathways that modify mutant Htt toxicity , we carried out a siRNA screen in cells expressing the N-terminal 558 amino acids of mutant Htt fused to GFP ( Htt1-558141Q-GFP ) . HEK293T cells expressing this mutant Htt fragment show rounding and detachment indicative of toxicity ( data not shown ) , and enhanced caspase activation upon growth factor deprivation relative to control cells ( Figure S1 ) . To perform the screen , we co-transfected the Htt1-558141Q-GFP construct with 7 , 494 unique siRNA pools , each targeting the product of a gene identified as pharmacologically tractable by empirical and/or homology-based analyses ( the Dharmacon Druggable Genome Set ) , as well as overlapping sets of kinase , G-protein coupled receptor ( GPCR ) , and protease gene families . The effect of each siRNA pool on caspase activation in response to serum-withdrawal was measured , and pools showing significant suppression of caspase activation . This was measured by caspase 3/7 activity , and control wells transfected with siRNA against CASP3 served as a positive control ( Figure S1 ) . The results for the entire screen are presented in Table S1 . The top 130 siRNA hits from the screen that caused a reduction of more than 1 standard deviation below the mean for the entire screen are shown in Table 1 . These are ranked according to the average magnitude of suppression of caspase activity . In the primary screen performed in HEK293T cells expressing a mutant huntingtin fragment , we found that 130 siRNAs reduced caspase-3 activity to one standard deviation below the mean activity of the entire library of 7 , 824 ( 7 , 494 unique ) siRNA pools ( Table 1 ) . Only eight siRNAs reduced caspase activity to two standard deviations below the mean . Gene ontology ( GO ) analysis [11] followed by a filtering step using a quality control statistic ( See Materials and Methods ) was used to explore the top 130 suppressors for enrichment of GO categories . Nineteen GO categories are significantly enriched among these modifiers ( Figure 1A ) . Among these are four categories representing neurological system processes ( Figure 1E ) , indicating that the screen for mutant Htt toxicity in the HEK293T cell model was able to identify modifiers in neuron-related pathways . Enrichment of the category “hydrolase activity” was the most significant for the suppressors ( Figure 1A ) , and two other enriched categories , “proteolysis” and “peptidase activity” ( Figure 1D ) are consistent with the important role proteolytic processing plays in mutant huntingtin toxicity [12] , [13] . One of the hits , siRNA targeting CASP3 , directly reduces the caspase 3 toxicity readout independent of Htt . However , this hit is included in these analyses because of the direct role of CASP3 in huntingtin proteolysis [14] . We also observed glutamate signaling to be an enriched GO category among the suppressors , both as a biological process ( Figure 1B ) , and by enrichment in members of the “ionotropic glutamate receptor complex” ( Figure 1C ) . Evidence for the involvement of N-methyl-D-aspartate ( NMDA ) -type glutamate receptor excitotoxicity in HD includes heightened vulnerability of NMDA receptor-expressing neurons in HD patients [15] , elevated levels of the NMDAR agonist quinolinate in the cortex and striatum of patients [16] and full-length mouse models of the disease [17] and enhanced sensitivity to NMDA in mouse models of HD [18] . Pathway Analysis ( IPA Core Analysis ) was performed on the hits and enrichment of metabolic and signaling pathways , cellular and disease processes , and molecular networks were calculated ( Table 2 ) . According to the IPA analysis , the most significantly enriched functional category was “Neurological Disease” , followed by “Genetic Disorder” , both of which directly apply to HD . Additionally , siRNAs that suppressed mutant Htt toxicity were found to be enriched for genes included in the categories of “Protein Degradation” , “Nervous System Development and Function” , “Cell Death” , and “Psychological Disorders” . These all are consistent with processes known to be involved in HD . Likewise , several enriched canonical pathways , including “Amyotrophic Lateral Sclerosis Signaling” , “Synaptic Long Term Potentiation” , “Mitochondrial Dysfunction” , “Amyloid Processing” , and “Death Receptor Signaling” correlate to processes known to be involved in HD and other neurodegenerative diseases . Suppressors identified in the HEK293T screen were analyzed according to several functional network criteria within IPA . First , a network was generated starting with the 130 suppressors such that a gene ( node ) was included in the network only if it directly connected to other genes with no intervening nodes . APP , CASP3 , and NR3C1 seem to play a central role in this subnetwork of the hits , as they are the most highly connected nodes with 13 , 10 , and 15 connections , respectively ( Figure 2 ) . Htt was then manually added to this network , and its connections to the existing nodes introduced . All the nodes represented in Figure 2 ( except for HTT ) are from the 130 top hits from the siRNA screen and were included if they formed at least one connection to the subnetwork . Direct interaction of Htt to GRIN1 , GRIN2A , GRIN2B , and CASP3 is intriguing because over-expression of wild-type Htt protects striatal neurons from NMDA-receptor mediated induction of caspase-3 , which in turn cleaves Htt [19] . We confirmed the expression of all three of the NMDA-receptor subunits in HEK293T cells using qPCR ( Figure S2 ) . A more inclusive network was generated using a shortest path algorithm within IPA that connects as many nodes as possible using the fewest number of intervening nodes . Restricting the analysis to data derived from “human and human tissue” results in the network shown in Figure S3 . Interestingly , Htt was independently included in this network by the IPA software protocol , underscoring significant associations between the suppressors and known pathways and processes related to Htt . Similarly , a number of the shortest path connections for the toxicity suppressor hits are mediated by established components of Htt toxicity processes . NFκB is recruited into the network as an intervening node and is known to be activated by mutant Htt through increased IkappaB kinase complex ( IKK ) activity [20] . IKK phosphorylates mutant Htt , enhancing its clearance [21] . Furthermore , the core kinases of IKK , IKKa and IKKbeta , have opposing roles in regulating DNA damage-induced proteolytic cleavage of Htt , with inhibition of IKKbeta blocking Htt proteolysis while increased levels of IKKa provide this benefit [22] . Detrimental activation of IKK by mutant Htt may be responsible for the increase in immune activation as indicated by elevated inflammatory cytokines such as IL6 in pre-symptomatic patients [23] . IKKbeta ( IKBKB ) , as well as IL6 , TGFB1 , interferon alpha , IL2 , IL15 , IL18 , and additional cytokines and signaling molecules were also drawn into the network as shortest path connectors . This suggests that the set of siRNA toxicity suppressors are enriched for factors involved in this inflammatory response . Another known dysfunction in HD is transcriptional dysregulation [24] . The shortest path human network also implicated established transcription factors whose activities are affected by their physical interactions with mutant Htt . It has previously been shown that p53 ( TP53 ) interacts with Htt in vitro and in vivo [25] . Furthermore , p53 is elevated in HD brain and in mouse models of the disease , mutant Htt upregulates p53 transcriptional activity , and inhibition of p53 prevents cytotoxicity in HD cells [26] . Similarly , Sp1 and huntingtin are known to interact [27] , and mutant Htt directly represses Sp1-dependent transcription in an in vitro transcription system [28] . The clear identification of known modifiers of HD molecular pathology in unbiased ontology and IPA network analyses provides significant validation of the primary screening results as having relevance to established mechanisms in HD . We therefore focused on the identification and validation of novel targets and pathways not previously implicated in HD pathology . All siRNA pools conferring caspase-3 activation ≤75% of control siRNA were selected for retesting in the HEK293T assay . Those siRNAs meeting this criterion in the retest were subsequently validated in a mouse striatal-derived , full-length mutant Htt knock-in cell-based toxicity model [9] . Although not among the most stringent hits in the HEK293T cells ( >1 standard deviation below the mean for the entire screen ) , knock-down of RRAS ( related RAS viral ( r-ras ) oncogene homolog ) [29] suppressed mutant Htt toxicity robustly , and in the more physiological mouse striatal-derived cell model , reduced toxicity to nearly the same extent as CASP3 siRNA . The retest also identified multiple components of the RRAS signaling pathway as being modifiers of mutant Htt toxicity ( Figure 3A ) . Thus , further analyses focused on this target and the associated signaling cascade it mediates were carried out . RRAS is a 23 kDa protein with roles in cell migration and adherence [30] , apoptosis [31] , neurite outgrowth [32] and hippocampal axon specification [33] . Interestingly , RRAS knockdown also suppressed the aggregation of mutant Htt in this cell-based assay ( Data not shown ) . Mature , active proteins of the Ras superfamily are prenylated at their carboxyl-termini by farnesyltransferase or geranylgeranyl transferase enzymes . Consistent with this , we observed that siRNA inhibition of FNTB , the β subunit of the mammalian farnesyltransferase , suppressed toxicity in this screen ( Figure 3A ) . Downstream of Ras proteins is RAF1 , a MAPKKK that is phosphorylated at Serine 338 ( S338 ) in response to activated Ras [34] . The MAPKs downstream of RAF1 , ERK1/2 , provide feedback inhibition by hyperphosphorylating RAF1 , inactivating it [35] . To recycle RAF1 for subsequent activation , the peptidyl-prolyl cis/trans isomerase PIN1 is required for removal of inactivating phosphates by the PP2A phosphatase [35] . Polo-like kinase 1 ( PLK1 ) stabilizes PIN1 [36] and siRNA inhibition of both PIN1 and PLK1 reduced caspase-3 induction by ∼33% relative to control ( Figure 3A ) . PKR ( double-stranded RNA-dependent protein kinase ) and MSLN ( mesothelin ) , two additional modifiers that upon inhibition significantly suppress mutant Htt toxicity , have functions related to RRAS/RAF/MEK/ERK signaling ( Figure 3B ) . PKR phosphorylates the B56α regulatory subunit of PP2A , increasing the phosphatase activity of the holoenzyme [37] . PKR also has a direct role in HD by binding to mutant Htt transcripts and is activated in HD brain [38] . MSLN , when overexpressed in breast cancer cells , causes sustained activation of ERK1/2 [39] . All of the RRAS signaling components identified as loss-of-function suppressors in the screen have positive roles in the signaling cascade . These data are consistent with a model indicating that pathogenically augmented signaling through RRAS contributes to mutant huntingtin-mediated toxicity ( Figure 3B ) . To further validate a role for this pathway in mutant Htt toxicity in a higher content cell-based assay , we used an immortalized mouse striatal-derived cell line containing a knock-in of 111 CAGs in the mouse Hdh locus ( STHdhQ111/Q111 ) [9] . As in the HEK293T model , the STHdhQ111/Q111 cells also exhibit enhanced caspase activation upon serum deprivation as compared to the wild-type STHdhQ7/Q7 cells [40] . Knock-down of the six RRAS pathway components confirmed as suppressors in the HEK293T assay also suppressed mutant Htt toxicity in this knock-in model of HD ( Figure 4A and Figure S4A ) . In addition , siRNAs targeting the common α subunit ( FNTA ) of the farnesyltransferase and geranylgeranyl transferase enzymes suppressed toxicity in STHdhQ111/Q111 cells ( Figure 4B ) . While specific targeting of RRAS is sufficient to abrogate mutant Htt-dependent toxicity , inhibition at the level of farnesylation provides similar effects , indicating that a prenylation-dependent process contributes to HD toxicity . To determine whether the suppression effect in the STHdhQ111/Q111 cells was specific to reducing RRAS levels as opposed to other Ras family members , we tested the effects of knocking down three canonical Ras proteins , NRAS , KRAS and HRAS . RRAS was the only family member tested whose knock-down suppressed toxicity in STHdhQ111/Q111 cells , and suppression was specific to cells expressing mutant Htt ( Figure 4C and Figure S4B ) . Notably , NRAS and KRAS knock-down significantly increased toxicity in the STHdhQ111/Q111 cells . Knock-down of the closely related RRAS homolog RRAS2 did not result in suppression ( data not shown ) . Modulation by RRAS in HD knock-in cells is likely independent of its antagonistic interaction with the anti-apoptotic BCL2 [31] as over-expression of BCL2 did not show an effect alone or in combination with RRAS knock-down ( data not shown ) . To validate these results in vivo , we tested RRAS signaling components in a Drosophila model of HD . RNAi or loss-of-function alleles for several RRAS signaling pathway components rescued the motor performance defect induced by expression of an N-terminal mutant Htt construct containing 128 glutamines in Drosophila melanogaster [10] ( Figure 3C ) . Consistent with modifier effects in the two mammalian cell culture models , reduced levels of the RRAS homolog Ras64B , the PIN1 homolog dodo , and the PLK1 homolog polo resulted in significant rescue in Drosophila ( Figure 4D–4F ) . Furthermore , decreasing the levels of either of two p21-activated kinase ( PAK ) proteins ( Pak and mbt ) , which phosphorylate RAF1 in response to activated Ras [41] , lead to suppression ( Figure S5A–S5D ) . Finally , decreased amounts of Drosophila RAF ( polehole ) , and the downstream effector MEK1 ( Dsor1 ) , suppressed the mutant Htt-dependent defect ( Figure S5E–S5G ) . These results demonstrate that orthologous components of the RAS pathway modify mutant Htt-mediated motor performance defects in a whole organism model of HD . These observations further validate results obtained in the cell-based assays . We investigated the mechanism by which mutant Htt interferes with normal RRAS signaling . GTP-bound RRAS binds to RAF1 [42] , recruiting it to the plasma membrane and likely stimulating its activation through phosphorylation at S338 mediated by PAK proteins [41] . Activated RAF1 phosphorylates MEK1/2 , which in turn activate ERK1/2 that target multiple classes of substrate [43] . Localization of RAF1 to the plasma membrane and subsequent activation of ERK phosphorylation has previously been reported to fail to protect against apoptosis [44] . These toxicity suppression effects consistently resulted from reductions in proteins that are positive effectors of RRAS/RAF/MEK/ERK signaling , suggesting the pathway is pathogenically activated in HD models . This is in agreement with a previous report [45] of enhanced ERK activity in two mutant Htt cell lines , although in that study over-expression of a constitutively active mutant of MEK was protective against caspase induction . We examined the phosphorylation status of RAF1 at S338 in these cellular HD models to determine if signaling is dysregulated , as has been observed previously in Alzheimers disease ( AD ) patient brains [46] and a mouse model of AD [47] . The ratio of phosphorylated S338 RAF1 to total is higher in STHdhQ111/Q111 cells than wild-type , and knock-down of RRAS restores the ratio ( Figure 5A ) . In this model , the observed p-S338/total ratio in STHdhQ111/Q111 cells is the result of reduced levels of unphosphorylated RAF1 , as opposed to elevated p-S338 levels . Unphosphorylated RAF1 has MEK kinase-independent anti-apoptotic functions [48] ( see Figure 3B ) , and reductions in this species could result in the release of apoptotic mediators ASK1 [49] , MST2 [50] and Rok-α [51] that RAF1 normally inhibits . Hence , while activated RAF1 ( as assessed by p-S338 levels ) is present at comparable levels in wild-type and mutant huntingtin cells , the proportion of unphosphorylated RAF1 is decreased . RRAS inhibition restores the normal ratios of RAF1 species by increasing the level of unphosphorylated RAF1 . An effect on the p-S338/total RAF1 ratio similar to that in the knock-in HD cell model is also observed in transiently transfected HEK293T cells . Htt1-558141Q-GFP cells have an almost 4-fold higher ratio of p-S338 RAF1 to the total RAF1 than Htt1-55823Q-GFP cells ( Figure 5B ) . RRAS siRNA abolished the difference in the ratio of p-S338/total RAF1 between cells transfected with mutant and wild-type constructs by substantially reducing the levels of p-S338 RAF1 and partially restoring the total RAF1 levels in cells with mutant Htt . RRAS knock-down in cells transfected with Htt1-55823Q-GFP does not alter the normal ratio , suggesting a specific effect of reducing RRAS activity in mutant Htt cells . The elevation in p-S338 RAF1 observed in this mutant Htt model , but absent in the knock-in model , may reflect greater phenotypic severity due to over-expression of a fragment of mutant Htt when compared to endogenously expressed full-length protein . Finally , the p-S338/total RAF1 ratio was examined in the R6/2 mouse model , which expresses exon 1 of Htt with an expanded polyglutamine tract [52] . We observed elevated ratios of p-S338/total RAF1 in the striatum and cortex from R6/2 mice relative to controls , and , similar to the HEK293T fragment model , this was due to an elevation in p-S338 RAF1 ( Figure 5C ) . The increased ratio of phosphorylated to total RAF1 species observed in the presence of mutant Htt across diverse models supports the conclusion that the RRAS signaling pathway is pathogenically modified by mutant Htt . To examine potential mechanisms for how RRAS levels might influence mutant huntingtin toxicity we looked for co-localization of RRAS with mutant huntingtin in the mouse STHdhQ111/Q111 cells . Figure 6A shows the co-localization of huntingtin and RRAS at leading edges of STHdhQ111/Q111 cells ( upper panels ) . RRAS is co-localized with the lamellipodial marker cortactin in these cells ( lower panels ) . This localization is consistent with reported sites of RRAS localization and the known role of RRAS in cell motility and adhesion [30] . We also observed long cellular processes extending from the cell bodies in the STHdhQ111/Q111 cells ( data not shown ) . Interestingly , huntingtin protein can be seen co-localized in these regions of the cell . This suggests that the effect of RRAS levels on Htt toxicity may be due to interactions between these proteins at lamellipodia . The immunohistochemistry of RRAS was examined in the HdhQ175 knock-in [53] , R6/2 and BACHD mouse models . We found increased colocalizaton of RRAS in the striatum and cortex in all three HD models when compared to littermate controls ( Figure 6B , 6C; Figure S6 ) . We compared the distribution of RRAS in the knockin model HdhQ175 ( homozygote ) relative to controls since the expression level of wild-type Htt and mutant Htt are similar . We observed that the RRAS colocalized with Htt ( Figure 6B , 6C ) and there is a statistically significant increase in colocalization of RRAS with Htt in the HdhQ175 mouse model . We also examined the localization of RRAS in a full-length Htt mouse model with 100 CAG repeats under the control of the human Htt promoter ( BACHD ) using both immunohistochemistry and cellular fractionation ( Figure S6 ) . Again we found an increase in the colocalization of mutant Htt with RRAS and an increase in the membrane fraction . We conclude that the RRAS pathway is pathogenically activated by mutant Htt . Decreased HD toxicity resulted from reductions in proteins that are positive effectors of RRAS/RAF/MEK/ERK signaling , suggesting the pathway is pathogenically activated in HD models . Direct reduction in the levels of RRAS protein using RNAi provides robust toxicity suppression in two cellular and one whole organismal model of HD . The levels of RRAS were observed to be equivalent between cells containing mutant Htt and control cells ( data not shown ) . However , the levels of active , GTP-bound RRAS might be elevated in the presence of mutant Htt without any observable changes in the amount of total protein . GTP-bound Ras proteins bind to the Ras binding domain ( RBD ) of RAF1 with 100-fold or greater affinity than their GDP-bound forms [54] . This affinity increase can be used to measure the abundance of active , GTP-bound Ras proteins using pull-downs with the isolated RBD from RAF1 , and then probing with antibodies against the Ras protein of interest [55] . Overexpression of RRAS in STHdhQ7/Q7 and STHdhQ111/Q111 cells was used to allow detection of GST-RBD bound RRAS . As shown in Figure 7A , STHdhQ111/Q111 cells contain more active RRAS than STHdhQ7/Q7 cells , although the difference is not statistically significant . This result is suggestive of an increase in the GTP-bound form of RRAS in the presence of mutant Htt . The RBD pull-down assay was also used to measure active RRAS in the R6/2 mouse model of HD which expresses exon 1 of Htt with an expanded polyglutamine tract [52] . Homogenates of cortex and striatum from 12-week old R6/2 and control mice were subjected to GST-RBD pull-downs , followed by western blot for RRAS ( Figure 7B ) . No significant difference in the levels of active RRAS was seen between control and R6/2 mice in the cortex samples . However , a significant ( p<0 . 05 ) increase in GTP-bound RRAS is observed in R6/2 striatum relative to control , in agreement with the model of pathogenic activation of RRAS in HD . Taken together , these data indicate that mutant Htt expression can pathogenically augment Ras signaling through RRAS . They suggest further that pharmacological interventions that dampen activation of Ras signaling could be of therapeutic benefit in HD . To test this idea , we examined the activity of small molecule inhibitors that target components of the Ras signaling pathway implicated by these genetic studies ( see Figure 3B ) . Use of the in vitro inhibitor of RAF1 kinase activity , GW5074 in the STHdhQ111/Q111 cell model reduced mutant Htt-induced toxicity ( data not shown ) . This is in agreement with a previous report of the neuroprotective effects of this compound against a variety of toxic insults [47] . GW5074 and similar in vitro RAF1 kinase inhibitors function as activators of RAF1 and the related BRAF within cells [56] , confounding interpretation of their toxicity rescue mechanism . Treatment with a farnesyltransferase inhibitor ( FPT inhibitor II , Calbiochem ) conferred robust , dose-dependent reductions in toxicity in mutant Htt cells ( Figure 8 ) . Wild-type cells were not responsive to FPT inhibitor II , supporting the hypothesis of a specific defect that is sensitive to perturbations of farnesylation in mutant Htt cells . RRAS signaling modulates toxicity in mutant Htt cells , and farnesylation of RRAS may be required for this activity . Alternative farnesyltransferase substrates such as Rhes , which has recently been shown to modulate Htt sumoylation and stability [57] , may contribute to the rescue due to FPT inhibitor treatment . Farnesyltransferase inhibitors have predominantly been explored as therapeutic agents in cancer treatment , but studies have demonstrated their efficacy in a cell model of α-synuclein toxicity [58] , as well as their amelioration of disease in a mouse model of progeria [59] . The beneficial effects of farnesyltransferase inhibition in these mutant Htt models suggest that pathogenic Ras signaling may be a common feature in these late onset diseases .
In this study we show that an unbiased siRNA screen performed in a human cell-based assay identified both known and novel targets and functional pathways as loss-of-function suppressors of mutant Htt-mediated toxicity . GO-based ontology analyses , identified glutamatergic NMDA receptors pathways as a prominent class of suppressors . Results from Ingenuity Pathway Analysis also implicated a number of other functional clusters among the hits including inflammatory processes and transcription . The dataset presented here provides a resource for interrogating the biological processes surrounding mutant Htt toxicity by indicating specific proteins and pathway components that act through these established mechanisms . Furthermore , the dataset provides significant insight into novel mechanisms and targets not previously implicated in HD pathology . In addition to the suppressors described here , the primary screen also identified a significant number of siRNAs that enhanced caspase activity upon knock-down in the HEK293T cell-based assay . One caveat concerning RNAi enhancers of caspase activation is that modifier phenotypes may arise through general loss-of-function toxicity ( e . g . knock-down of essential genes ) and/or off-target effects . However , the enhancers of toxicity identified in our screen represent an interesting class of modifiers for further study . One group of modifiers identified in the screen implicates RRAS activation as a pathogenic consequence of mutant huntingtin . This is supported by our observation that the expression of mutant huntingtin is correlated to increased levels of GTP-bound RRAS in mouse cells and in the striatum of the R6/2 HD mouse model . This is also consistent with the fact that all the suppressor effects observed in this pathway result from loss-of-function ( i . e . RNAi knock-down ) in positive regulators of Ras signaling . Activated RAF1 phosphorylates MEK1/2 , which in turn activate ERK1/2 [43] . The alterations in RAF1 signaling observed in response to mutant Htt expression are consistent with a previous report [45] of enhanced ERK activity in two mutant Htt cell lines , although in that study over-expression of a constitutively active mutant of MEK was protective against caspase induction . Elevated RAF1 S338 phosphorylation has been reported previously in Alzheimer's disease ( AD ) patient brains [46] and a mouse model of AD [47] . GW5074 , an in vitro RAF1 kinase inhibitor , has been shown to have neuroprotective effects in an AD model [47] and against a variety of toxic insults [60] . The beneficial effect of RNAi-mediated inhibition at multiple distinct points of the pathway demonstrates that loss-of-function is the mechanism responsible for rescue in HD models . In addition to activating RAF1 and other downstream effectors such as MEK and ERK , RRAS is known to affect cell motility , and its activity is inhibited by semaphorin-plexin signaling [61] . It has recently been shown that RRAS influences integrin-dependent motility through regulation of integrin internalization in Rab11 containing vesicles [62] . Intriguingly , defects in Rab11 dependent vesicle trafficking have been implicated as a pathogenic effect of mutant huntingtin expression [63] . It will be of interest to explore how effects on semaphorin signaling and/or vesicle trafficking may play a role in the modifier effect of RRAS on mutant huntingtin toxicity . However , our modifier data from cellular , Drosophila and mouse models of HD indicate that aberrant signaling through RAF1 is clearly a mechanism involved in toxicity suppression by loss-of-function in RRAS . Of note the signaling defect may be distinct for RRAS in the straitum while RAF1 appears to affect the cortex and striatum . This may be a limitation in detecting RRAS activation . We show that mutant huntingtin is co-localized with RRAS in a perinuclear region as well as at the cell periphery at lamellipodia . The co-localization suggests that mutant huntingtin may exert some direct effect on RRAS through protein interaction or presence in a shared protein complex . The fact that this co-localization occurs in multiple cell compartments further suggests that mutant huntingtin could influence the function of RRAS in the context of cell migration and/or vesicle traffic as well as through signaling via RAF1 . One model for the pathogenic effect of RRAS activation is the depletion of non-phosphorylated RAF1 . RAF1 has MEK kinase-independent anti-apoptotic functions [48] ( see Figure 3B ) , and reductions in this species could result in the release of apoptotic mediators ASK1 [49] , MST2 [50] and Rok-α [51] from RAF1 inhibition . Toxicity suppression resulting from inhibition of the pathway at points upstream of RAF1 phosphorylation is in agreement with this model . Additionally , RRAS and FNTB inhibition by siRNA , and pharmacologic farnesyltransferase inhibition , all reduce the amount of SDS-insoluble mutant huntingtin ( data not shown ) . This suggests that RRAS signaling may modulate Htt solubility or turnover , and this may play a role in reduced HD toxicity . Farnesyltransferase inhibitors have demonstrated efficacy in a cell model of α-synuclein toxicity [58] , as well as in amelioration of disease in a mouse model of progeria [59] . The beneficial effects of farnesyltransferase inhibition in mutant Htt models suggest that these inhibitors may have efficacy across these late onset diseases . In this study we show that an unbiased siRNA screen performed in a human cell-based assay identified RRAS and multiple downstream signaling components as a coherent group of loss-of-function suppressors . Notably , we do not observe suppression through knock-down of other canonical Ras family members , HRAS , KRAS and NRAS , indicating a specific role for RRAS in HD . We demonstrate further that modifier effects observed in two cell-based models of HD also modulate a Drosophila model of HD induced motor dysfunction . In agreement with these genetic modifier effects , RRAS signaling defects associated with mutant Htt expression in cell models and the R6/2 , HdhQ175 knockin and BACHD mouse model of HD are observed . Finally , we demonstrate that chemical inhibition of the Ras modifying enzyme farnesyltransferase can rescue mutant Htt toxicity in cell models of HD . The results presented here provide evidence that augmented signaling through RRAS may be a pathogenic feature of HD and that pharmacological manipulation of the Ras signaling pathway should be considered as a therapeutic strategy for the treatment of Huntington's disease .
Constructs expressing the first 558 amino acids of Htt with 141Q or 23Q and a C-terminal GFP tag ( Htt1-55823Q-GFP or Htt1-558141Q-GFP ) were generated by PCR amplification from pTet-splice-full-length Htt [64] using primers F: 5′-AAAGGTACCATGGAGCAGAAACTCATCTCTGAAGAG-3′ ( which is upstream of the Htt coding sequence in an N-terminal myc epitope tag ) and R: 5′-AAAGGATCCGACGAGGCCTGGGTCCCATCATT-3′ , digested with Kpn1 and BamH1 and then sub-cloned into the pEGFP-N1 vector ( Vector Biolabs ) . Due to significant contraction of the CAG repeats in the Htt1-558141Q-GFP , the vector was subsequently digested with EcoR1 and EcoRV to remove the N-terminal 530 amino acids containing the contracted CAG region and replaced with the N-terminal region from the original pTet-splice-full-length Htt148Q vector . The CAG tract length was confirmed by sequencing . HEK293T cells were used for the primary screen , and STHdhQ111/Q111 and STHdhQ7/Q7 ( WT ) cell lines [9] were used as a full-length Htt cell-based model . For the HEK293T screen , cells were plated into 96-well format using a Multidrop 384 ( Thermo Electron ) , and all transfection and assay manipulations were carried out on a Bio Mek FX ( Beckman Coulter ) automated workstation . The siRNAs used were SMARTPOOLs of the Human Druggable Genome siRNA set ( Dharmacon ) , consisting of 76 plates of Druggable Genome siRNAs , 10 plates of Protein Kinase siRNAs , 7 plates of G-protein Coupled Receptor siRNAs , and 7 plates of Protease siRNAs . siRNAs were reconstituted in 1X siRNA buffer ( Dharmacon ) at a concentration of 20 µM . Mouse siRNAs were reconstituted at a concentration of 1 µg/µl . For the initial screen toxicity suppression assays , transfections were performed by plating 20 , 000 HEK293T cells in 100 µl media into each well of a collagen I coated 96-well plate ( BD Biosciences ) and , 24 h after plating , adding a mixture of 25 µl serum-free media , 320 ng DNA , 17 pmoles siRNA , and 1 µg Lipofectamine 2000 ( Invitrogen ) . Screening of the Druggable Genome set of 76 plates was performed with 10 siRNA plates on a given day , transfecting each plate of siRNAs plates of cells in triplicate . The Kinase , GPCR and Protease sets were done similarly , except the Protease set was screened in quadruplicate . In the initial STHdhQ111/Q111 assay ( Figure 4A ) , nucleofection was performed with a Nucleofector 96-well Shuttle System ( Amaxa Biosystems ) using program FF-130 and Solution SG with 200 , 000 cells/well and 40 picomoles of siRNA . All other mouse cell nucleofections were carried out with 3 µg siRNA and two million STHdhQ111/Q111 or STHdhQ7/Q7 cells using a Nucleofector II device with Solution L and program T-030 , plating 50 , 000 cells per well in a 96 well plate . For both cell types , the media was removed and replaced with 100 ul of serum-free media 48 h after transfection . At 72 h after transfection , the cells were assayed for caspase 3/7 activity . For the HEK293T siRNA screen , media was removed by inverting 96-well plates and replacing with 100 µl ( 50 µl in the case of the Protease set ) of a 50/50 mixture of serum-free DMEM and APO 3 HTS 1X Lysis Buffer ( Cell Technology , Inc ) . Plates were shaken for 30 s at 700 rpm . Plates were incubated at room temperature for 20 minutes to ensure complete lysis , and then centrifuged at 2 , 500× g for 3 min . Three 10 µl aliquots were taken to assay for protein ( BCA assay; Pierce ) . To the remaining 70 µl ( 20 µl for the Protease set ) of lysate , 30 µl of a DMEM/1x lysis buffer mixture containing DTT [15 mM]final and substrate ( zDEVD2Rhodamine 520 ) were added . Plates were again shaken for 30 s , and then assayed for fluorescence ( EX485 nm/Em530 nm ) in a Fusion alpha HTS plate reader ( PerkinElmer ) with reads every 44 min . Assays were the same for STHdh cells except that cells were lysed in 50 µl and reads were taken every 51 s . All assays were performed in triplicate ( quadruplicate for Protease siRNA screen ) for each transfected siRNA , and caspase 3/7 activity was calculated as the change in RFU per minute per milligram of total protein in the well . Each plate in the primary HEK293T screen contained three of each of the following control wells: co-transfection of Htt1-55823Q-GFP with non-targeting ( NT ) siRNA or CASP3 siRNA ( toxicity controls ) , Htt1-558141Q-GFP with NT siRNA ( negative control ) , or Htt1-558141Q-GFP with CASP3 siRNA ( positive control ) . Caspase 3 activity units were first normalized for position effects by dividing the values for a given well by the average for that well throughout the plates screened on that day ( 28–30 plates ) . These were then represented as fractions of the “negative control” caspase activity values derived from control wells present on the individual plate . This normalization was chosen instead of plate mean because the arrangement of plates in the siRNA sets is by gene families , and thus not organized with an unbiased distribution . The mean and standard error for each siRNA were then calculated for the normalized individual replicates . siRNAs were selected as “hits” if the confidence interval , defined as the mean value for each siRNA ± its standard error , was less than one standard deviation ( 0 . 317 ) from the mean ( 1 ) of all siRNAs in the entire screen . siRNAs were thus considered hits if the sum of their normalized caspase 3 activity mean and standard error was less than 0 . 683 ( 1−0 . 317 ) . The genes encoding the 130 hits were analyzed for enrichment in gene ontology ( GO ) categories in order to gain insight into the biological context of mutant Htt toxicity suppression . The enrichment analysis was performed using Ontologizer [11] , a java-based program that performs enrichment analysis in a manner that takes into account parent-child relationships in the GO tree , rather than the standard term-for-term analysis that does not incorporate this feature of GO . For this analysis , the genome was used as the background ( population ) dataset , and the hits as the test ( study ) set . Due to a lack of power , the screened siRNA library could not be used as the background dataset . The Parent-Child Union algorithm in Ontologizer was used for analysis and the data was represented as Benjamini-Hochberg ( B-H ) corrected p-values . A threshold of p≤0 . 05 was applied to the GO enrichment results and Ontologizer was used to generate directed acyclic graphs ( DAGs ) of the enriched GO terms . To ensure that the enriched categories were truly enriched over the siRNA library , the same analysis was repeated using the siRNA library list as the study set . The enriched categories for the toxicity suppression hits were then compared against the same categories in the siRNA library . From this information , proportions for each analysis were generated for each GO category: number of genes in the GO category/number of genes in the study set . These proportions were then subjected to a two-tailed proportions test as follows:whereand where and are the sample proportions , is the combined proportion , x is the number of hits within a population , and n is the total number of samples in the population . The resulting z-scores were compared against the z-distribution and p-values calculated . Categories that did not pass a significance threshold of p≤0 . 05 were excluded from the results . Additionally , only GO categories below level 2 were included , and any categories referring to non-eukaryotic processes were excluded . In order to further classify the biological context of the mutant Htt toxicity suppressor hits , the dataset was analyzed using Ingenuity Pathway Analysis ( IPA; Ingenuity® Systems , www . ingenuity . com ) . The dataset was subjected to an IPA Core Analysis using all model organisms and the Ingenuity Knowledge base as the background . Functional categories and canonical pathways were then represented as B-H adjusted p-values and were considered to be enriched if p≤0 . 05 . A network analysis of the mutant Htt toxicity suppressor hits was also performed within IPA . For these analyses , three approaches were utilized . First , a network was created from the hits that could be directly connected to each other using orthologous data from all model organisms within IPA . Subsequently , the Huntingtin ( HTT ) protein was manually added to this pathway in order to observe its potential connections . The second network was created by linking together as many hits as possible using the “shortest path” mechanism from within IPA , in which intervening nodes are placed in the network to connect the hits to each other . Generation of this second network was limited to human data . The third network was generated in the same fashion as the second , but incorporating all model organisms as the background . Striatum was dissected from Q175 homozygote and BACHD mice along with WT littermates at 7-months and 9-months respectively . The mice striatum were homogenized in fractionation buffer ( 50 mM HEPES pH 7 . 0 , 200 mM NaCl , 5 mM MgCl2 , 1 mM DTT , 1 mM EDTA , 0 . 25M Sucrose containing protease inhibitors ) by using a glass dounce for 60 strokes twice with 1-minute rest in between . Samples were then centrifuged at 2 , 000×g for 10 min at 4°C . The nuclear pellet ( P1 ) was saved and the post nuclear supernatant ( S1 ) was further centrifuged at 100 , 000×g for 1 hr at 4°C in a Beckman-Coulter TLA-100 rotor . The supernatant ( S2 , cytosol ) was saved and the total membrane pellet ( P2 ) was re-suspended in fractionation buffer supplemented with 1% Triton X-100 and went through a freeze thaw cycle at −80°C . | Huntington's disease ( HD ) is an inherited disorder caused by mutation of the gene that encodes the huntingtin protein . The specific mutation that results in disease is an increase in the copies of the amino acid glutamine in a stretch of repeated glutamines at the amino-terminus of the protein . This “expanded polyglutamine” huntingtin acquires toxic properties , presumably through mechanisms that involve its reduced solubility and aberrant interactions with other cellular proteins that do not occur with the normal protein . In this study , we sought to identify cellular processes that were involved in the toxicity conferred by the mutant huntingtin protein . We used RNA interference in order to specifically reduce the levels of individual cellular proteins and identified a number that could reduce mutant huntingtin toxicity . These modifiers clustered into functional pathways know to be involved in HD and other novel pathways . Among these modifiers , we found that the signaling protein RRAS , as well as additional members of its signaling cascade , are involved in mutant huntingtin toxicity . We further showed that a small molecule inhibitor of an enzyme involved in this pathway is effective at reducing this toxicity , indicating that the targeted inhibition of the RRAS pathway may be of therapeutic benefit in Huntington's disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"neurobiology",
"of",
"disease",
"and",
"regeneration",
"neuroscience",
"gene",
"function",
"animal",
"models",
"drosophila",
"melanogaster",
"model",
"organisms",
"molecular",
"genetics",
"huntington",
"disease",
"biology",
"mouse",
"genetic",
"screens",
"neurology",
"neurological",
"disorders",
"genetics",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics"
] | 2012 | A Genome-Scale RNA–Interference Screen Identifies RRAS Signaling as a Pathologic Feature of Huntington's Disease |
The core components of the planar cell polarity ( PCP ) signaling system , including both transmembrane and peripheral membrane associated proteins , form asymmetric complexes that bridge apical intercellular junctions . While these can assemble in either orientation , coordinated cell polarization requires the enrichment of complexes of a given orientation at specific junctions . This might occur by both positive and negative feedback between oppositely oriented complexes , and requires the peripheral membrane associated PCP components . However , the molecular mechanisms underlying feedback are not understood . We find that the E3 ubiquitin ligase complex Cullin1 ( Cul1 ) /SkpA/Supernumerary limbs ( Slimb ) regulates the stability of one of the peripheral membrane components , Prickle ( Pk ) . Excess Pk disrupts PCP feedback and prevents asymmetry . We show that Pk participates in negative feedback by mediating internalization of PCP complexes containing the transmembrane components Van Gogh ( Vang ) and Flamingo ( Fmi ) , and that internalization is activated by oppositely oriented complexes within clusters . Pk also participates in positive feedback through an unknown mechanism promoting clustering . Our results therefore identify a molecular mechanism underlying generation of asymmetry in PCP signaling .
PCP is the tissue-level organization of cells in the plane of an epithelium , resulting from the coordinated acquisition of cellular polarity orthogonal to the apical-basal axis . PCP signaling controls the polarity of numerous epithelial cells in both Drosophila and vertebrates . In Drosophila , the most thoroughly studied planar polarized tissue is the fly wing , in which each cell produces a trichome ( “hair” ) , that in wild type , emerges from the distal side of the cell and points distally [1] . PCP mutants cause disruption of this pattern . In vertebrates , many of the PCP signaling components identified in flies are conserved , and function together with additional regulators not present in flies . Defects in vertebrate PCP result in a range of developmental anomalies and diseases including open neural tube defects , conotruncal heart defects , and disruption of sensory hair cell polarity . PCP is also believed to underlie the directed migration of malignant cells during invasion and metastasis ( reviewed in [2 , 3] ) . Despite increasing study , understanding of PCP signaling mechanisms remains limited . A key feature of PCP signaling is the generation of subcellular asymmetry in which critical signaling components segregate to form complexes on opposite sides of the cell . Components of the Drosophila PCP signaling mechanism may be divided into three functional module types including a core module , global directional modules and a suite of tissue specific effector modules that execute morphological polarization in individual tissues [4] . The core module acts both to amplify asymmetry , and to coordinate polarization between neighboring cells , producing a local alignment of polarity . Proteins in the core signaling module , including the serpentine receptor Frizzled ( Fz ) [5 , 6] , the multi-domain protein Dishevelled ( Dsh ) [7 , 8] , the Ankryin repeat protein Diego ( Dgo ) [9] , the 4-pass transmembrane protein Van Gogh ( Vang; a . k . a . Strabismus ) [10 , 11] , the Lim domain protein Prickle ( Pk ) [12] , the seven-transmembrane atypical cadherin Flamingo ( Fmi; a . k . a . Starry night ) [13 , 14] , and perhaps others [15 , 16] , adopt asymmetric subcellular localizations that predict the hair polarity pattern ( reviewed in [17] ) . These proteins communicate at cell boundaries , recruiting one group to the distal side of cells , and the other to the proximal side , thereby aligning the polarity of adjacent cells [18 , 19] . Insight into this mechanism comes from studies of clones either not expressing or overexpressing core PCP components . These clones display characteristic perturbations ( or lack thereof ) of cells in nearby wing tissue ( referred to as domineering non-autonomy ) [5 , 10 , 20 , 21] that have been exploited in conjunction with mathematical modeling to better understand the signaling mechanisms ( reviewed in [22] ) . Several global modules have been proposed to provide tissue-level directional information to the core module , aligning polarization to the tissue axes . These include the Fat/Dachsous/Four-jointed module [23] , Wnt4/Wg [24] , and other undefined signals [25] . The Ft/Ds/Fj module is thought to orient core signaling by organizing polarized microtubule-dependent vesicular trafficking of distal core proteins [26–28] . An important unanswered question is how asymmetric subcellular localization and amplification of core components is achieved . It is proposed that an input bias from one or more of the global modules is amplified by feedback mechanisms , eventually producing strong asymmetric localization of core components . Theoretical models indicate that polarization requires a combination of a short range cooperative ( positive ) feedback and a long range inhibitory ( negative ) signal [29] . Polarization of isolated cells requires that the long range signal be intracellular and diffusible . However , coordinated polarization within sheets of cells allows the possibility that the long range negative signal , as well as the short range positive signal , might operate through contact-mediated intercellular mechanisms . Cell biological and biophysical analyses have indicated that the transmembrane core components Fz , Fmi and Vang can assemble into stable intercellular complexes mediated by Fmi homodimerization and perhaps direct interaction between Fz and Vang ( [Fz-Fmi]-[Fmi-Vang] , where square brackets indicate complexed proteins in adjacent cell membranes ) [30 , 31] . These complexes may be regulated by internalization and recycling [32] . Additional analyses suggest that the peripheral membrane associated core proteins Dsh , Pk and Dgo are required for amplification of core asymmetry [32 , 33] . These proteins induce stabilization of intercellular complexes and clustering into discrete puncta , of which presence and size correlate with amplification of asymmetry [32] . Clustering may be a mechanism for positive feedback . In addition , it has been proposed that oppositely oriented intercellular complexes antagonize each other , a process we refer to as mutual antagonism [18 , 19] . This competitive inhibition could be a form of negative long range intercellular feedback . Molecular mechanisms for these events are not known . Regulation of levels of each of the core proteins is required for correct core PCP function , such that clonal loss or gain of function perturbs normal polarization ( summarized in [18] ) . Ubiquitinylation has been identified as a regulator of core PCP function . E3 ligases covalently attach ubiquitin to target proteins to control protein levels and intracellular trafficking of membrane proteins [34 , 35] . Poly-ubiquitinylation often works as a proteasomal degradation signal , while mono-ubiquitinylation of membrane proteins typically drives targets toward vesicular trafficking pathways . Mammalian Prickle-like proteins undergo Dvl ( Dsh ) -dependent ubiquitinylation by Smurf E3 ubiquitin ligases and degradation at the cell surface that is required for cellular asymmetry and neural tube closure [36] . In Drosophila , the Cullin 3 ( Cul3 ) E3 ligase complex regulates apicolateral Dsh levels , which in turn , regulates accumulation of other polarity proteins [37] . Disruption of this mechanism causes only mild PCP phenotypes . Recently , RNA interference ( RNAi ) of SkpA ( a Cul1 complex component ) was shown by both immunofluorescence and Western blot to produce accumulation of Pk in pupal wings [38] . No PCP phenotype was demonstrated or consequences characterized , nor was ubiquitinylation of Pk demonstrated . To characterize ubiquitinylation pathways for PCP control , we carried out an RNAi screen for Drosophila E3 ligases and found Cul1 E3 ligase complex components as regulators of PCP in Drosophila wings . We provide evidence suggesting that the Cul1 complex directly regulates Pk levels and , indirectly , accumulation of other core PCP proteins . Blocking this mechanism disrupts asymmetric subcellular localization of core PCP proteins and correct hair orientation . Furthermore , our results lead us to describe a mechanism for feedback regulation required to generate asymmetry . We provide evidence for an intercellular long range inhibitory mechanism ( mutual exclusion ) , in which Pk , triggered by Fz complexes , antagonizes the accumulation of Pk-Vang-Fmi complexes by promoting internalization . These results show that Cul1 complex-mediated control of Pk is required to ensure amplification and molecular polarization of the core module .
To identify ubiquitinylation pathways regulating PCP , RNAi constructs knocking down E3 ubiquitin ligases were screened for wing hair polarity defects . In a previous genome-wide in vivo RNAi screen , pnr-GAL4 ( and MS1096GAL4 ) driven expression of RNAi’s targeting several E3s showed notal bristle polarity defects suggesting possible PCP defects [39] . However , many E3 RNAi’s caused lethality , and for these polarity could not be assessed . To overcome this limitation , we re-examined E3 RNAi constructs that caused notal bristle polarity defects and those that caused lethality by clonal over-expression using the FLP-out technique . Two independent RNAi lines knocking down each of cul1 and skpA showed wing hair polarity defects including swirling patterns and multiple hairs . This phenotype was also observed upon induction of cul1 mutant clones ( Fig 1A , 1C , and 1E ) . Pre-hairs within cul1 and skpA knock-down clones showed abnormal polarity , and delayed emergence , as is frequently seen in PCP mutants ( Fig 1B , 1D , and 1F ) [1] . Furthermore , pre-hairs in neighboring wild-type cells , up to five to six cells away , grew toward mutant clones ( Fig 1B , 1D , and 1F ) , suggesting that the Cul1 complex affects a PCP process that acts both cell autonomously and non-autonomously . Cul1 and SkpA are components of the SCF E3 ligase complex , known to regulate the cell cycle and various signaling pathways including the Wnt and Hedgehog pathways [40 , 41] . Our screen suggests that they may also work together to regulate hair polarity . F-box protein components of the SCFs sequester substrates to the complex for ubiquitinylation , thereby determining substrate specificity of the E3 complex [42] . Similar to cul1 and skpA , knock-down clones of the F-box protein Slimb produce retarded and abnormally polarized trichome emergence , and showed domineering non-autonomy ( Fig 1G ) , suggesting that Slimb is the F-box protein mediating the function of the Cul1 complex in trichome polarization . Notably , Slimb has recently been implicated in regulating the Par-3/Par-6/aPKC complex to control polarity of the Drosophila oocyte , follicle cells , and imaginal disc cells [43 , 44] . Clones mutant for cell-cycle regulators show multiple hairs that correlate with irregular cell shape and size , and that are reminiscent of those seen in Cul1 complex mutant clones [45 , 46] . However , these phenotypes are not closely correlated in SCF complex knock-down or mutant clones , such that even clones with relatively normal cell shape and size show profound polarity defects . Furthermore , retardation of hair growth and non-autonomous hair polarity defects were not observed for cell-cycle regulator mutants . Polarity defects caused by Cul1 complex mutation are therefore not likely to result from cell cycle-dependent alterations of cell size and shape . The nature of the cul1 clonal non-autonomy is more similar to that seen with core module components as compared to global module components . We therefore explored whether the Cul1 complex could regulate a core protein ( s ) . Fmi , Fz , Dsh , Vang , and Pk , all accumulated to elevated levels at the apical membrane in Cul1 complex knock-down and mutant clonal wings ( Fig 2A–2E and S1 ) , suggesting that the Cul1 complex indeed regulates core proteins . The domineering non-autonomy associated with Cul1 complex mutant or knock-down clones shows hairs in wild-type cells growing toward the clones . Since trichomes emerge on the side of the cell where the ‘distal’ proteins Fz , Dsh , and Dgo , are located , we examined whether cul1 mutant cells affect the polarity of neighboring cells by recruiting distal core proteins and repelling proximal core proteins at the adjacent boundary of neighboring cells . Fz::GFP or Vang::YFP expressed only in wild-type cells surrounding cul1 mutant clones show that cul1 mutant cells attract Fz::GFP but repel Vang::YFP ( Fig 2E and 2F ) . This phenotype resembles overexpression of proximal core proteins or loss of the distal core protein Fz [30] , suggesting that Cul1 complex mutation either up-regulates a proximal or down-regulates a distal core protein ( s ) . To identify the potential target of Cul1 , we compared the profile of effects of cul1 knock-down or mutant clones on hair polarity and the levels of other core proteins to those caused by either loss-of-function or overexpression of each of the other core components ( S1 Table ) . Like cul1 knock-down or mutant clones , only Pk overexpression reorients hairs in neighboring cells to point toward the clone and induces elevated levels of all other core factors at intercellular junctions . Furthermore , we and others have observed that , like cul1 knock-down or mutant clones , Pk over-expression induces co-clustering of Fmi , Fz , and Dsh at membrane domains where Pk accumulates [19 , 33] . These observations suggest that Pk is a likely target of Cul1 complex mediated degradation . These results do not rule out the possibility that other unknown factor ( s ) or other core proteins might also be regulated by the Cul1 complex to control PCP . If Pk is a principle target of Cul1 in PCP signaling , the effects of cul1 knock-down or mutation should be suppressed by loss of Pk . To test this , cul1 RNAi clones were generated in pkpk-sple mutant wings and patterns of core proteins were analyzed ( Fig 3A ) . Effects of cul1 RNAi on core protein accumulation were abolished in pkpk-sple mutant wings , such that , for both Fmi and Fz , signal strength inside and outside of clones was indistinguishable ( Fig 3A ) . As expected , asymmetry of Fmi and Fz was disrupted in pkpk-sple wings . Pk is therefore required for the Cul1 complex to act on the core mechanism , and taken together with the full phenocopy of cul1 knock-down or mutation by Pk overexpression ( sufficiency; S1 Table ) , the complete blockade ( necessity ) suggests that no other Cul1 targets play a significant role in Cul1’s ability to modify core PCP activity . Drosophila is believed to have three Pk isoforms , two of which , PkPk and PkSple , are important for epithelial planar cell polarity [12] . Hair polarity in the wing requires PkPk but not PkSple , although some PkSple is expressed [27] . We therefore examined whether both isoforms are regulated by the Cul1 complex . Fmi protein accumulated in cul1 RNAi clones generated in either a pkpk/pkpk-sple or pksple/pkpk-sple background ( Fig 3B–3D ) , indicating that Cul1 regulates both the PkPk and PkSple isoforms . Though our antibody detects only very low levels of diffuse Sple signal in pkpk/pkpk-sple wings ( signal is assumed to represent Sple since the Pkpk isoform is not expected to be expressed; Fig 3C’ and 3D’ ) , the signal is stronger in cul1 RNAi clones than in surrounding tissue . Nonetheless , total PkSple levels remain well below levels of the Pkpk isoform . We next asked if Cul1 complex activity acts post-transcriptionally on Pk . Like endogenous Pk , levels of GFP::Pk expressed from a heterologous promoter are increased in 28hr APF cul1 mutant clones ( Fig 3E ) , thus demonstrating a post-transcriptional regulation of Pk protein levels . The same was true during third instar ( S2B and S2C Fig ) and 24hr APF pupal wings ( S2D and S2E Fig ) , suggesting that Cul1 is likely required throughout the course of PCP signaling . These data are consistent with Pk being a ubiquitinylation substrate of the Cul1 complex , though they do not rule out an indirect effect . To test whether Pk could be a target of the Cul1 complex , we sought evidence of a physical interaction between Pk and the Cul1 complex . As Slimb appeared to be the F-box protein providing substrate specificity to the Cul1 complex for its PCP function , we first asked whether Slimb might colocalize with Pk . Consistent with this possibility , in wildtype tissue , ubiquitously expressed Myc::Slimb protein was detected at the apical cell boundary , with a very subtle asymmetric localization that appears to be on the proximal side of the cell ( arrow heads in Figs 4A and S3A–S3C ) . In pkpk-sple mutant clones , Myc::Slimb staining was diminished both at the apical cell junctions and also more basally in the absence of Pk ( Fig 4A ) , indicating that retention of Slimb protein depends on Pk . Furthermore , in wildtype tissue near a clone that induces domineering non-autonomy , orientations of Pk and Myc::Slimb are coordinately reorganized ( S3C Fig ) . To further assess dependence of Slimb localization on Pk , GFP tagged PkPk or PkSple isoforms were clonally overexpressed in wings ubiquitously expressing 6XMyc::Slimb . Upon GFP::Pk or GFP::Sple overexpression , Myc::Slimb was sequestered at the apical membrane ( Fig 4B and 4C ) , suggesting that they might physically form a complex . In contrast , clonal overexpression of Fmi::YFP failed to sequester Myc::Slimb ( S3A Fig ) . Since Fmi overexpression causes Fz and Dsh accumulation [47] , neither Fmi , Fz or Dsh cause Slimb accumulation . Vang overexpressing clones showed slightly higher Myc::Slimb staining ( S3B Fig ) , though less robust than PkPk or PkSple overexpression . Previous studies showed that F-box proteins are often ubiquitinylated by their own Cul complex in the absence of other substrates , and this provides a mechanism by which they can exchange their F-box proteins and substrates [48 , 49] . Consistent with this , Myc::Slimb protein levels were increased in cul1 knock-down clones ( S3D Fig ) . More importantly , these results support the possibility that both Pk and Sple isoforms are substrates of the Cul1 complex . If Pk is a substrate for the Cul1 complex , and if ubiquitinylation targets it for degradation , then in addition to co-dependent localization , levels of Pk should be dependent on Cul1 . The elevation of endogenous Pk or exogenous Myc::Pk levels upon cul1 knock-down as shown by immunofluorescence was corroborated by increased levels detected by Western blot in cul1 knock-down wing discs ( Fig 5A and 5B ) , consistent with Cul1 dependent destabilization of Pk protein . Furthermore , If Pk is a substrate for Cul1 complex activity that targets for proteasomal degradation , then uibquitinylated Pk should be detected when proteasome activity is impaired . We identified conditions in which expression of the dominant negative form of the proteasomal subunit , Prosbeta2’ knocked down proteasome activity without causing lethality . Under these conditions , we detected slowly migrating forms of Myc::Pk , consistent with ubiquitinylation , that were diminished upon cul1 knock-down , suggesting that these are ubiquitinylated forms of Pk ( Fig 5C ) . Finally , we verified that the slowly migrating forms of Pk are indeed ubiquitinylated by immunoprecipitating Myc::Pk from wing discs with knocked down proteasome activity , either with or without cul1 knock-down , and probed for Ubiquitin ( Fig 5D ) . Ubiquitin signal was stronger without cul1 knock-down; therefore , levels of ubiquitinylated Pk depend on Cul1 activity . These results together argue that elevated endogenous Pk or exogenous Myc::Pk levels in cul1 knock-down wing discs ( Fig 5A and 5B ) are caused by defects in Cul1-mediated ubiquitinylation and proteasomal degradation of Pk . We do not rule out the unlikely possibility that the Cul1 complex , acting in close proximity to Pk , acts on an intermediate substrate that in turn regulates ubiquitinylation of Pk . Overexpression of a C-terminal deleted form of PkPk ( HA-PkdC; aa1-472; supplemental information ) showed less membrane localization and greater expression in the cytosol and nucleus as compared to wild type PkPk , as expected due to deletion of both the CaaX sequence and Vang binding domains ( S4E and S4F Fig ) , This truncated Pk did not accumulate in cul1 mutant clones ( Fig 3F ) nor did it sequester Myc::Slimb ( Fig 4D ) , suggesting the possibility that the C-terminal domain of Pk contains one or more Slimb binding sites , though these observations may also be explained by reduced membrane recruitment or failure to interact with Vang . Of note , USPX9 , the human de-ubiquitinylating ortholog of Fat Facets ( Faf ) , interacts with the C-terminus of vertebrate Pk1 and Pk2 , suggesting C-terminal ubiquitinylation of these proteins [50] . Additional work will be required to more precisely define the regions of Pk required for Cul1 ubiquitinylation Levels of each of the core PCP proteins must be regulated to achieve normal polarization . Our results thus far indicate that the Cul1 complex regulates Pk levels . Previous studies also showed that farnesylation and association with Vang are required to regulate Pk levels [33 , 38] . However , the function of Pk in core PCP function is not well understood , and these results fail to illuminate how excess Pk perturbs normal core PCP function . Core PCP proteins assemble into asymmetric complexes , with Fmi homodimers spanning intercellular junctions , and the proximal proteins Vang and Pk associated with one side , and the distal proteins Fz , Dsh and Dgo associated with the other side of the complex ( [Dgo-Dsh-Fz-Fmi]-[Fmi-Vang-Pk] ) . Asymmetric subcellular localization represents the preferential accumulation of this complex oriented in one direction and exclusion of the oppositely oriented complex . Pk has been suggested to mediate a mutual exclusion to facilitate this function via feedback [19] . Pk has also been implicated in stabilizing [Fz-Fmi]-[Fmi-Vang] complexes [19 , 33 , 47] . Though the mechanism by which Pk carries out its function is unknown , it has been known for some time that overexpression of Pk induces co-clustering of core proteins Fmi , Dsh and Vang at junctional domains [19 , 33] . Clustering of Fmi does not require Fz [33] . As Pk interacts with Vang , it has been assumed that Pk acts via Vang to stabilize intercellular bridges . Consistent with this idea , we observed that upon Pk overexpression , co-clustering of Pk and Fmi does not depend on Fz , but does indeed depend on Vang ( Fig 6A–6D ) . Similarly , Pk co-clustering with Fmi at apical junctions in cul1 knock-down clones is observed in fz mutant wings ( S2A Fig ) . These results confirm that Pk can induce clustering of Fmi complexes through Vang , but independent of the distal core protein , Fz ( [Fmi]-[Fmi-Vang-Pk] ) . We note that while Pk overexpression can induce accumulation of Vang in the absence of Fmi , the characteristic clustering is not observed ( S4A and S4B Fig ) . While Pk overexpression induces clustering and enrichment of core components at apical junctions , several observations suggested additional functions . First , in addition to clustering at the apical cell membrane , we noticed that overexpression greatly increased the amount of GFP::Pk that was localized in cytosolic puncta , presumably consisting of vesicles , that are observed from apical to basal regions of the cell ( apical planes in Fig 6A and 6B , and 6C , sub-apical planes in Fig 7 ) . Furthermore , formation of Fmi vesicles was also induced , and Fmi staining co-localized with GFP::Pk ( most Fmi positive puncta were also GFP::Pk positive ) ( Figs 6A and 7A ) . We infer that these double labeled puncta are also positive for Vang since nearly all Pk positive puncta in Pk overexpressing cells are also positive for Vang::YFP ( Figs 6B and 7B ) . To determine whether the Pk dependent puncta are vesicles resulting from an endocytic process , FM4-64 dye uptake assays were carried out . As FM4-64 cannot penetrate the plasma membrane of live cells , FM4-64 positive puncta indicate endocytic vesicles from internalization of plasma membrane [51] . Live pupae with exposed wings were briefly bathed in FM4-64 and live wings directly subjected to confocal microscopy . To assay for internalization of apical [Fmi-Vang-Pk] complexes , apical and subapical puncta were analyzed . Of GFP::Pk ( driven by ptc-GAL4 ) positive puncta , 36 to 53% ( five wings; Fig 8A ) were positive for FM4-64 . Similarly , when Pk was clonally overexpressed in the presence of Vang::YFP 55 to 74% of Vang::YFP positive puncta were positive for FM4-64 ( Fig 8B;five wings tested ) . This fraction was increased compared to regions not overexpressing Pk ( 22 to 41%; five wings tested ) . Note that these percentages are expected to be lower bounds of the true values due to brief exposure to the dye . These results support the idea that Pk mediates endocytosis of Vang . We then asked whether endogenous levels of Vang vesicles depend on endogenous Pk . Consistent with our conclusion , in the absence of Pk ( pkpk-sple mutant clones ) , the number of Vang::YFP positive puncta was reduced to 77% of that in wildtype cells ( 77± 9% , p = 0 . 0025; Vang::YFP puncta counted from 412 mutant and 1000 wildtype cells in pkpk-sple mutant clones in five wings ) . Taken together , these results demonstrate that Vang ( together with Fmi ) is endocytosed with Pk , and that increased Pk stimulates this internalization . Not surprisingly , these data also indicate that a fraction of Vang::YFP positive vesicles are independent of Pk , perhaps representing distinct recycling and/or biosynthetic pathways . In the absence of Vang , GFP::Pk overexpression still produced GFP::Pk positive vesicles , but Fmi positive internalization was greatly reduced ( Figs 6C and 7C ) , These results are consistent with the model that Pk dependent internalization of Fmi requires Vang . Conversely , depletion of Fmi does not affect Pk dependent internalization of Vang ( Figs 7E and S4B for fmi knock-down efficiency ) . Based on these results , we hypothesize that Pk overexpression induces the coordinate internalization of Vang and Fmi through an endocytic pathway , and that internalization of Fmi requires interaction of Pk with Vang . Unlike internalized vesicles containing Fz and Dsh , a substantial fraction of which remain in the apical part of the cell and is seen to transcytose in a microtubule-dependent fashion [26–28] , these vesicles do not transcytose , and are likely either recycled or targeted for degradation . While we have examined these vesicles in the context of manipulating Pk levels directly , we also see excess vesicle formation in cul1EX mutant clones ( S5A Fig ) , reinforcing the role of Cul1 dependent ubiquitinylation in regulating this function . An additional observation is consistent with the hypothesis that Pk promotes internalization of Vang and Fmi . While both Pk overexpression and Cul1 depletion lead to increased , clustered core PCP protein distribution , simultaneous Pk overexpression and Cul1 depletion produced even higher levels of Pk , resulting in even greater vesicle formation and depletion of Fmi from apical junctions ( S5B , S5C , and S5D Fig ) . Thus , while moderately elevated Pk causes clustering and accumulation of core components , very high levels result in removal of core components from the membrane . This is consistent with the possibility that rather than being independent , clustering is associated with internalization , with modest levels of clustering and internalization facilitating amplification and extreme levels resulting in depletion of Vang-Fmi from junctions . In contrast to clustering , Pk-dependent Fmi vesicle formation was substantially diminished in fz mutant tissue ( Figs 6D and 7D ) . The requirement of Fz for Pk to stimulate Fmi-Vang-Pk vesicle formation suggests that it is the interaction between full length complexes , most likely within clusters of complexes in opposite orientations that promotes internalization ( Fig 9G ) . This conclusion is further supported by the observation that clones overexpressing Pk , which induce recruitment of Fz to the neighboring cell boundaries and therefore hairs to point toward the clone , simultaneously induce Vang vesicle formation in the adjacent cells ( S6 Fig ) . We note that in fz mutant clones , apicolateral Fmi , the pool from which vesicles may be drawn , is modestly reduced , perhaps by a factor of two [47] . However , there is a greater than 10 fold reduction in vesicle formation , suggesting reduced apicolateral Fmi alone cannot explain the decrease in vesicles . The same is true for vang clones . Fz-dependent internalization of Fmi-Vang-Pk vesicles is likely to be involved in the generation of asymmetry , as competition between oppositely oriented complexes leading to dose-dependent removal of the less abundant complex would amplify asymmetry . Additional evidence for the idea that clustering might be associated with internalization and amplification comes from visualization of asymmetric localization . Asymmetric localization can be visualized by generating twin clones expressing PCP proteins tagged with different fluorophores , and examining shared cell boundaries . This experiment was performed at 16 h APF , when asymmetric localization is detectable , but incomplete , as assessed by traditional methods . As expected , labeling one clone with a distal protein , Fz::ECFP or ECFP::Dgo , and the adjacent clone with the proximal protein Vang::EYFP , shows overlapping accumulation of distal and proximal proteins , with some clustering in puncta ( Fig 9A , 9B , 9D , and 9E ) . In contrast , when both twin clones are labeled with the same protein , in this case Vang::ECFP and Vang::EYFP , asymmetry would be reflected by unequal amounts on the distal and proximal sides of the boundary . Notably , we observed unequal amounts of proximal and distal Vang in puncta , but not in the more diffusely localized protein ( Fig 9C , 9F , and 9J ) . This observation indicates that the generation of asymmetry in core PCP protein complexes is occurring within clusters . Pk is not needed for assembly of complexes including [Fz-Fmi]-[Fmi-Vang] , but does play a role in stabilizing these complexes , perhaps via clustering [32 , 33] . However , Pk is also needed for efficient generation and propagation of polarity , a function hypothesized to occur by negative feedback [19] that appears to occur among clustered complexes . We initially proposed that negative feedback involves the Vang dependent exclusion of [Dsh-Fz-Fmi] complexes [18 , 19] . However , our current observations suggest that negative feedback might also occur in the opposite direction , in the form of Fz-dependent removal of proximal complexes ( Fmi-Vang-Pk ) by endocytosis ( Figs 6D , 7D , and 9G ) . We therefore further examined the requirement for Pk in the feedback-dependent amplification/mutual exclusion observed during intercellular PCP communication . Overexpression of Fz causes hairs in neighboring cells to point away from the clone; this is associated with recruitment of Vang to the boundary of neighboring cells , and at the same time repulsion of Fz from that boundary [30] . Conversely , we observe that overexpressing Vang causes hairs in neighboring cells to point toward the clone , and this is associated with recruitment of Fz and exclusion of Vang from the neighboring cell border ( Fig 9I ) . Because Pk appears to act via Vang , we tested whether Pk might be required for this exclusion of Vang in the wildtype neighbor . We observe that whereas in wildtype cells adjacent to a Vang overexpressing clone , Vang is excluded from the cell boundary , in pk mutant cells Vang exclusion was not observed ( Fig 9K 9L , and 9M ) . In contrast , Pk is not required for recruitment of Vang adjacent to cells that express Fz but not Vang ( vang mutant clone ) ( S7 Fig ) . These results support the idea that Pk regulates Vang removal .
In this study , we have shown that Cul1 complex-mediated ubiquitinylation of Pk is required for correct function of the core PCP signaling module , thereby ensuring proper alignment of hairs on the Drosophila wing . Ubiquitinylation by the Cul1 complex targets Pk for proteasome-dependent degradation , and in its absence , excess Pk accumulates , resulting in disruption of core PCP function . In several previous reports , ubiquitinylation has been recognized to regulate PCP signaling . In a mouse model , Smurf E3 ligases were shown to regulate PCP signaling by modulating Pk levels [36] . However , mutation of Drosophila smurf failed to show PCP defects [37] . In Drosophila , Cul3 E3 ligase-BTB protein-mediated regulation of Dsh ubiquitinylation modulates PCP signaling , as does the de-ubiqutinylating enzyme Faf , possibly acting on or upstream of Fmi [37] , or more recently proposed to act on Pk [50] . Loss of either activity shows subtle effects on final PCP outcomes in Drosophila . In no case is there a demonstrated mechanism for how these events impact the characteristic asymmetric subcellular localization of PCP proteins that underlies cell polarization . We find that Slimb is the F-box protein that mediates Pk and Cul1 complex association in vivo . It appears likely that the motif that mediates interaction between Pk and Slimb resides in the C-terminal half of the protein , as do the Vang interaction domain and the farnesylation ( CaaX ) motif [52] . Of note , the amount of Slimb protein in the cell was also dependent on Pk ( Fig 4A ) . In previous cell culture studies , F-box proteins themselves were targeted for ubiquitinylation by their own Cul complexes when not bound by other substrates [48] , and this appears to be the case here , as Slimb levels are increased in cul1 knock-down clones ( S3D Fig ) . Furthermore , this result supports the idea that Pk is the major target of the Cul1 complex during pupal wing development . If the Cul1-SkpA-Slimb complex targets Pk for degradation , why do Slimb and Pk accumulate together on the proximal side of wildtype cells ? Pk is known to bind to Vang , and to localize with it in the proximal complex . Slimb adapts the Cul1 complex to Pk and is seen to colocalize with Vang on the proximal side , as well as with overexpressed Pk ( Fig 4 ) . However , this suggests that the Pk in this location is resistant to Cul1 complex-dependent degradation . Pk levels have long been known to be limited by a Vang-dependent activity [33] . Recently , Strutt et al . showed that farnesylation of Pk is required for Pk to interact with Vang and promote its degradation , and that levels of Pk also depend on SkpA , leading to the suggestion that farnesylation-dependent Pk-Vang interaction results in SkpA-dependent Pk degradation [38] . We provide evidence suggesting that the Cul1-SkpA-Slimb E3 complex directly targets Pk for destruction , but in contrast , our finding that Pk with deleted CaaX domain accumulates to elevated levels in cul1 knock-down cells ( S4C and S4D Fig ) indicates that Cul1/SkpA/Slimb-dependent degradation is independent of farnesylation . Furthermore , our finding that Pk promotes internalization of Fmi-Vang-Pk during mutual exclusion of oppositely oriented core PCP complexes leads to a model , described below , that is consistent with our shared observation that Pk associated with stable intercellular complexes ( [Dsh-Fz-Fmi]-[Fmi-Vang-Pk] ) is protected from degradation . In theory , generation of cell polarity requires the combination of a local self-enhancement of a cell polarity factor and a long range inhibition of the same factor [29] . In isolated cells , likely the evolutionarily more ancient mechanism , intracellular local self-enhancement can arise through cooperativity among P proteins ( Fig 10A ) . Intracellular long range inhibition is most easily accomplished by limiting amounts of a component of the P complex , such that aggregation of P complexes in one location decreases the probability of aggregation elsewhere by depletion of that component . Cell polarization within a multicellular system introduces additional possible intercellular mechanisms for both the local self-enhancement and the long range inhibition ( Fig 10B and 10C ) . If two polarity complexes , P and Q , exist , and can interact at junctions between adjacent cell boundaries , then both the local and long range effects can be mediated through these intercellular interactions . If P complexes recruit Q complexes to opposing sides of junctions , and if mutual antagonism between P and Q occurs , then long range inhibition can occur by P recruiting Q to the neighbor , where P is then excluded ( Fig 10B ) . Similarly , exclusion of P decreases Q in that region of the original cell , enabling the accretion of more P ( in effect , cooperativity ) ( Fig 10C ) . The peripheral membrane associated core PCP proteins Pk , Dsh and Dgo appear to mediate these polarization events , but how they do so is not known . They are not required for assembly of asymmetric [Fz-Fmi]-[Fmi-Vang] complexes , but were known to share the ability to induce clustering , and are all required for the feedback amplification that results in the asymmetric subcellular localization of PCP signaling complexes . While their action somehow promotes the assortment of proximal and distal core proteins to opposite sides of the cell , how they carry out this function , and in particular whether this is through intracellular or intercellular mechanisms , is unclear . To understand how excess Pk resulting from mutation of the Cul1 E3 complex disrupts PCP , we further studied Pk’s role in establishment of core asymmetry . pk mutation causes symmetric distribution of other core proteins without substantially diminishing or enhancing their junctional recruitment . On the other hand , Pk overexpression causes both accumulation of higher levels of all proximal and distal core proteins and induces their clustering at apical membrane domains , generating discrete puncta . A Pk induced clustering of similarly oriented core complexes , as previously proposed [32] , could explain both the aggregated punctate appearance and the increased levels of accumulated proteins if one assumes a steady state relationship between free asymmetric complexes and unassembled components as asymmetric complexes are sequestered into puncta . Our Pk over-expression study shows that Fz is not required for making Fmi clusters , but Vang is ( Fig 6 ) . This suggests an intracellular mechanism in which Pk interacts with Vang at the apical membrane to induce clustering . However , since Vang over-expression does not cause accumulation of other core proteins [33] , a specific function for Pk beyond stabilization of Vang must be considered to explain the accumulation of other core proteins . Furthermore , the depletion of Fmi from the membrane achieved by the very high levels of Pk upon simultaneous Pk overexpression and Cul1 depletion ( S5B , S5C , and S5D Fig ) argues for a function for Pk beyond clustering . Pk might stimulate amplification simply by promoting clustering , with long range inhibition mediated by other mechanisms , or perhaps by limiting amounts of Pk . However , our data suggest an alternative interpretation , as we observe Pk-dependent mutual exclusion of oppositely oriented complexes: forcing local accumulation of distal proteins induced the Pk-dependent removal of proximal proteins within the same cell ( Fig 9G , 9I , 9K , 9L , and 9M ) . Exclusion is associated with Pk mediated internalization of Pk-Vang-Fmi complexes , suggesting that this exclusion involves endocytosis ( Fig 10D; Pk positive vesicle ) . The requirement for Vang in this internalization is consistent with a previous study showing that Vang contributes to Fmi internalization [47] . We therefore propose that Pk is involved in an intercellular long range inhibition to promote feedback amplification ( Fig 10D ) . Like clustering , the Pk-induced routing of Fmi into intracellular vesicles was dependent on Vang , and Pk , Vang and Fmi colocalize in vesicles both apically and more basally ( Figs 6 and 7 ) , indicating that Fmi-Vang complex trafficking is regulated by associated Pk . However , unlike clustering , it is also dependent on Fz ( Figs 6D and 7D ) . This suggests a model for feedback inhibition in which oppositely oriented asymmetric complexes interact within clusters , leading to endocytosis and removal of Pk-Vang-Fmi . Competitive interaction between the proximal protein Pk and the distal protein Dgo for Dsh binding is known to occur [19 , 52–54] , suggesting that these interactions might result in either of two alternative outcomes , one of which would be disruption of the proximal complex , and the other disruption of the distal complex ( Fig 10D ) . We propose that if the distal complex ‘wins , ’ thus remaining stable , the proximal Pk-Vang-Fmi complex becomes internalized in a Pk-dependent step . Once there is a predominance of complex in a given orientation , Vang will be enriched on one side of the intercellular boundary with relatively little Fz present . Since Pk and Slimb associate with Vang , they too will be enriched , but the absence of competitive interactions from the Fz complex allows them to remain within clusters , accounting for the accumulation of Pk and Slimb on the proximal side of wildtype wing cells . According to this model , Pk and Slimb are observed primarily at sites where they are inactive and therefore not internalized . Modest levels of Pk overexpression both enhance accumulation of PCP protein complexes at the membrane and disrupt the normal orientation of polarization . This may be explained by enhanced feedback amplification that overwhelms the ability to interpret directional inputs . In contrast , the depletion of Fmi from the membrane observed with the very high levels of Pk induced by simultaneous Pk overexpression and Cul1 depletion ( S5B–S5D Fig ) suggests that sufficient Pk can induce internalization even without the competitive interactions from the Fz complex that normally stimulate internalization . The mechanism for Pk-dependent clustering is not known . As previously proposed , clustering may result from a scaffolding effect; the possibility of decreased endocytosis accounting for clustering was previously discounted [32] . Whatever the mechanism , clustering by Pk must occur independent of Fz ( Fig 6D ) . Furthermore , Pk must enable the multimeric aggregation of complexes containing [Vang-Fmi]-[Fmi] or [Vang-Fmi]-[Fmi-Fz] . Induction of multimeric clustering would also provide a context for the dose-dependent competition that determines internalization of either the proximal or distal complex . Additional work will be required to determine how Pk facilitates clustering . Since Cul1 depletion increases the amount of Pk , and excess Pk produces clustering and amplification , we now consider how Cul1 might produce the observed phenotype . The simplest possibility is that in the Cul1 mutant , excess Pk produces excess clustering and amplification that overwhelms the directionality in the system . However , because Pk is associated with Slimb and yet stable in the polarized state , and because Pk degradation is dependent on Vang , we also entertain the possibility that Cul1-dependent degradation is somehow functionally coupled to Pk-mediated internalization . Additional studies will be required to distinguish these possibilities . In summary , we propose a model in which Pk-dependent internalization of proximal complexes provides an intercellular long range inhibition that contributes to amplification of core protein asymmetric localization ( Fig 10D ) . At the same time , Pk provides a local cooperative effect by inducing clustering and accumulation of proximal complexes . We don’t know the mechanism for clustering , but a simple model is that Pk mediates closely related internalization events . We note that a similar intercellular long range inhibition was initially discussed long ago [18–20] , except that [Vang-Pk] was proposed to disrupt [Fz-Dsh] . This interpretation was based largely on inference . Here , we provide evidence that [Fz-Dsh] disrupts [Vang-Pk] ( by promoting internalization ) . On theoretical grounds , either one would be sufficient to cause polarization , but we don’t exclude the possibility that both may occur . Indeed , vesicles containing Fz , Dsh and Fmi have been shown to be transcytosed in a microtubule-dependent fashion with a directional bias [26–28] , and these vesicles appear to derive from apical junctions , where they may arise by exclusion . Although knock-down of smurf in flies reveals no function in PCP , the mechanism we describe is similar to that inferred for Smurf in mouse PCP [36] . Narimatsu and colleagues found that mice mutant for both Smurf1 and Smurf2 show PCP defects and lose asymmetric localization of core PCP proteins . Furthermore , biochemical evidence was provided that Smurfs , in the presence of the Dsh homolog Dvl2 ( and Par6 ) mediated ubiquitinylation of mouse Pk1 . From this , they proposed the model that proximal complexes containing Pk1 , and presumably Vang and Celsr ( Fmi ) , are disrupted upon proximity to distal complexes containing Fzd and Dvl2 . This model is similar to our model of mutual exclusion , except that the mode of disruption was not directly addressed . While we propose disruption by internalization , perhaps coupled to degradation , they were only able to address degradation . Furthermore , it is not known if , in mouse , Pk1 mediates clustering , perhaps by a related mechanism , as we describe in flies . The de-ubiquitinase USPX9 was recently identified as a regulator of Pk in the context of Pk’s role in epilepsy in human , mouse , zebrafish and flies [50] . Similarly , the orthologous Drosophila de-ubiquitinase Faf modulates the pksple dependent seizure phenotype in flies . These observations suggest that while the ubiquitinylating and de-ubiquitinylating activities of Smurf and USPX9 control the ubiquitinylation state of vertebrate Pk’s , Cul1 and Faf may serve the analogous function to regulate ubiquitinylation of Drosophila Pk .
All flies were grown at 25°C . The following alleles and stocks were used . FlyBase and VDRC id numbers , when available , are in parentheses . Detailed chromosomes and genotypes are provided in the figure legends . UAS-cul1RNAi ( VDRC# 108558 , 42445 ) ; UAS-skpARNAi ( VDRC# 32789 , 107815 ) ; UAS-slimbRNAi ( FBst0033898 , FBst0033986 ) ; UAS-fmiRNAi ( FBst0026022 ) ; FRT42D cul1EX ( from R . Duronio , UNC , Chapel Hill , USA ) , pkpk-sple13 [12]; FRT42D pkpk-sple13 ( FBst0044230 ) ; pkpk-sple14 [12]; pkpk30 ( FBst0044229 ) ; pksple1 ( FBst0000422 ) ; vangA3 [10]; FRT42D vangA3 [10]; vangstbm6 ( FBst0006918 ) ; fzR52; actP>CD2>GAL4 UAS-GFP ( from B . Lu , Stanford , Stanford , USA ) ; actP>CD2>GAL4 UAS-RFP ( FBst0030558 ) ; D174GAL4 [27]; actP>CD2>GAL4 ( FBst0004779 ) ; ptc-GAL4 ( FBst0002017 ) ; armP-fz::GFP [30]; Cas-dsh::GFP [27]; actP-vang::YFP; UAS-GFP::pk [12]; UAS-GFP::sple [12]; UAS-pkpk [12]; tubP-6XMyc::slimb ( from E . Verheyen , Simon Fraser University , Burnaby , Canada ) ; FRT42D ubiP-NLS::mRFP ( FBst0035496 ) ; UAS-fmi::YFP [30]; UAS-vang [30]; actP-GFP::pkdCaaX and actP-6XMyc::pk ( from D . Strutt , University of Sheffield , Sheffield , UK ) ; UAS-Dcr2 ( FBst0024646 ) ; FRT80B ( FBst0001988 ) ; UAS-lacZ RNAi ( R . Carthew , Northwestern University , Evanston , USA ) ; UAS-prosbeta2’ ( FBst0006785 ) . C-terminal deleted pk ( aa1-472 ) was tagged at the N-terminus with HA ( YPYDVPDYA ) and cloned into pUASt vector ( S1 Text ) . vang::EYFP , vang::ECFP , fz::ECFP and ECFP::dgo were generated by fusing the respective fluorophore at the 5’ or 3’ end of the corresponding cDNA . vang::ECFP and fz::ECFP carry an additional HA-tag sequence between the respective coding sequences . Sequences were cloned into pCM43-ubiP-SV40 [55] and integrated into the VK0033 landing site by target-site specific transgenesis [56 , 57] . Transgenic flies were generated by BestGene Inc . ( Chino Hills , CA , USA ) for the C-terminal deleted pk construct and direct injection for other constructs . FLP-on ( using the actP>CD2>GAL4 construct for trans-gene expression ) and FLP/FRT mitotic clones were generated by incubating third-instar ( for analyzing pupal wings ) or second-instar ( for analyzing wing discs ) larvae at 37°C for 1 hr and pupal wings ( 60 to 72 hr after heat shock ) and third-instar larval wing discs ( 48 hr after heat shock ) with appropriate clones were selected for analysis at indicated developmental time points . Primary antibodies were as follows: mouse monoclonal anti-Fmi ( 1:200 dilution , DSHB ) , guinea pig polyclonal anti-Pk ( 1:800 , [27] ) , rat monoclonal anti-HA ( clone 3F10 , 1:200 dilution , Roche ) , rabbit polyclonal anti-c-Myc ( 1:200 dilution , Santa Cruz ) , mouse anti-LacZ ( 1:500 dilution , Promega ) . Secondary antibodies from Life Technologies were as follows: 488-donkey anti-mouse , 488-donkey anti-rabbit , 594-donkey anti-mouse , 633-goat anti-guinea pig , 633-goat anti-mouse , 633-goat anti-rat , 647-goat anti-rabbit . Alexa 635 and Alexa 488 conjugated phalloidin were from Life Technologies . As mutation or knock-down of components in the Cul1 complex induces size and shape defects of cells , clonal wings with mild defects where cell shape and size are comparable with wildtype cells were selected for quantification . To measure the intensity of junctional core proteins in Fig 2 and S1 Fig , 50 P/D and 20 A/P junctions were randomly selected from cul1i clones and wildtype areas from each of five wings . Using Adobe Photoshop , mean intensity was obtained for P/D and A/P junctions and a background intensity from the non-junctional region was subtracted . Fold differences were calculated for each wing . Average fold differences were calculated and p-values were obtained from a t-test with values obtained from five wings for each genotype . Vesicle numbers were quantified by imaging through several planes and counting vesicles/cell in the relevant wildtype and mutant cells . Since this does not capture a complete apical-basal scan , numbers were normalized against the wildtype value to allow comparisons between mutant and wildtype , and expressed as a percentage for each image stack . To analyze adjacent clones expressing ECFP and EYFP-tagged PCP proteins in Fig 9 , pupae were dissected at 16 hAPF as previously described [58] . Pupal wings were imaged using an Olympus Fluoview FV1000 confocal microscope equipped with a 63x oil immersion lens . ECFP and EYFP were excited using 458 and 514 nm laser lines , respectively . Boundary profiles were measured in Fiji using a 3px wide line and the Plot Profile function . To plot normalized boundary profiles for the adjacent Vang::ECFP and Vang::EYFP twinspots , measured boundary intensities were normalized to the average pixel intensity along cell boundaries that only touch cells belonging to the respective twinspot . All other immunofluorescence images were taken with a Leica TCS SP5 AOBS confocal microscope and processed with LAS AF ( Leica ) and Adobe Photoshop . Adult wings were imaged with Spot Flex camera ( Model 15 . 2 64 MP ) equipped with Nikon Eclipse E1000M . Live pupae with partially ripped cuticle were incubated in 10μg/ml FM4-64 ( Life Technologies ) in M3 media ( Sigma-Aldrich ) for 15-30m at 25°C . After the incubation , wings were directly subjected to confocal microscopy . Third-instar larval wing discs were dissected and lysed in protein loading buffer . Lysates from eight discs were loaded per lane for SDS-PAGE analysis . Antibodies: Guinea pig polyclonal anti-Pk ( 1:1000 ) , mouse monoclonal anti-Myc ( 1:1000 , Sigma-Aldrich ) , mouse monoclonal anti-γ-Tubulin ( 1:1000 , Sigma-Aldrich ) . For the experiments using the temperature sensitive dominant negative form of proteasomal subunit , Prosbeta2’ , larvae with appropriate genotypes were incubated for 2hr 30 min at 30°C before isolating wing discs . Lysates from 15 wing discs were loaded in each lane . To immunoprecipitate Myc-tagged Pk , 80 wing discs from each genotype , grown under the same condition as for the Western blot assay , were lysed and ground in 50μl of lysis buffer ( 2% SDS , 150mM NaCl , 10mM Tris-HCl , pH 7 . 5 ) with 2mM sodium orthovanadate , 50 mM sodium fluoride , and protease inhibitors . After boiling samples for 5 min , 350μl of dilution buffer ( 150mM NaCl , 10mM Tris-HCl , pH 7 . 5 , 2mM EDTA , 1% Triton ) was added and samples were incubated at 4°C for 30min ( ref . modified from [59] ) . Samples were spun at 15 , 000 x g for 5 min , and anti-Myc affinity gel ( Biotool ) was added to the supernatants and the mixtures were incubated at 4°C for 3 hr . The remaining immunoprecipitation procedures were as described by the Biotool manual . Rabbit polyclonal anti-Ubiquitin antibody ( 1:2000 , Thermo Scientific ) was used to detect ubiquitinylated Myc::Pk . | Many epithelial cells display a level of organization in which cellular structures or appendages are positioned asymmetrically within the cell along an axis perpendicular to the apical-basal axis of the cell . When the direction of this polarization is coordinated within the plane of the epithelium , this phenomenon is referred to as planar cell polarity ( PCP ) . PCP is organized , at least in part , by a group of molecules that interact across cell-cell junctions and segregate into two groups that localize on opposite sides of each cell . Their asymmetric localization is thought to both produce molecular asymmetry , and to mark polarized domains within the cell for subsequent morphological polarization . In segregating to produce molecular asymmetry , these proteins participate in both positive and negative feedback , much like ferromagnets , to align their localization within and between neighboring cells . In this work , we identify a mechanism for negative feedback that utilizes the protein Prickle , one of the PCP signaling components . Levels of Prickle are precisely regulated , in part by a ubiquitinylation mechanism that targets excess protein for degradation . Prickle mediates internalization and removal of one class of PCP proteins , thereby causing repulsion of opposite ‘poles . ’ Excess Prickle disrupts this mechanism and interferes with establishing polarity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Clustering and Negative Feedback by Endocytosis in Planar Cell Polarity Signaling Is Modulated by Ubiquitinylation of Prickle |
It is now well established that in yeast , and likely most eukaryotic organisms , initial DNA replication of the leading strand is by DNA polymerase ε and of the lagging strand by DNA polymerase δ . However , the role of Pol δ in replication of the leading strand is uncertain . In this work , we use a reporter system in Saccharomyces cerevisiae to measure mutation rates at specific base pairs in order to determine the effect of heterozygous or homozygous proofreading-defective mutants of either Pol ε or Pol δ in diploid strains . We find that wild-type Pol ε molecules cannot proofread errors created by proofreading-defective Pol ε molecules , whereas Pol δ can not only proofread errors created by proofreading-defective Pol δ molecules , but can also proofread errors created by Pol ε-defective molecules . These results suggest that any interruption in DNA synthesis on the leading strand is likely to result in completion by Pol δ and also explain the higher mutation rates observed in Pol δ-proofreading mutants compared to Pol ε-proofreading defective mutants . For strains reverting via AT→GC , TA→GC , CG→AT , and GC→AT mutations , we find in addition a strong effect of gene orientation on mutation rate in proofreading-defective strains and demonstrate that much of this orientation dependence is due to differential efficiencies of mispair elongation . We also find that a 3′-terminal 8 oxoG , unlike a 3′-terminal G , is efficiently extended opposite an A and is not subject to proofreading . Proofreading mutations have been shown to result in tumor formation in both mice and humans; the results presented here can help explain the properties exhibited by those proofreading mutants .
Unlike prokaryotes , eukaryotic cells have multiple DNA polymerases involved in chromosomal replication . It was first demonstrated in Saccharomyces cerevisiae [1] and then in human cells [2] that Pol α , Pol δ , and Pol ε were necessary for normal replication . It was subsequently found that two of these polymerases , Pol δ and Pol ε , had 3′ to 5′ exonuclease proofreading activities that could be inactivated to yield proofreading defective enzymes [3–5] . The Pol α-primase complex initiates DNA replication with short RNA primers followed by limited elongation by Pol α; this initiation takes place for each Okazaki fragment and is likely the case for initial initiation of the leading strand as well [6] . Using the two proofreading mutants and analysis of various mutational spectra , it was proposed that leading and lagging strands of replication were each replicated primarily by only one of the two polymerases , Pol δ and Pol ε , [7–9] . At that point , it was not possible to determine which of the polymerases was responsible for each of the replication strands . The use of mutations in each of the DNA polymerases that decrease their fidelity has proven very useful in analyzing their roles in replication . It was suggested that Pol δ , but not Pol ε , could proofread errors created by Pol α [10] , supporting a model in which lagging strand synthesis was performed by Pol δ . Mutator alleles of Pol ε were consistent with its role in leading strand synthesis [11] , and mutator alleles of Pol δ showed its activity in lagging strand synthesis [12] . A later genome-wide analysis using a Pol δ mutator allele again demonstrated that most Pol δ errors were on the lagging strand [13] . Therefore a current model of replication in yeast is that the lagging strand is replicated by Pol δ and the leading strand by Pol ε . The fact that a similar differentiation is observed in the very distantly related yeast Schizosaccharomyces pombe has led to the suggestion that this model is likely true for at least most eukaryotes [14] . One major issue in understanding yeast DNA replication has been the extent to which the leading strand is replicated only by Pol ε . It was found that the catalytic activity of Pol ε is not essential [15] , demonstrating that Pol δ is in some cases able to replicate both strands . In addition , it has been consistently found that proofreading defective alleles of Pol ε have a much weaker mutator phenotype than do proofreading defective alleles of Pol δ [6–8 , 16–18] . Such results have led to proposals that Pol δ could replicate the leading strand under conditions of dysfunction [19] or could be part of an alternative fork formed after stalling on the leading strand [20] . However , the most comprehensive model of Pol δ involvement in leading strand replication was proposed by Pavlov and Shcherbakova based on an extensive survey of the literature and some of their unpublished work [6] . Their model also has initial synthesis of the leading strand by Pol ε and the lagging strand by Pol δ . They envision a variety of different possibilities for an interruption on the leading strand , including incorporation of an incorrect nucleotide that would be difficult to extend , a lesion on the leading strand , collision with RNA polymerase , or spontaneous dissociation of Pol ε . In any of those cases , they propose that reinitiation would be done by Pol δ and not Pol ε [6] . In addition to proofreading , an extremely important system for maintaining fidelity of replication is the mismatch repair system ( MMR ) . The mismatches that result from incorporation of mispaired bases are recognized in eukaryotes by homologues of the bacterial protein MutS , generally MutSα , a heterodimer of Msh2 and Msh6 , and MutSβ , a heterodimer of Msh2 and Msh3 [21–23] . Base-base mismatches are recognized almost entirely by MutSα , although there is evidence for recognition of some base-base mispairs by MutSβ [24] . Insertion and deletion mismatches are recognized by both MutSα and MutSβ , with small loops preferentially recognized by MutSα and larger loops preferentially recognized by MutSβ [21–23] with an additional preference of MutSα for repair of insertion loops and repair of deletion loops by MutSβ [25] . Recognition by MutS homologues is followed by interaction with homologues of MutL , usually MutLα in eukaryotes [21–23] . The newly replicated DNA is excised , followed by resynthesis . The method of MMR strand discrimination is still not completely known , but is likely a result of the endonuclease activity of the MutL homologues and interaction of MMR proteins with PCNA [26–28] as well as the incorporation of ribonucleotides into the newly synthesized strand [29 , 30] . Given the role of proofreading in eliminating mispairs and the role of MMR in repair of mismatches , one might predict that the two systems would function in the same pathway and would exhibit synergistic interactions , and that is what has been observed [3 , 6 , 7 , 17 , 18] . Although haploid strains of S . cerevisiae defective either in MMR or Pol δ proofreading grow well and are viable , the double mutant is not viable , whereas the corresponding double mutant with a Pol ε proofreading defect is viable [3] . This latter result seems to indicate that Pol ε proofreading plays a lesser role in replication than does Pol δ proofreading . In addition to proofreading and MMR , a major determinant of replication fidelity is the accuracy of the polymerase itself and its ability to extend mispaired nucleotides [31] . DNA polymerases can vary substantially in their fidelity; both Pol δ and Pol ε are relatively accurate even in the absence of proofreading [32 , 33] . If a mispair is formed and not corrected by proofreading , there is a wide variability in how well various mispairs can be extended—as measured , for example , in vitro with Taq DNA polymerase or E . coli Pol I Klenow fragment [34 , 35] . The difference in extension efficiency can be explained at least in part by the structure of the mispairs [36] . It has proven difficult to study proofreading in vivo . Due to the strong synergism with MMR , any measurement of proofreading in the presence of MMR reveals only those mispairs that escaped MMR . However , proofreading defects coupled with an absence of MMR give mutation rates so high that the resulting strains are sick , even as diploids . The situation is even more complex if one is interested in analyzing the role of each replicative polymerase , as the direction of replication of a given assay region is frequently not known , and thus the mispair leading to mutation is indeterminate . In this work , we make use of a collection of trp5 mutants , each of which can revert to wild type via only one given base pair change [37] . Those trp5 alleles are placed in a region with a dependable origin of replication so that for each strain it is known which strand is replicated on the leading strand and which on the lagging strand [37] . In order to examine proofreading in the absence of MMR , we use diploid strains that are hemizygous for the trp5 mutations . Our results are consistent with replication of the lagging strand by Pol δ and initial replication of the leading strand by Pol ε . However , we find that Pol δ can proofread errors on both leading and lagging strands , including errors created by other proofreading-defective Pol δ molecules , whereas a Pol ε molecule is only able to proofread its own errors . It has been difficult to reconcile the much greater mutator phenotype of Pol δ-proofreading deficient strains compared to Pol ε-proofreading deficient strains with a model of replication in which each of the DNA polymerases is primarily responsible for the replication of one strand of DNA . Our results explain this discrepancy by demonstrating Pol δ proofreading of Pol ε errors on the leading strand . Our finding that reversion rates of diploid strains heterozygous for either proofreading deficiency are similar is consistent with reports that Pol ε-proofreading deficient human cells have a disposition toward tumor formation at least as strong as that of Pol δ-proofreading deficient cells . We find large differences in extension efficiencies of various base-base mismatches and additionally find that an 8-oxoG-A mismatch is extended very efficiently and is not subject to proofreading . The demonstration of greatly varying mismatch extension biases in vivo can potentially help explain the striking differences in tumor spectra between mammalian cells deficient in Pol δ proofreading compared to Pol ε .
We first measured the rates of spontaneous reversion of the hemizygous trp5-G148T allele to the Trp+ phenotype in various genetic backgrounds . In general , the single-mutant reversion rates were not distinguishable from each other or from wild-type , in part due to the very low reversion rates and correspondingly large Confidence Intervals ( Fig . 2A and S1 Table ) . It has been known for many years that defects in proofreading and MMR were synergistic [3 , 7] , and the reversion rates of strains deficient in both MMR and one proofreading activity ( Fig . 2A and S1 Table ) strongly demonstrate that fact . With one exception ( the trp5-G148T msh6 pol2–4 R strain ) the double-mutant reversion rates were one to two orders of magnitude higher than any of the single-mutant reversion rates . Although defects in Pol ε proofreading ( pol2–4 ) generally result in a lower mutation rate than defects in Pol δ ( pol3–01 or pol3–5DV ) [7 , 43] , we observed that in msh6 strains with the F orientation , the mutation rates were approximately the same ( compare msh6 pol2–4 F with msh6 pol3–5DV F in Fig . 2A ) , whereas they were vastly different in msh6 strains with the R orientation . Based on previously reported results , we would have expected that the reversion rates for the msh6 pol3–5DV strains to have been much higher than the msh6 pol2–4 strains in either orientation . Because of the unexpected differences in reversion rates of the double mutants , we repeated the experiments in a second set of diploid strains , containing the hemizygous trp5-A149C allele ( Fig . 2B and S2 Table ) . Reversions of the trp5-A149C allele occur via the same mismatches as for the above trp5-G148T allele , but the bases on the primer and template strands are switched . The pattern of reversion rates in the trp5-A149C strains was very similar to that of the trp5-G148T strains ( compare Fig . 2A to Fig . 2B ) with wild-type or single-mutant strains having low and generally similar reversion rates and double-mutant strains having much higher reversion rates . Thus in two independent sets of strains , we observed striking effects of the orientation of the TRP5 marker gene on reversion rates due to proofreading defects; for pol2–4 msh6 strains , reversion rates were much higher in the F orientation and for pol3–5DV msh6 strains reversion rates were much higher in the R orientation . The effect of TRP5 orientation on reversion rates of the double mutant strains was both striking and unexpected . These results were uncovered due to the novelty of this assay; previous assays for spontaneous mutations in proofreading mutants have used either forward mutation rates in genes such as CAN1 or URA3 that give many different types of mutations [3 , 7 , 8 , 10 , 16 , 18 , 40 , 41 , 43 , 45–50] or reversion assays that usually involve slippage in simple repeats to give frameshifts in the his7–2 or hom3–10 alleles or various alleles of LYS2 [3 , 7 , 10 , 16–18 , 41 , 43 , 45–49 , 51 , 52] . Unless the replication direction of a marker gene is known , there is little information to be gained by measuring mutation frequencies of an inverted copy of the gene . With only two exceptions , none of the mutation assays referred to above examined orientation of the marker gene for spontaneous mutation . Those two exceptions were analysis of mutation spectra in a URA3 gene in both orientations near a defined origin of replication in the chromosome [7] and of the SUP4-o gene in both orientations on a yeast plasmid [8] . In both cases , the mutational spectra were different in the two orientations , which was used as an argument that the two polymerases replicated different strands , but there was no analysis of any differences in mutation rates in the two orientations [7 , 8] . As discussed above , assuming that the leading strand was replicated by Pol ε and the lagging strand by Pol δ [11 , 12] , it is possible to infer which mispair was increased in each strain . For example , in the trp5-G148T strain in the F orientation , a defect in Pol2 proofreading should increase the number of T-C mispairs , but should have no effect on the number of A-G mispairs ( see Fig . 1B ) . For each double mutant strain in Fig . 2 , we could associate a reversion rate with the particular mispair that should have led to the reversion event as shown in Fig . 3 . We then did two comparisons . For a given mispair , we compared the reversion rate for that mispair in a pol3–5DV strain to the same mispair in a pol2–4 strain . For example , the trp5-G148T pol3–5DV F reversion rate ( presumably occurring via A-G mispairs ) is 29-fold greater than the trp5-G148T pol2–4 R reversion rate ( also dominated by A-G mispairs ) ( Fig . 3 ) . These comparisons show that for the same mispair , in MMR-deficient strains , the reversion rate of the pol3–5DV strain is greater than the reversion rate in a pol2–4 strain , consistent with previous results showing a greater mutator effect in Pol δ proofreading-defective strains compared to Pol ε proofreading-deficient strains . We next compared the reversion rates due to one mispair compared to the other mispair in strains with the same proofreading defect . For example , the reversion rate of a trp5-G148T F pol2–4 strain ( with increased T-C mispairs ) is 17-fold greater than the reversion rate of the trp5-G148T R pol2–4 strain ( with increased A-G mispairs ) ( Fig . 3 ) . In every case , the reversion rate due to increased T-C or C-T mispairs was greater than that due to increased A-G or G-A mispairs . That result suggested that either T-C mispairs were more readily formed than A-G mispairs , or that they were more easily extended , and thus more susceptible to proofreading , than A-G mispairs . This analysis helps explain the difference in mutational spectra observed in strains defective in either Pol δ or Pol ε proofreading: for at least certain mismatches , the frequency of forming and extending one mismatch is much greater than forming and extending the complementary mismatch . However , these experiments cannot distinguish whether the difference is due to likelihood of formation of a given mispair or the relative efficiency of extending a given mispair . Comparing the effect of a Pol δ proofreading defect to a Pol ε proofreading defect for the same mispair revealed that the reversion rate for pol3–5DV msh6 mutants was 19- to 29-fold higher than for pol2–4 msh6 mutants in trp5-G148T strains and approximately 2- to 7-fold higher in trp5-A149C strains ( Fig . 3 ) , which might suggest that Pol δ was less accurate than Pol ε in the absence of proofreading . However , an analysis of in vitro activity suggests that is not the case and that the accuracy of the proofreading defective enzymes is similar , with that of Pol ε being slightly less in certain instances [33] . If the inherent accuracies of Pol δ and Pol ε without proofreading activity are similar , an alternative explanation for the much higher reversion rates of pol3–5DV msh6 strains compared to pol2–4 msh6 strains would be that Pol δ could proofread Pol ε errors , but not vice-versa . An initial step was to ask whether a wild-type polymerase molecule was able to proofread errors created by a proofreading-defective molecule of the same type . We answered this question by constructing diploid strains deficient in MMR , but only heterozygous for proofreading defects ( msh6/msh6 pol2–4/POL2 abbreviated pol2–4± or msh6/msh6 pol3–5DV/POL3 , abbreviated pol3-5DV± ) . In heterozygous strains , we would expect half of the polymerase molecules to be mutant and half to be wild-type . If errors produced by the mutant polymerase were not subject to subsequent proofreading by wild-type polymerases in the heterozygous strains , we would expect the reversion rate of the heterozygous strains to be one half that of homozygous proofreading defective strains . The results are shown in Fig . 4 and S3 Table and compared in Table 1 . The difference between pol2–4± and pol3-5DV± strains is striking . For Pol δ , the difference between heterozygous and homozygous strains ranged from 12- to 46-fold ( Table 1 ) . This result strongly suggests that wild-type Pol δ molecules could proofread the errors created by the proofreading-defective Pol δ molecules , as the presence of one wild-type POL3 allele reduces reversion rates by over an order of magnitude . This conclusion is also consistent with the suggestion that Pol δ can proofread Pol α errors [10] . Proofreading of Pol α errors by Pol δ would imply that DNA strands created by Pol α , but with some error of replication , are subject to proofreading by Pol δ , as well as extension by Pol δ . Similarly , cis-proofreading of Pol δ errors by Pol δ would imply that a DNA strand synthesized by Pol δ but containing errors in replication would be subject to proofreading by Pol δ , as well as extension by Pol δ . Strains that were heterozygous or homozygous for Pol ε proofreading ( msh6/msh6 pol2–4/POL2 or msh6/msh6 pol2–4/pol2–4 ) were very similar in reversion rates . The increase between strains that are heterozygous in Pol ε proofreading ( pol2–4± ) and those that are homozygous was approximately two-fold for each strain ( Table 1 ) . One issue is whether that difference is statistically significant . Although it is standard to assume that 95% Confidence Intervals for two measurements should not overlap in order for the difference to be significant , in fact 95% Confidence Intervals that do not overlap are significant at the P = 0 . 005 level and it is 83% Confidence Intervals that are significant at the P = 0 . 05 level if they do not overlap [53] . Consequently , 83% Confidence Intervals were calculated for the various strains defective in Pol ε proofreading . Those results are given in S3 Table and show that three of the four comparisons in Table 1 are significantly different . A recent measurement of CAN1 mutation rates in diploids that were pol2–4/pol2–4 or pol2–4/POL2 also found a 2-fold difference [49] . A 2-fold difference is what would be expected if a given region were replicated either by a proofreading-defective or proofreading-competent molecule and there was no compensating effect of the wild-type molecules on the proofreading-defective molecules . If the reversion rates of the pol2–4 and pol2–4± strains were considered to be not significantly different , the implication would be that the pol2–4 allele was dominant over the wild-type allele . In either case , there is no evidence of any cis-proofreading by Pol ε . We have a set of 12 trp5 haploid strains , with 6 different alleles of TRP5 , each in both orientations relative to the ARS306 origin of replication [37] . An analysis of the remaining trp5 strains defective in MMR and heterozygous in one of the proofreading mutants was performed and the results shown in Fig . 5 with the data given in S4 Table . The trp5-G148A and trp5-A149G alleles are in some ways analogous to the trp5-G148T and trp5-A149C alleles as they also revert via complementary mutations , AT→GC and GC→AT respectively ( Fig . 6 ) . However , unlike the situation with the trp5-G148T and trp5-A149C alleles , the complementary mispairs in the trp5-A149C strain are in the strain of opposite orientation compared to the mispairs in the trp5-G148A strain ( compare Fig . 3 and 6 ) . With both sets of strains , there are orientation biases in reversion rates and the biases are opposite in msh6 pol2-4± strains compared to msh6 pol3-5DV± strains ( Fig . 5 ) . The reversion rates for these strains were analyzed in Fig . 6 in a manner similar to that shown in Fig . 3 for the trp5-G148T and trp5-A149C strains . In contrast to the strains with homozygous proofreading deficiencies in Fig . 3 , the relative reversion rates for a given mispair in pol3-5DV± compared to pol2–4± strains are much more similar , with the reversion rates in some pol2–4± strains being higher than for the equivalent mispair in pol3-5DV± strains ( Fig . 6 ) . In every case the loss of proofreading for a T-G mispair causes a higher reversion rate than the loss of proofreading for an A-C mispair . Thus it appears that either T-G mispairs are formed at a higher rate than A-C mispairs , or they are more easily extended . The situation with the trp5-G148C and trp5-A149T strains is quite different . With those strains , there is a very low reversion rate even in the absence of MMR and a partial proofreading defect ( Fig . 5 ) . In all of those strains , reversion is due to the mispairing of identical bases: G-G or C-C and A-A or T-T respectively . Transformation of cells by single-stranded oligonucleotides ( oligos ) in which a permanent change is made to either chromosomal or plasmid DNA by introduction of oligos into the cell has been studied extensively in three systems: E . coli , mammalian cells , and yeast . In E . coli , numerous experiments from multiple labs support a mechanism in which oligos anneal to single-stranded DNA at the replication fork and serve as primers for continued replication , with oligos annealing to the lagging strand being considerably more efficient than when annealing to the leading strand of replication [54–62] . Mechanistic studies of oligo transformation in mammalian cells are more difficult than in E . coli . However , multiple labs have shown that oligo transformation is associated with cellular replication [63–65] , that it is more efficient in S phase [66 , 67] , that the oligo is incorporated into the genome , likely during replication [68] , and that evidence suggests that the transforming oligos do so by serving as primers for replication [69–71] . Transformation in both E . coli and mammalian cells is inhibited by MMR , in agreement with the association of oligo transformation and replication [56–58 , 60 , 61 , 63 , 69 , 72–81] . In yeast , we have shown that oligos transform more efficiently when directed to the lagging strand [25 , 39 , 82–84] , that transformation is inhibited by MMR by removing oligo sequences creating MMR-recognized mispairs [25 , 39 , 82–84] , that oligos transform by incorporation [83] , and that the 5’ end of transforming oligos is usually removed by a process partially dependent on Rad27 , suggesting removal as part of Okazaki-like processing [84] . We also showed that in normally growing cells , MMR specifically removes oligo sequences that are part of mispairs , but that if oligo sequences escape MMR recognition and survive past S phase , MMR no longer can distinguish between the oligo and chromosomal sequences [83] . There remain two questions about oligo transformation in yeast: how oligo-directed replication could occur on the leading strand and whether transformation might generally occur in single-stranded gaps remaining in the G2 cell cycle phase . Work from the Marians lab has shown in vitro in E . coli that there can be “lesion skipping” on the leading strand that can result in repriming of replication [85 , 86] . Many years ago , it was found in UV-irradiated yeast cells that on both the leading and lagging strands short single-stranded gaps were observed that were proposed to be the result of repriming events [87] . Proposals that Pol δ could replicate the leading strand under conditions of dysfunction [19] or could be part of an alternative fork formed after stalling on the leading strand [20] would also suggest some type of repriming event on what was the leading strand . A very recent study of in vitro yeast replication showed that Pol ε is tightly associated with the CMG helicase during leading strand synthesis but that it can periodically cycle on and off PCNA-DNA [88] . An analysis of that work suggested that such cycling could provide access to a mismatched primer for extrinsic proofreading [89] . An oligo bound to the leading strand might appear much like a normal replicative end exposed by a cycling off of the Pol ε-CMG complex . Although we cannot rule out the possibility of transformation occurring in single-stranded gaps left in G2 , we consider that possibility unlikely as a general mechanism . Our cells are undamaged and growing in rich medium before transformation . It seems unlikely that there would be sufficient single-stranded gaps in the particular region to be transformed to account for the high transformation frequencies we have observed in some cases [39] . It is also not clear why in G2 there would be five-fold or more single-stranded gaps on what used to be the lagging strand compared to what used to be the leading strand of replication . The very active involvement of MMR and of Rad27 also seem more compatible with a process occurring during replication rather than post-replicationally . Therefore we consider it likely that in yeast , as appears to be the case in E . coli and mammalian cells , oligos transform by annealing to a single-stranded region at the replication fork , with a strong preference for lagging strand , and then serve as pseudo-Okazaki primers for replication . If oligos can serve as primers for replication , it might be possible to transform strains with oligos that create a mispair necessary for reversion at their very 3′ end as indicated in Fig . 7 assuming the mispair was extended rather than being proofread . We tested this hypothesis by transformation using Oligo 148C with a 3′ C that would create a T-C mispair necessary for reversion of the trp5-G148T allele ( Fig . 7 ) . The results are given in Table 2 . Because the transformation results in the table are relative to transformation with an oligo creating a mispair internal to the oligo , low transformation in these experiments indicates either the removal of the 3′ terminal mispair necessary for reversion of the strain , or failure to extend the mispair . When Oligo 148C was transformed into strains in the R orientation , which would put the oligo on the lagging strand , we obtained a relatively low number of transformants in an msh6 strain . That number did not increase in pol2–4 strains , but increased about 6-fold in the msh6 pol3-5DV± strain and about 30-fold in the msh6 pol3–5DV strain . In strains with the F orientation , transformation of the msh6 strain is even lower , as the oligo would anneal to the leading strand . There is little if any increase in the msh6 pol2–4 strain or the msh6 pol3-5DV± strain but a large increase ( ~30-fold ) in the msh6 pol3–5DV strain . When we attempted to perform the same experiment with Oligo 148G , creating a G-A mispair on the opposite strand from Oligo148C , we obtained essentially no revertants in any background . We performed the same type of oligo transformation experiment in trp5-A149C strains , using Oligos 149A and 149T . As illustrated in Fig . 7 , these oligos produce the same mismatches for extension as in the trp5-G148T strains , but with the opposite base as primer in the mispair . The results of these experiments are given in Table 2 . As in the trp5-G148T strains , there is little transformation of msh6 or msh6 pol2–4 strains . In contrast to the results with Oligo 148G , there is measurable transformation of Oligo 149A in msh6 pol3–5DV± strains and substantial transformation in msh6 pol3–5DV strains , even when directed to the leading strand . It thus appears , at least in this sequence context , that extension of an A in a G-A mispair is much more likely than the G in an adjacent G-A mispair . Oligo 149T gives robust transformation in msh6 pol3–5DV strains , similar to transformation with Oligo 148C . These experiments help make several important points . None of the oligos ( Oligo 148C , Oligo 148G , Oligo 149A , or Oligo 149T ) showed much transformation in msh6 cells , but 3 of the oligos ( all except Oligo 148G ) showed significant transformation in msh6 pol3–5DV cells , when targeted to either the lagging or leading strand . Those results demonstrate that the lack of transformation in msh6 cells is due to proofreading of the 3′ terminal mismatch by Pol δ , as elimination of Pol δ proofreading is sufficient to enable robust transformation by the oligos . In addition , the effect of Pol δ proofreading is observed , whether the oligos are targeted to the lagging or leading strand . Because incorporation of the mismatch created by the 3′ terminal base of the oligos is necessary for transformation , these results also strongly suggest that the oligos must be serving as primers for continued DNA synthesis for it is difficult to propose another mechanism that could explain oligo transformation with a 3′-terminal mismatch . There is marked variability in the efficiency by which the oligos are able to transform . Oligo 148G gave essentially no transformants in any strain ( although the same oligo with a modified G at the end gave robust transformation , see below ) . Oligos 148C and 149T gave the highest levels of transformation , while Oligo 149A gave markedly lower levels of transformation . These relative transformation efficiencies are similar to the differences in reversion rates seen in the trp5-G148T and trp5-A149C strains ( Figs . 1 and 2; S1 and S2 Tables ) . In those double mutant strains , the lowest reversion rates were due to G-A mispairs in which G was on the primer strand; reversion rates due to A-G mispairs with the A on the primer strand were also very low . In all cases , reversion rates due to T-C or C-T mismatches were much higher . In our analysis of the orientation effects on reversion rates , we were not able to discriminate between reversion rate effects due to different frequencies of formation of certain mispairs or differences in elongation frequencies ( see above ) . However , these results with oligo transformation suggest that the biased reversion rates are due at least in part to differential frequencies in elongation of various mispairs . Our oligo results show that at least in this sequence context a G paired opposite an A is very rarely elongated so that no matter how frequently such a mispair might be formed , it would rarely be extended . These results also are in line with in vitro mismatch extension experiments ( see Discussion ) . In contrast to the effects of Pol δ proofreading on oligo transformation , we observed no effect of elimination of Pol ε proofreading , whether oligos were targeted to either the leading or lagging strand . These results indicate that , no matter what mechanism is responsible for oligo transformation , it is Pol δ alone that interacts with , and elongates , the oligo . If one accepts a model in which oligos transform by priming at the replication fork , these results would suggest that any replication restart due to oligo priming on the leading strand would be extended by Pol δ and not Pol ε , in line with a model of replication restart on the leading strand being due to Pol δ [6] . We also examined oligo transformation in strains heterozygous for Pol δ proofreading ( msh6/msh6 pol3–5DV/POL3 or msh6 pol3-5DV± ) . As can be seen in Table 2 , in most cases the difference between transformation in msh6 pol3–5DV strains was significantly greater than in msh6 pol3-5DV± strains , and in 3 cases was 20–50 fold greater . Those differences between pol3-5DV± and pol3–5DV strains are similar to the differences in reversion rates shown in Table 1 and are consistent with cis-proofreading by wild-type Pol δ . These oligo transformation experiments can also help explain how one could understand proofreading by wild-type Pol δ of errors made by proofreading-defective Pol δ molecules . When a proofreading-defective Pol δ molecule inserts a mispaired base , presumably there is some frequency at which the polymerase will extend the mispair; when that happens , the mispaired base is no longer susceptible to proofreading . Frequently , one would assume that the mispaired base would stall the polymerase synthesis and in the absence of the ability to proofread , might cause a release of the polymerase , exposing the mispaired base to other exonucleases in the cell . A wild-type Pol δ molecule could bind to the primer-DNA substrate and either extend , or more likely proofread , the mispair . A proofreading-defective Pol δ molecule could interact with the substrate , either extending the mispair , or disassociating . The oligos with 3′ mispairs mimic a dissociated primer-DNA complex . In pol3-5DV± strains , if a proofreading-defective Pol δ molecule would usually extend the mispair when it interacted with the 3′ mispair , one would expect that the difference in extension frequencies between pol3-5DV± and pol3–5DV strains would be 2-fold . The fact that it is much greater suggests that many of the proofreading-defective polymerase interactions are not productive , allowing more chances for the mispair to be proofread by the wild-type Pol δ . This same scenario in vivo could explain how mispairs could be cis-proofread and also why there is such a large difference in reversion rates in pol3-5DV± and pol3–5DV strains . It is known that incorporation of oxidatively damaged nucleotides can lead to mutations [90] and that MMR can recognize 8-oxoG-A mispairs [83 , 91–93] . It is not known to what extent an 8-oxoG-A mispair due to incorporated 8-oxoGTP might be subject to proofreading . We therefore used Oligo 148oxoG that creates an oxoG-A mispair at the 3′ end of the oligo in the trp5-G148T strains . The results of those transformations are given in Table 2 . The results with Oligo 148oxoG are very different from those observed with any of the other oligos . There are a substantial number of transformants in msh6 strains of both orientations . However , there is not a significantly greater number of revertants in any proofreading-defective strain , suggesting that the oxoG-A mispair is not subject to proofreading . Because the Oligo 148oxoG has exactly the same sequence as the Oligo 148G except for the modified 3′ terminal base , these results support the conclusion that the extremely low numbers of Oligo 148G transformants are due to failure to extend the G-A mispair and not due to low formation of the G-A mispair . There is also no difference in Oligo 148oxoG transformants in msh6 pol3-5DV± compared to msh6 pol3–5DV strains in contrast to the differences in those strains observed with the other oligos . Those results suggest that there is no inherent defect in elongation ability of the proofreading defective Pol δ enzyme .
Some of the earliest work on proofreading and MMR in yeast found a multiplicative relationship between mutants defective in proofreading and MMR and those results were interpreted as demonstrating serial action of proofreading and MMR [3 , 7 , 94] . Our single-mutant rates , given in Fig . 2 and S1 and S2 Tables , are so low and have such large Confidence Intervals that they cannot be used in such calculations , but the double mutant rates are sufficiently high that they suggest synergism and not multiplicativity and thus seem at odds with the previous results . The two assays used by Morrison and Sugino [3 , 7 , 94] were forward mutation rate measurements in URA3 and reversion of the his7–2 frameshift allele . In both of those assays , the wild-type mutation rate was much higher than in our assay , and the mutation rate in the absence of MMR was increased by , in one of their haploid analyses , 41-fold in the URA3 assay and 150-fold in the his7–2 assay ( Table 1 in [7] ) . It is thus very likely that the underlying mutation rates observed in those assays reflected errors not due to DNA polymerase proofreading defects . In contrast with the previous assays , we know in our trp5 reversion assay not only what base pair mutation is made , but in the case of proofreading mutants what particular base-base mispair led to the reversion event . In the case of the single proofreading mutants , we know that a proofreading error leading to a reversion event and thus creating a base-base mispair should be corrected by MMR , and the slight increase in reversion rate is almost certainly due to random escape from MMR , as no repair system will be 100% efficient . The amount of escape would presumably depend in part on the particular mispair and sequence context , as MMR repair of base-base mispairs is sequence dependent [95] . The small increase in reversion rates observed in the msh6 mutants is somewhat more complex . It is likely that some of the reversion events are due not to a failure in proofreading but rather to mispairs that results from damaged DNA , as we have previously demonstrated [96 , 97] . What appears to be a higher mutation rate in the msh6 A149C strain compared to the msh6 G148T strain , for example , could be due to mispairs involving an 8-oxoG and not due to proofreading errors . Such errors should not be increased in strains that would be defective in proofreading . Therefore in any proofreading-defective MMR-defective double mutant , we would expect to see a large increase in reversion rate due to the failure of MMR to repair proofreading errors , and that is what is observed . In the absence of MMR , the probability of a base pair mutation is a function of the probabilities of misinsertion of a base , its removal by proofreading , and elongation from the mismatched base pair . As noted above , we found a very strong orientation dependence in MMR-deficient , proofreading-defective , strains with four of the six different trp5 mutations ( trp5-G148T , trp5-A149C , trp5-G148A , and trp5-A149G; Figs . 2 and 5 ) . However , our reversion data did not allow us to discriminate between mispairs that are formed at a high rate and mispairs that are easily extended . For example , a mispair that was easily formed , but very poorly extended , would likely contribute little to the overall reversion rate . Our oligo transformation experiments , however , allow us to analyze efficiencies of mispair elongation . Using oligos that formed terminal A-G or C-T mispairs , we found that transformation efficiencies in msh6 pol3–5DV strains were quite variable depending on the particular mispair and that the variability correlated with the variability in reversion rates in msh6 pol3–5DV strains . Thus our results with oligos containing 3′ mismatches suggest that at least part of the reason for orientation bias in reversion rates was due to differential extension rates from various mispairs . Although as discussed above our oligo results are likely to reflect extension only by Pol δ , the fact that we see similar orientation bias with Pol ε proofreading mutants ( Figs . 2 and 5 ) , strongly suggests that Pol ε has similar elongation biases . Our oligo experiments studied only a subset of possible base mispairs—those for which we had reversion data in homozygously-deficient proofreading strains . As shown in Fig . 5 , we found evidence using heterozygously-deficient proofreading strains that other mispairs also showed biased orientation effects . We think it likely that those biases could also be explained by differential mispair elongation efficiencies . It has been difficult to devise experiments that would measure mispair extension within the context of a chromosome , as both proofreading and MMR are very effective at eliminating extensions of mispaired bases . However , there have been some in vitro measurements of mispair extension . Even those measurements are complicated by the demonstrated sequence effects on mispair extension [35] and the necessity to use DNA polymerases devoid of proofreading activity . For the E . coli exonuclease-deficient Klenow fragment of Polymerase I , it was found with two exceptions that in each sequence context , extension of mispairs with identical base pairs was the least favored of all combinations [35] , in line with the low reversion rates of the trp5-G148C and trp5-A149T strains observed in Fig . 5 . The two mispairs that were least favored of all were extension of G opposite template A and extension of A opposite template G [35] , again agreeing with the failure of Oligo 148G to transform trp5-G148T strains and the relative low transformation of trp5-A149C strains with Oligo 149A ( Table 2 ) . A similar pattern of mispair extension was observed with Taq DNA polymerase [34] and AMV reverse transcriptase and Drosophila Pol α [98] . In these publications , the mispair most efficiently extended was primer G against template T [35 , 98] , and of all of our heterozygous reversion rates , two of the three highest were mispairs in which primer G against template T would have been on the strand with a heterozygous proofreading deficiency ( trp5-G148A F msh6 pol3-5DV± and trp5-G148A R msh6 pol2–4± , Fig . 5 ) . Thus the existing in vitro data show clear differences for DNA polymerase extension of different 3′ terminal mispairs . Our in vivo results , including both reversion rates of different trp5 mutants and our oligo transformation experiments , show biases that are consistent with the in vitro data demonstrating differential extension efficiencies of various mispairs . In strains proficient in proofreading , these differential extension frequencies are unlikely to be evident; however , in proofreading-defective strains , mispair extension bias is likely to be much more important , and underappreciated . Several studies have shown that the mutation spectra of Pol ε and Pol δ proofreading deficient strains differ [7 , 8 , 17]; one would expect that mispairs less likely to be extended would be most susceptible to trans-proofreading . Therefore we propose that differential mispair extension frequencies can explain not only the biased reversion rates we have found , but , more generally , the differences in mutation spectra observed in strains deficient in Pol δ compared to Pol ε proofreading . Many base pair mutations are likely due to mispairings involving a damaged base . Indeed , we speculate that the higher spontaneous mutation rates in the trp5-A149C wild type and msh6 strains compared to the equivalent trp5-G149T strains ( Fig . 1 ) is due to oxidative damage of the template G in the trp5-A149C strains; we have shown that increased endogenous oxidative damage leads to greatly increased reversion rates of this mutation [96] . It is also known that incorporation of oxidized nucleotides represents a mutagenic threat to organisms [90 , 99] and we have previously shown that yeast can use exogenously added 8-oxoGTP and mutagenically insert it into the genome [83] . We also showed that MMR greatly inhibits the incorporation of 8-oxoGTP into the chromosome [83] . However , it has not been clear how well an 8-oxoG-A mispair could be proofread . Our interest in using Oligo 148oxoG for transformation is that it mimics the incorporation of 8-oxoGTP into the DNA and thus allows analysis of processes acting on the 8-oxoG-A mispair . In contrast to the lack of transformants with Oligo 148G , we obtained large numbers of transformants using Oligo 148oxoG , containing an 8-oxoG at the 3′ end rather than a G . As seen in Table 2 , we find that the 8-oxoG-A mispair is essentially not recognized by proofreading , as there is a large number of transformants in msh6 strains , and the number is not increased in proofreading-defective strains . Thus not only is the 8-oxoG-A mispair extended well , in stark contrast to the lack of extension of the G-A mispair , but it is not recognized by proofreading . It now appears well established that in yeast , and likely most eukaryotes , the leading and lagging strands of replication are usually replicated by different DNA polymerases as suggested nearly two decades ago [7] and more recently demonstrated in detail [11–13] . With that understanding , it has been difficult to explain why there is a much greater increase in the mutation rates of strains with a proofreading deficiency in Pol δ compared to Pol ε [6–8 , 16–18] . That result is even more surprising given evidence that MutSα function is more efficient on the lagging strand which would be replicated by Pol δ [38] and that MMR in general appears to balance the fidelity of replication of leading and lagging strands [100] . Our analysis of reversion rates in homozygously versus heterozygously proofreading-deficient strains indicated that wild-type Pol δ polymerases could cis-proofread , whereas wild-type Pol ε polymerases could not proofread errors created by proofreading-deficient Pol ε molecules . Our oligo transformation experiments were consistent with the reversion analysis: wild-type Pol δ molecules prevented transformation by oligos , even in the presence of proofreading-defective Pol δ molecules . Elimination of all Pol ε proofreading activity made no difference in oligo transformation , even if the transforming oligos were targeted to the leading strand of replication . Although as stated above we cannot conclusively rule out that oligo transformation events might take place post-replicationally , the oligo transformation experiments are consistent with trans-proofreading by Pol δ , as there was insignificant transformation by oligos directed to the leading strand unless Pol δ proofreading was inactivated . It should be noted that an earlier analysis of Pol δ and Pol ε replication proposed that stalled leading strand replication would be continued by Pol δ [6] . How does a polymerase error become susceptible to proofreading by a different polymerase molecule ? Because of the large size of the DNA polymerase molecules , it is likely that an error would have to cause a replication stall followed by at least partial release of the polymerase before a 3′ mispair could be exposed to a different polymerase molecule . The lagging strand is discontinuously replicated so it is not surprising that Pol δ molecules could proofread errors created by proofreading-defective Pol δ molecules , particularly as there is already evidence that Pol δ can proofread Pol α errors [10] . The leading strand is normally synthesized continuously; as noted above there is recent evidence suggesting that a Pol ε-CMG helicase complex can periodically cycle on and off PCNA-DNA and thus expose a mispair to extrinsic proofreading [88] . However , there is no known mechanism in which a different Pol ε molecule could be brought in to proofread , which is consistent with our reversion analysis demonstrating lack of Pol ε cis-proofreading . Our oligo transformation experiments suggest an additional possibility: that Pol δ molecules could proofread errors on the leading strand . Given the possibility of Pol δ proofreading of Pol ε errors , we can examine our reversion data for evidence of such trans-proofreading and how extensive it might be . The increase in reversion rate in msh6 pol2–4 strains by orders of magnitude over either single mutant indicates that a large number of Pol ε errors are not subject to Pol δ proofreading . Those reversion events must be due to errors by the proofreading-defective Pol ε polymerase that did not cause polymerase dissociation but were then extended by the polymerase . For Pol δ , there is a large increase in reversion rates , not only of msh6 pol3–5DV strains compared to either single mutant , but of msh6 pol3–5DV strains compared to msh6 pol3-5DV± strains ( Table 1 ) . The reversion rate in the msh6 pol3–5DV strains is due to the error rate of the pol3–5DV enzyme . Assuming that half of the replication in the msh6 pol3-5DV± strains is done by the wild-type Pol δ and half by the proofreading defective Pol δ , we would expect the reversion rate in the msh6 pol3-5DV± strains to be one half that of the msh6 pol3–5DV strains . The fact that the difference is 12- to 46-fold indicates that more than 90% of the time , a polymerase error results in a polymerase dissociation event that allows proofreading by a wild-type Pol δ enzyme . We can then assume that the reversion rate in a pol3-5DV± strain is indicative of the error rate due to molecules that do not dissociate but continue replication from the mispair . The reversion rate in msh6 pol2–4 or pol2–4± strains would be a combination of errors created by Pol ε proofreading-defective molecules that did not dissociate minus errors that were created and then proofread by the wild-type Pol δ molecules . Table 3 shows a comparison of the msh6 pol2–4± reversion rates compared to those of the msh6 pol3-5DV± strains for orientations that would have the same mispaired bases for reversion . ( For example , the trp5-G148T msh6 pol2–4± F strain would be expected to revert via extension of a mismatched C opposite T on the leading strand , whereas the trp5-G148T msh6 pol2–4± R strain would be expected to revert via extension of a mismatched C opposite T on the lagging strand as illustrated in Fig . 3 . ) In these comparisons , the pol2–4± reversion rates are generally similar to the pol3-5DV± reversion rates . Given our assumption that each of these rates is due mainly to mispairs that are extended by the initiating polymerase , these results indicate that the underlying inaccuracy and tendency to extend mispairs of each polymerase is roughly similar . However , if one does the same comparison with the completely homozygous msh6 pol2–4 and msh6 pol3–5DV strains for which we have data , the reversion rate for each mispaired base configuration is much higher with the pol3–5DV mutant than the pol2–4 mutant ( Table 3 ) . As noted above , there is no reason to think that Pol ε is inherently more accurate , especially since in vitro results suggest that if anything Pol ε is slightly less accurate [33]; therefore the lower reversion rate for msh6 pol2–4 strains on identical mismatches compared to msh6 pol3–5DV suggests that there is quite substantial Pol δ proofreading of Pol ε errors . These results also indicate that when a DNA polymerase incorporates a mispair that it is unable to proofread , there is a high probability of polymerase dissociation from the template . Some results in the literature have been used to suggest that there could be functional redundancy of Pol δ and Pol ε proofreading activities so that Pol ε proofreading might , for example , be able to correct Pol δ errors . For example , a study of proofreading and MMR using a frameshift reversion assay found reversion rates of triple mutants ( pol2–4 pol3–01 msh2 ) were not higher than that of double mutants and found two hotspots in mutation spectra of pol2–4 pol3–01 double mutants that were not present in either single mutant [17] . Based on those results , the authors suggested “The presence of these hotspots only in the double mutant is consistent with the functional redundancy between the Pol δ and Pol ε exonuclease activities as deduced from mutation rate measurements . ” [17] . Comparison of mutation rates in very sick strains is quite problematic . Our double mutant strains that were only partially deficient in MMR ( msh6 ) were quite sick; the strains used in the cited experiments were completely deficient in MMR ( msh2 ) . The phenomenon of “error catastrophe” and saturation of MMR was shown in E . coli in 1996 , for example [101] and is likely observed in those results , as the measured mutation rate of a pol2–4 pol3–01 msh2 strain is actually lower than that of a pol2–4 pol3–01 strain [17] . Because of those high mutation rates one would assume that MMR would be reduced due to saturation in the pol2–4 pol3–01 strain . Therefore hotspots that would appear in the double mutant strain could be due to mispairs that were less well recognized by MMR and would be more likely to escape in the partially MMR-defective environment of the double mutant strain . In addition , it is now known that there are suppressors that can arise in mutants that are defective in MMR and proofreading [40 , 48] , which is one reason we have been careful to use multiple isolates in our experiments . A proposed functional redundancy of the proofreading exonucleases would be counter to the finding that Pol ε cannot even cis-proofread its own errors , and more importantly could not explain the much higher mutation rates that have been consistently observed in Pol δ proofreading-defective strains compared to Pol ε proofreading-defective strains . Our results demonstrating trans-proofreading by Pol δ are also consistent with experiments that show that MMR appears to use Pol δ for resynthesis of DNA on either replication strand after mismatch excision [18 , 22 , 23 , 41 , 102–105] . In MMR , extension of the newly excised primer strand would be analogous to extension of one of our oligos . The disassociation of the polymerase from certain mispairs could also explain the replication checkpoint activation seen in Pol δ proofreading defective strains [46]; once a poorly-extended mispair is incorporated in a Pol δ proofreading defective strain , replication would be inhibited . Because of the low mutation rate of a pol2–4 mutant in their assay [46] , it was not possible to determine if there were a replication checkpoint activation due to Pol ε proofreading defective mutations , but our results would suggest that checkpoint activation would not be observed , due to proofreading of the Pol ε errors by wild-type Pol δ molecules . In order to explain experimental results that were not consistent with a model of replication in which Pol δ was responsible for all lagging strand synthesis and Pol ε was responsible for all leading strand synthesis , Pavlov and Shcherbakova proposed that lagging strand synthesis was performed by Pol δ , but that leading strand synthesis , although begun by Pol ε , was completed after any interruption by Pol δ [6] . Their model proposes that the lower mutation rate of strains lacking Pol ε proofreading relative to those lacking Pol δ proofreading can be explained by the fact that switching of leading strand synthesis to Pol δ is the rule , and “the majority of the genome replication involves copying of both DNA strands by Pol δ” [6] . That model , however , is inconsistent with recent whole genome sequence analysis of replication in yeast by fidelity mutants of Pol α , Pol δ , and Pol ε indicating that most leading strand synthesis must be done by Pol ε [106] . Our model differs from that of Pavlov and Shcherbakova in that Pol δ synthesis on the leading strand could be accompanied by proofreading of Pol ε errors , thus substantially reducing the amount of leading strand synthesis by Pol δ necessary to explain the differential mutation rates observed in proofreading-defective strains . Thus our model can explain the considerably higher mutation rate of strains deficient in Pol δ proofreading compared to Pol ε proofreading , the lower viability of such Pol δ strains , but also the observation that each polymerase is responsible for most replication of only one strand of DNA . We chose to analyze proofreading mutations in the absence of MMR due to the known synergism of proofreading mutations and MMR and also because of the very low reversion rates in our strains , even when defective in proofreading . In principle , one would expect that any errors normally corrected by proofreading would be repaired by MMR . However , the fact that defects in proofreading alone do show increased mutation rates [7] is an indication that some of the excess replication errors manage to escape MMR . Recently , mutations in Pol ε and Pol δ in human endometrial and colorectal cancers have been found that appear to be pathogenic [49 , 107–110] . Although many of the mutations appear to be in domains that would affect proofreading , one of the more common mutations appears to affect fidelity as well as proofreading [49] . In general , these mutations appear to be heterozygous , inherited dominantly although somatic mutations are also seen , present in MMR proficient tumors , and the cancer spectrum of Pol δ and Pol ε mutations appears to be different [107–109] . Proofreading mutations have been made in the Pol δ and Pol ε polymerases of mice and studied in vivo . In contrast to the results seen in human tumors mentioned above , there is no tumor phenotype of heterozygous proofreading mutations in either Pol δ or Pol ε , but a robust tumor phenotype for homozygous mutations [111 , 112] . The tumor phenotype of the two proofreading mutations was perhaps even more distinct than that observed in humans [111] . The mutation rates of proofreading defects in each polymerase was measured and the mutator phenotype of a Pol ε defect was found to be greater than that of a Pol δ defect; the mutation rate of mice with homozygous defects in both Pol δ and Pol ε proofreading was not measurably greater than that of either single defect [111] . In contrast to the tumor phenotype , there was found to be an increased mutation rate in heterozygous defects in proofreading of either Pol δ or Pol ε compared to wild-type mutation rates with the heterozygous Pol ε defect giving a larger effect than that of Pol δ [111] . The above results seen in mice and humans seem puzzling in light of previous yeast work , in which the conclusion has been that defects in Pol δ proofreading are much more mutagenic than defects in Pol ε proofreading—although those conclusions were based almost entirely on homozygous proofreading defects . However , many of those results are compatible with our findings . Most mutations due to proofreading errors , particularly those that would escape MMR , would be expected to be base pair mutations , and that in fact is what is observed in mice [111] . Oncogenic and tumor suppressor mutations due to base pair mutations would be expected to be at least somewhat sequence specific and the marked orientation biases observed in our pol2–4 and pol3–5DV strains indicate that the probability of a given base pair mutation could be strongly dependent on which polymerase was proofreading defective and the orientation of replication of that gene in a given tissue . Therefore the differences in tumor spectra are perhaps not so surprising . The relative prevalence of Pol ε mutations compared to Pol δ mutations in human tumors is one of the most striking differences compared to what would have been expected from previous yeast work . Most of the observed human proofreading mutations are heterozygous and as Table 3 indicates , even in the absence of MMR , the reversion rate of some of our trp5 strains is higher or about the same level in pol2–4± strains compared to pol3-5DV± strains . It has been found that MMR due to MutSα is more efficient on the lagging strand than the leading strand , at least in yeast [38] , which would tend to reduce even further the relative mutational bias in Pol δ mutants compared to Pol ε mutants . It is also possible that there could be some selective pressure for second site mutations to moderate the error rate of either proofreading polymerase as has been observed in yeast [40 , 48] , particularly in homozygously-deficient animals . The fact that heterozygous defects in proofreading can lead to tumors in humans , but not in mice , is similar to findings with other genes and may be reflective of the much longer lifespan of humans than mice . For example , mice homozygously-deficient in Msh6 show a strong tumor phenotype , but there is little increase in heterozygous mice [113] . In humans inheriting heterozygous MSH6 mutations , there is a significant increase in various types of tumors [114] .
The genotypes of all strains used in these experiments can be found in S5 Table . All haploid strains containing a TRP5 point mutation were derivatives of the strains previously published [37] . For creation of diploid strains that would be hemizygous for the trp5 point mutations , we used a haploid strain of opposite mating type , BY4741 [115] that shared parentage with our strains , but contained complementary markers . We further modified BY4741 by restoring the strain to Leu+ and making an exact deletion of the TRP5 gene by delitto perfetto [116] , creating GCY2122 ( S5 Table ) . The MSH6 gene was deleted by transformation with a PCR fragment generated from the MSH6 gene deletion described in [117] or from a strain containing an MSH6 deletion created with a loxP-kanMX-loxP fragment [118] . When a second allele of MSH6 was to be deleted in a diploid strain , a PCR fragment obtained from a strain containing an MSH6 deletion made by insertion of the loxLE-hphNT1-loxRE fragment contained in pZC3 [119] was used . pol2–4 haploid mutants were created by transformation with plasmid YIpJB1 as described [4] . pol3–5DV haploid mutants were created by transformation with EagI-digested pY19 [43] , selecting for Ura+ cells . Cells were subsequently selected for URA3 loss and screening for strains containing the pol3–5DV mutation . In order to create msh6 pol3–5DV haploid strains , pol3–5DV cells were first transformed with pBL304 , a plasmid containing POL3 on a URA3 CEN plasmid , which was constructed by Peter Burgers and is described in [7] . The MSH6 gene in such strains could subsequently be deleted with the strain maintaining viability . Diploid strains were constructed by mating of two haploid strains followed by selection on synthetic dextrose ( SD ) medium lacking methionine and leucine [120] . Diploid msh6 pol3–5DV strains were constructed by mating of the two haploids , one being MSH6 , and the other containing the POL3 plasmid pBL304 rescuing the msh6 pol3–5DV genotype , followed by deletion of the second MSH6 allele with a hphNT1 marker [119] followed by selection for loss of the POL3 plasmid . Oligos used for transformation were gel purified ( Eurofins MWG Operon ) ; the sequences are listed in S6 Table . Reversion analysis was performed as described [96] . Reversion rates and Confidence Intervals were calculated [96] using the program Salvador [121–123] . When multiple reversion experiments were done for a given genotype , the median value was used for subsequent analysis . The reversion rates of heterozygous and homozygous proofreading-deficient strains ( Table 2 ) were considered to be different if the 83% confidence levels did not overlap [53] . Oligo transformation was essentially as described previously [25 , 82–84] . An overnight culture of a strain was diluted 1:50 in YPAD [120] , incubated with shaking at 30° to an OD600 of 1 . 3–1 . 5 , washed twice with cold H2O , and once with cold 1 M sorbitol . After the final centrifugation , all solution was removed from the cells and a volume of cold 1 M sorbitol equal to that of the cell pellet added to resuspend the cells . For a typical transformation , 200 pmol of a Trp oligo was added to 200 μl of this cell suspension in a 2-mm gap electroporation cuvette , and the mixture electroporated at 1 . 55 kV , 200 Ω , and 25 μF ( BTX Harvard Apparatus ECM 630 ) . Immediately after electroporation , the cell suspension was added to 5 ml of YPAD , and the cells incubated at 30° with shaking for 2 h . Cells were then centrifuged , washed with H2O , and plated on SD medium lacking tryptophan [120] to select transformants . In order to control for some of the variability we observed in transformation efficiencies , one portion of each cell suspension was electroporated with the Trpwt40 oligo , which reverts all of the strains via a centrally-located mismatched base and is thus not subject to any proofreading effect . | Many DNA polymerases are able to proofread their errors: after incorporation of a wrong base , the resulting mispair invokes an exonuclease activity of the polymerase that removes the mispaired base and allows replication to continue . Elimination of the proofreading activity thus results in much higher mutation rates . We demonstrate that the two major replicative DNA polymerases in yeast , Pol δ and Pol ε , have different proofreading abilities . In diploid cells , Pol ε is not able to proofread errors created by other Pol ε molecules , whereas Pol δ can proofread not only errors created by other Pol δ molecules but also errors created by Pol ε molecules . We also find that mispaired bases not corrected by proofreading have much different likelihoods of being extended , depending on the particular base-base mismatch . In humans , defects in Pol δ or Pol ε proofreading can lead to cancer , and these results help explain the formation of those tumors and the finding that Pol ε mutants seem to be found as frequently , or more so , in human tumors as Pol δ mutants . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Replicative DNA Polymerase δ but Not ε Proofreads Errors in Cis and in Trans |
Taenia solium infection causes severe neurological disease in humans . Even though infection and exposure to swine cysticercosis is scattered throughout endemic villages , location of the tapeworm only explains some of the nearby infections and is not related to location of seropositive pigs . Other players might be involved in cysticercosis transmission . In this study we hypothesize that pigs that carry nematodes specific to dung beetles are associated with cysticercosis infection and/or exposure . We carried out a cross-sectional study of six villages in an endemic region in northern Peru . We euthanized all pigs ( 326 ) in the villages and performed necropsies to diagnose cysticercosis . For each pig , we counted cysticerci; measured anti-cysticercus antibodies; identified intestinal nematodes; tabulated distance to nearest human tapeworm infection; and recorded age , sex , productive stage , and geographic reference . For the purpose of this paper , we defined cysticercosis infection as the presence of at least one cysticercus in pig muscles , and cysticercosis exposure as seropositivity to anti-cysticercus antibodies with the presence of 0–5 cysticerci . Compared to pigs without nematode infections , those pigs infected with the nematode Ascarops strongylina were significantly associated with the presence of cysticerci ( OR: 4 . 30 , 95%CI: 1 . 83–10 . 09 ) . Similarly , pigs infected with the nematode Physocephalus sexalatus were more likely to have cysticercosis exposure ( OR: 2 . 21 , 95%CI: 1 . 50–3 . 28 ) . In conclusion , our results suggest that there appears to be a strong positive association between the presence of nematodes and both cysticercosis infection and exposure in pigs . The role of dung beetles in cysticercosis dynamics should be further investigated .
Cysticercosis affects humans following the ingestion of Taenia solium eggs , generally by fecal-oral contamination . Once ingested , T . solium eggs turn into cysticerci . In humans , cysticerci establish primarily in the central nervous system and is the main cause of epilepsy in adults in endemic areas [1] . In Peru , the prevalence of human cysticercosis is relatively high in endemic areas [2] . The T . solium life cycle requires an intermediate host , the pig , to develop its larvae stage . When humans eat pork contaminated with cysticerci ( T . solium larvae ) , T . solium tapeworms develop in their guts and then release eggs via human defecation . T . solium eggs turn into cysticerci when ingested by pigs . The presence of cysticerci in pigs can be observed macroscopically in the muscles as cysticerci are as large as rice grains . When cysticerci are viable , they are able to transmit the disease; cysticerci that are not able to transmit the disease are considered degenerated or non-viable cysts [3] , [4] . Distance to human tapeworm carrier has been associated with swine cysticercosis infection [5] , [6] . However , not all pigs around the tapeworm carrier become infected; in addition , some pigs far from the tapeworm carrier become infected or show seroprevalence to cysticercosis [6] . These findings suggest that an environmental vector may play a role in T . solium egg dispersion . We add evidence that the dung beetle may be such an environmental vector [7] . Pigs carry nematodes that require dung beetles as intermediate hosts to complete their life cycle [8] , [9] . Because dung beetles feed exclusively from feces and have been previously described to have a role in disease transmission [8] , [10] and because their role in T . solium transmission is uncertain , we tested the hypothesis that pigs that carry nematodes specific to dung beetles are associated with cysticercosis infection and/or exposure ( Figure S1 ) . For the purpose of this paper , we defined cysticercosis infection as the presence of at least one cysticercus in pig muscles , and cysticercosis exposure as seropositivity to anti-cysticercus antibodies with the presence of 0–5 cysticerci .
This study was reviewed and approved by the Ethical Committee of Animal Welfare of the School of Veterinary Medicine , San Marcos Major National University , Lima-Peru ( Authorization Number 2004-007 ) which adheres to the guidelines of the Council for International Organizations of Medical Sciences ( World Health Organization ) and the guidelines of the Office of Laboratory Animal Welfare ( National Institutes of Health , USA ) . A cross-sectional study of six villages of an endemic region in northern Peru was carried out , and all pigs ( 326 ) in the villages were euthanized and necropsies performed to diagnose cysticercosis , as described by Lescano et al . ( 2007 ) [6] . Data on T . solium infection were collected: infection/non-infection , number of viable cysticerci in muscles , and number of degenerated cysticerci in muscle . Also , blood samples were obtained and seroprevalence for cysticercosis was determined by western blot test [11] , [12] . In addition , data on other parasites observed at necropsy were gathered; parasites were further identified by genus and species . Other collected variables related to the pigs included age , sex , productive stage , village , household , and geographic reference ( latitude , longitude ) . A new dichotomous variable for age was created for pigs less than and more or equal to nine months of age [13] . The logarithm of the distance ( in meters ) to the nearest tapeworm carrier was calculated for each pig [5] . In addition , another dichotomous variable was created for pigs with a positive western blot anti-cysticercus antibody test that have 0–5 cysts [14] , as a measure of recent exposure to T . solium eggs [8] , [11] . To study the association between cysticercosis infection and dung beetle nematodes , we first analyzed the bivariate associations between cysticercosis infection ( viable cysticerci ( PV ) , degenerated cysticerci ( PD ) and positive infection to any type of cysticerci ( PIC ) ) and the nematodes Ascarops strongylina and Physocephalus sexalatus . In the same way , cysticercosis exposure ( positive for exposure or seropositive ( PE ) ) was also analyzed . Multivariable logistic regression ( MLR ) models were constructed to study the association between cysticercosis infection and exposure and A . strongylina and P . sexalatus , controlling for traditional risk factors for infection ( distance to the nearest tapeworm carrier , sex , age ) . The four models were evaluated for the number of parameters ( Akaike Information Criteria ) . The Huber/White Estimator was used to obtain robust standard errors to account for clustering . Data were analyzed using statistical software Stata/IC 10 . 0 ( College Station , TX , US ) . P values≤0 . 05 were considered statistically significant .
The overall prevalence of cysticercosis in the six villages was 12 . 27% ( 40/326 ) . In addition , the prevalence of viable and degenerated cysticerci was 5 . 52% ( 18/326 ) and 9 . 51% ( 31/326 ) , respectively; and seroprevalence of pigs with 0–5 cysticerci was 52 . 45% ( 171/326 ) . The pig population characteristics by villas are depicted in Table S1 . Distance to the nearest tapeworm carrier ranged from 0 to 10 , 844 meters . Among all pigs , 54 . 91% were females and 45 . 09% were males . Also , the age of pigs ranged from 1 to 48 months . Two nematode species were found during the pig necropsies: A . strongylina ( Family: Spirocercidae , Order: Spirurida ) and P . sexalatus ( Family: Physalopteridae , Order: Spirurida ) [9] . A . stronglyina and P . sexalatus were present in 17 . 79% ( 58/326 ) and 29 . 45% ( 96/326 ) of pigs , respectively ( Table S1 ) . Based on analysis of age , we observed that the cysticercosis-infected pigs tended to be older than 10 months and closer to the nearest tapeworm carrier . In addition , there were no substantial sociodemographic and agricultural differences between villages , which supported excluding village of our subsequent logistic regression models ( Table S2 ) . However , we used the robust estimate of the standard error to account for any clustering effect of the villages . The four MLR models are for PV ( positive infection with viable cysticerci ) , PD ( positive infection with degenerated cysticerci ) , PIC ( positive infection with any type of cysticerci ) , and PE ( positive exposure ) . The model with seven parameters was selected based on an Akaike information criterion ( AIC ) evaluation ( AIC<10 , Table S7 ) and scientific input [15] . The Hosmer-Lemeshow goodness-of-fit test confirmed a good fit of the data for the four models ( p-value>0 . 05 ) . The odds of having viable cysticerci was 3 . 9 times higher in those pigs that carried A . strongylina compared to those pigs that did not carry A . strongylina after adjusting for sex , age , distance to the nearest tapeworm carrier , presence of P . sexalatus ( p-value = 0 . 083 , 95% CI: 0 . 83–18 . 6; Table S3 ) . Likewise , the odds of having degenerated cysticerci was 3 . 1 times higher in those pigs that carried A . strongylina compared to those pigs that did not carry A . strongylina after adjusting for sex , age , distance to the nearest tapeworm carrier , and presence of P . sexalatus ( p-value = 0 . 037 , 95% CI: 1 . 07–9 . 31; Table S4 ) . Moreover , the odds of having any type of cysticerci was 4 . 3 times higher in those pigs that carried A . strongylina compared to those pigs that did not carry A . strongylina after adjusting for sex , age , distance to the nearest tapeworm carrier , and the presence of P . sexalatus; this association was statistically significant ( p-value = 0 . 001 , 95% CI: 1 . 83–10 . 09; Table S5 ) . Furthermore , the presence of P . sexalatus was positively associated with pigs positive to western blot that have 0–5 cysticerci ( OR: 2 . 21 , p-value = <0 . 001 , 95%CI: 1 . 50–3 . 28; Table S6 ) adjusting for other risk factors ( age , sex , distance to the nearest tapeworm carrier , presence of A . strongylina ) .
Our results show an association between the presence of nematodes and cysticercosis infection and exposure in pigs . Whereas A . strongylina was associated with cysticercosis infection , P . sexalatus was associated with cysticercosis exposure . The larvae of these two nematodes are transmitted by intermediate hosts; dung beetles [16] , [17] . These results suggest that dung beetles may play a role in cysticercosis transmission dynamics . To date , distance to the tapeworm carrier has been evaluated as the primary variable in explaining swine cysticercosis [6] . However , after adjusting for distance to nearest tapeworm carrier and other factors such as age and sex , we found a positive association between cysticercosis infection and the presence of A . strongylina . The presence of A . strongylina indicates that pigs have eaten dung beetles; therefore , dung beetles may play a role in swine cysticercosis infection . In addition , the other nematode species , P . sexalatus , was found to be associated with exposure to cysticercosis , suggesting that P . sexalatus' intermediate hosts may be playing a role in dissemination of low egg loads that provide sufficient exposure to cysticercus antigen and thereby conferring anti-cysticercus antibodies but no or low disease . In a pilot study Gonzalez et al . 2007 ( unpublished data ) demonstrated the capacity of dung beetles to ingest T . solium eggs and reproduce the disease by orally infecting naïve pigs . Two western blot negative pigs were fed six dung beetles each in an experimental design . Pig #1 was fed dung beetles that were fed T . solium eggs three days prior . Pig #2 was fed dung beetles that were fed T . solium eggs three weeks prior . Each dung beetle harbored approximately 50 T . solium eggs . The two pigs were slaughtered after 60 days of infection . At necropsy , pig #1 had no cysticerci but showed a positive anti-cysticercus western blot , and pig #2 had 100 viable cysticerci , 6 degenerated cysticerci and was positive to anti-cysticercus western blot . Although limited by the number of pigs in this experiment , this pilot study shows that 1 ) dung beetles can ingest T . solium eggs and 2 ) pigs can become infected or antigen-exposed with cysticercosis when eating dung beetles that have ingested T . solium eggs . These findings reaffirm the observational evidence presented by Nichols and Gómez ( 2014 ) [18] . Although not assessed in this study , different dung beetle species may serve as intermediate hosts for different nematodes . A study in a cysticercosis endemic area in Peru showed that the most frequent dung beetle species were from the genera Canthon and Deltochilum [19] . These two species differentiate in that Canthon has affinity for human and bovine feces whereas Deltochilum has affinity for bovine and equine feces [19] . In addition , there might be differences in dung beetle ecology or characteristics that explain these different associations [20] . For instance , Verdú and Lobo ( 2008 ) observed different flying techniques in these two genera [21] . The present study indirectly assessed the potential role of dung beetles as paratenic hosts by analyzing data of nematodes that require dung beetles as an intermediate host . However , further studies may elucidate specific dung beetles species that are associated with cysticercosis transmission . The importance of these findings lies in its implication for T . solium control and elimination programs in endemic areas . Elimination efforts have taken place in endemic areas in Peru [22]–[26] , but these strategies have yet to eliminate the disease for more than two years [27] . Dung beetles may help explain the re-emergence of the parasite in controlled endemic areas [28] , [29] . Dung beetles may also serve as potential markers for T . solium/cysticercosis in the community [30] . This study's finding of an association between dung beetle nematodes and swine cysticercosis infection and immune response encourages further investigation into the role that dung beetles play in cysticercosis transmission . | In endemic areas , pigs acquire cysticercosis when ingesting Taenia solium eggs that have been released into the environment in the feces of a person infected with T . solium . The present study has found evidence that players , such as dung beetles , might be involved in further dissemination of the parasite into the environment . Specifically , we found an association between helminths , for whom dung beetles act as an intermediate host , and porcine cysticercosis infection and exposure after adjusting for other porcine cysticercosis predictors such as distance to tapeworm carrier and age . Although the study does not evaluate dung beetles directly , parasites specific to dung beetles serve as a novel proxy to evidence the potential role of dung beetles in the epidemiology of cysticercosis . Therefore , it is important that further studies elucidate the role of other players in cysticercosis transmission in order to better explain the reemergence and persistence of cysticercosis after elimination and control efforts . In addition , vector populations could potentially be used as markers for cysticercosis in the communities . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"veterinary",
"diseases",
"medicine",
"and",
"health",
"sciences",
"veterinary",
"epidemiology",
"epidemiology",
"global",
"health",
"biology",
"and",
"life",
"sciences",
"veterinary",
"science"
] | 2014 | New Insights in Cysticercosis Transmission |
The sperm’s crucial function is to locate and fuse with a mature oocyte . Under laboratory conditions , Caenorhabditis elegans sperm are very efficient at navigating the hermaphrodite reproductive tract and locating oocytes . Here , we identify chemosensory and oxygen-sensing circuits that affect the sperm’s navigational capacity . Multiple Serpentine Receptor B ( SRB ) chemosensory receptors regulate Gα pathways in gustatory sensory neurons that extend cilia through the male nose . SRB signaling is necessary and sufficient in these sensory neurons to influence sperm motility parameters . The neuropeptide Y pathway acts together with SRB-13 to antagonize negative effects of the GCY-35 hyperoxia sensor on spermatogenesis . SRB chemoreceptors are not essential for sperm navigation under low oxygen conditions that C . elegans prefers . In ambient oxygen environments , SRB-13 signaling impacts gene expression during spermatogenesis and the sperm’s mitochondria , thereby increasing migration velocity and inhibiting reversals within the hermaphrodite uterus . The SRB-13 transcriptome is highly enriched in genes implicated in pathogen defense , many of which are expressed in diverse tissues . We show that the critical time period for SRB-13 signaling is prior to spermatocyte differentiation . Our results support the model that young C . elegans males sense external environment and oxygen tension , triggering long-lasting downstream signaling events with effects on the sperm’s mitochondria and navigational capacity . Environmental exposures early in male life may alter sperm function and fertility .
Animals employ sexual reproduction to increase genetic diversity critical for adapting to changing environments [1–3] . An essential process is fertilization , the merging of sperm and oocyte [4 , 5] . The motile spermatozoa ( referred to as sperm ) is a highly specialized cell built for finding and fusing with a competent oocyte . This task is particularly difficult in female animals where fertilization occurs internally , due to the reproductive tract’s convoluted architecture [6 , 7] . Sperm must successfully navigate through the tract in coordination with oocyte meiotic progression and compete with other sperm [8 , 9] . Females often have sperm storage sites where sperm compete for entry into the oviduct or access to oocytes [10 , 11] . Sperm motility is critical for navigation and competitive performance , yet varies extensively between , and within , species [12 , 13] . Beyond sperm competition forces , what drives these performance differences is not clear [9] . Sperm contain glycolytic enzymes and mitochondria to produce energy or sequester Ca2+ for movement [14 , 15] . Oxidative metabolism is more efficient than anaerobic metabolism , but an important byproduct is reactive oxygen species ( ROS ) , which can damage sperm proteins , lipids , and paternal DNA [16] . Oxidative stress is thought to be a major factor in male infertility and sperm DNA damage [16–18] . In rodent species with different sperm competition levels , increased competition is associated with increased sperm oxidative metabolism , motility performance , and DNA damage measured in vitro [19 , 20] . The extent to which sperm rely on aerobic metabolism may vary among environmental conditions . The relationship among environment , sperm metabolism , and sperm motility is not well understood , despite the potential importance for male fertility . The nematode C . elegans produces extremely efficient sperm that circumnavigate fertilized eggs in the hermaphrodite uterus while migrating to the fertilization site or spermatheca [8 , 21] . Developing oocytes secrete lipid guidance cues called prostaglandins [22–24] . Sperm respond to prostaglandins by increasing speed and directional velocity . Motility is driven by pseudopod translocation , a common feature among nematode species [25] . C . elegans sperm and flagellated sperm from other species share evolutionarily conserved metabolic pathways and cell surface proteins important for fertilization [26–28] . C . elegans exists in 2 sexes , hermaphrodites and males . Both sexes produce sperm , but the hermaphrodite reproductive tract is otherwise largely female . Hermaphrodite spermatids enter the spermatheca from the ovary , whereas male-derived spermatids are ejaculated through the vulva into the uterus during mating [29 , 30] . Mixing of male-derived spermatids with seminal fluid triggers spermiogenesis , resulting in motile sperm [31] . Male-derived sperm migrate hundreds of microns around fertilized eggs to the spermatheca . As ovulating oocytes pass through the spermatheca , sperm ( male or hermaphrodite ) are often pushed out into the uterus and must crawl back . Oocyte prostaglandin deficiency causes inefficient targeting of male-derived sperm to the spermatheca and loss of hermaphrodite and male-derived sperm from the spermatheca and uterus along with passing eggs [8 , 23] . Wild C . elegans colonize decomposing fruits and plants , where O2 tension is lower than the surface environment [32] . Although C . elegans prefers O2 concentrations around 8%–10% found in dense microbial habitats , O2 exposures are thought to fluctuate depending on local environment [32–34] . Ambient O2 triggers activation of the GCY-35/GCY-36 hyperoxia sensor , an atypical soluble guanylate cyclase heteromer that promotes aerotaxis behaviors [34] . The neuropeptide Y receptor ( NPR-1 ) and its ligands FLP-18 and FLP-21 modulate these behaviors [35–37] . Most wild isolates aggregate under ambient laboratory conditions , presumably to reduce local O2 tension [34 , 38] . During lab cultivation , the N2 Bristol strain acquired a gain-of-function mutation in npr-1 that suppresses aggregation behavior , alters pheromone responses , and reduces locomotion on plates seeded with Escherichia coli [39–41] . However , the role of hyperoxia-sensing circuitry in reproduction is largely unknown . C . elegans uses an extensive repertoire of chemosensory G protein-coupled receptors ( GPCRs ) to detect microbes and pheromones [42] . Chemosensory GPCRs are expressed in amphid sensory neuron cilia that extend their tip through the worm’s nose . Here , we identify a compact genomic cluster of SRB class chemoreceptors that influence sperm navigation performance . SRB-13 functions in amphid single I ( ASI ) and/or amphid single K ( ASK ) sensory neurons , where it localizes to cilia exposed to the external environment . flp-21 and npr-1 act in the same genetic pathway as srb-13 , but independently of aerotaxis behavior . SRB-13 antagonizes signaling output by the GCY-35 hyperoxia sensor , which functions to negatively affect sperm navigation performance . We show that SRB-13 signaling is important during early larval stages prior to testis maturation . Our data support the model that young C . elegans males alter neurosensory circuits involved in O2 sensing , depending on gustatory stimuli . These circuits impact gene expression during spermatogenesis important for mitochondrial function and sperm migration through the hermaphrodite uterus to the spermatheca .
Male-derived sperm navigational performance is assessed in wild-type hermaphrodites . We measure fluorescent sperm distribution in the uterus 1 hour after mating and sperm motility parameters ( i . e . , velocity and reversal frequency ) shortly after mating . Sperm distribution is assessed by dividing the uterus into 3 zones from vulva to spermatheca and counting fluorescent sperm in each zone [22 , 24] . When control males are mated to hermaphrodites , approximately 90% of sperm reach zone 3 , where a bottleneck forms at the spermathecal-uterine valve ( Fig 1A and 1B ) . We previously found that the srb-13 ( ok3126 ) mutation causes qualitatively abnormal sperm distribution [23] . In this current study , we found that 66% of srb-13 ( ok3126 ) male-derived sperm accumulate in the third zone 1 hour after mating ( Fig 1A and 1B ) . srb-13 encodes a predicted chemosensory GPCR . To investigate specificity , we screened a panel of 11 available GPCR mutants for sperm distribution effects . Initial investigation of available mutants found that only mutations in srb-5 , srb-13 , and srb-16 cause significant reductions in zone 3 targeting ( Fig 1A and 1B and S1 Table ) . These related srb genes are physically clustered in a 22 kilobase pair region on chromosome II , together with 4 other srb class members ( Fig 1C ) . The srb cluster is largely conserved in C . briggsae and C . remanei , but not in C . japonica and may have formed by gene duplication events during Caenorhabditis evolution ( genome data available at www . wormbase . org ) . To further investigate srb function , we used MosDEL and Cas9 genome-editing technologies [43 , 44] to generate additional srb deletions ( S1 Fig ) . The xm1 and xm15 deletions , which disrupt srb-13 and srb-12 , respectively ( Fig 1C ) , cause similar sperm-targeting defects ( Fig 1A and 1B ) . The xmDf2 deletion , which disrupts srb-12 , srb-13 , and srb-16 , causes a more severe defect than deletions disrupting single srb genes ( Fig 1 ) . Similarly , disrupting srb-2 , srb-3 , and srb-5 together causes a more severe defect than srb-5 ( tm5831 ) single mutants ( Fig 1 ) . When sperm targeting is assessed in srb mutant hermaphrodites mated to control males , male-derived sperm migrate efficiently to the spermatheca ( S2 Table ) . Hence , srb mutations specifically affect intrinsic sperm properties and the srb mutant hermaphrodite reproductive tract is like the wild type . srb mutant hermaphrodites have reduced brood sizes with no apparent oogenesis defects ( S2A Fig ) . These fertility deficits are reversed by mating to wild-type males and are thus due to defects in self-derived sperm , which could include reduced sperm production , reduced sperm motility , or other functional parameters . We conclude that srb chemoreceptors act in at least 2 parallel pathways to promote male-derived sperm targeting . We used time-lapse video microscopy immediately after mating to determine the basis for the srb mutant sperm distribution defects . Spermatids from srb mutant males are similar in size to controls , activate for motility upon insemination , and are competent to fertilize oocytes ( S3A Fig ) . Relative number of spermatids inseminated is also similar in control and srb mutant males ( S3B Fig ) . However , srb mutations differentially impact sperm motility performance in the uterus . Compared to control sperm , srb-13 and srb-2 , 3 , 5 ( xmDf1 ) mutant sperm have mildly reduced velocities and reverse course frequently ( Table 1 ) . On the other hand , srb-16 ( gk774 ) and srb-5 ( tm5831 ) sperm migrate slower than control sperm ( Table 1 ) . srb-13 , 12 , 16 ( xmDf2 ) sperm are slow and reverse course frequently ( Table 1 ) . These motility defects are consistent with failure to respond effectively to prostaglandins , which stimulate sperm velocity and prevent reversals ( Table 1 ) [22 , 24] . When sperm competitiveness was evaluated , we found that srb-13 , 12 , 16 ( xmDf2 ) sperm are less competitive at fertilization than control and srb-13 ( xm1 ) sperm ( S3C and S3D Fig ) . These data indicate that srb mutations , alone or in combination , primarily disrupt a sperm’s ability to navigate to the fertilization site and to compete with other sperm . srb mutant male sperm are similar to wild-type male sperm in fertilization ability and morphology , otherwise . Chemosensory GPCRs signal through heterotrimeric Gα , Gβ , and Gɣ proteins . Gα is divided into stimulatory Gsα , olfactory Goα , Gqα , and G12α [45] . Although Gα signaling is important for male mating behavior , we identified mutations in Gsα gsa-1 , Goα goa-1 , and Gqα egl-30 that permit sperm transfer in mass mating assays . The gsa-1 ( ce94gof ) gain of function mutation did not affect sperm navigation ( Fig 1A and 1B ) . In contrast , goa-1 ( sa734 ) loss of function and egl-30 ( tg26gof ) gain of function males have strongly reduced sperm navigation performance , similar to srb-13 , 12 , 16 ( xmDf2 ) males ( Fig 1A and 1B ) . This antagonistic relationship between GOA-1 and EGL-30 is also observed for motor neuron neurotransmission [45] . These data are consistent with SRB chemoreceptors regulating Goα and Gqα pathways . SRB chemoreceptors could function autonomously in sperm , as previously hypothesized [23] , or in other cell types . To investigate where SRB chemoreceptors function , we first examined available transgenic srb reporters [46] . The reporters show GFP ( green fluorescent protein ) expression in various larval and adult hermaphrodite and male sensory neurons in the head and tail ( S2B and S2C Fig ) . Expression is not observed in gonads , although transgenes are typically silenced in the germ line . We focused on srb-13 and srb-16 male expression . The srb-13 reporter expresses GFP in 3 pairs of head sensory neurons called ASI , ASK , and AWB ( amphid wing B ) , which extend dendrites with ciliated endings to the nose ( Fig 2A ) . The srb-16 reporter shows broader expression throughout the nervous system in pharyngeal muscle , hermaphrodite vulva muscles , and the male tail . GFP is detectable in ASH ( amphid single H ) , ASI , ASK , and AWB ( Fig 2B ) . Therefore , transgenic reporters show srb-13 and srb-16 expression in amphid sensory neurons . To determine endogenous SRB-13 and SRB-16 expression , we used Mos1 transposase and Cas9 to knock-in a tdTomato fluorescent tag into srb-13 and srb-16 genomic loci , respectively ( Fig 2C and S4 Fig ) . SRB-13::tdTomato and SRB-16::tdTomato fusion proteins exhibit near wild-type function , as indicated by sperm navigation assays ( S4G Fig ) . Male SRB-13::tdTomato expression is observed in 2 pairs of nose sensory cilia , their corresponding periciliary dendritic domains , and in puncta within neuron cell bodies ( Fig 2D and S5A Fig ) . Cilia location and morphology [52] are consistent with ASI and ASK expression observed in the transgenic reporter ( Fig 2A ) . Male SRB-16::tdTomato expression is observed in pm4 and pm5 pharyngeal muscles , I1 interneurons , and numerous head neuron cell bodies near the posterior pharyngeal bulb ( Fig 2E ) . Cell body positions near the pharyngeal bulb are consistent with ASH , ASI , ASK , and AWB expression . SRB-16::tdTomato expression is observed in neuron cell bodies and dendrites , but not ciliated endings , contrasting with SRB-13::tdTomato ( S5A and S5B Fig ) . Plasma membrane SRB-16::tdTomato is seen in pharyngeal ( Fig 2E ) and hermaphrodite vulva muscles . We could not detect evidence for srb chemoreceptor mRNA or protein expression in sperm or somatic gonadal cells ( Fig 2F and 2G ) . Two bona fide sperm-expressed genes , spe-9 and spe-11 [50 , 51] , are abundantly expressed in the qPCR experiment ( Fig 2F ) and a control spe-9 tdTomato knock-in shows sperm expression ( Fig 2G and S4C Fig ) . In summary , transgenic exogenous and endogenous srb reporters show expression in hermaphrodite and male amphid sensory neurons , but are not detectable in male somatic gonads or sperm . To test whether srb-13 and srb-16 function in amphids , we generated transgenic mutant males expressing these chemoreceptors under specific neuron and muscle promoters ( Fig 3 ) . Expressing srb-13 in striated muscles using the myo-3 promoter does not rescue the srb-13 ( ok3126 ) sperm navigation defect . In contrast , expressing srb-13 pan-neuronally using the unc-119 promoter or specifically in ciliated sensory neurons using the osm-6 promoter does rescue the sperm localization phenotype ( Fig 3 ) . We confirmed transgenic srb-13 expression in appropriate male cell types using a functional srb-13::mCherry fusion ( S5C and S5D Fig ) . Identical results are observed for srb-16 ( Fig 3 ) . Therefore , amphid srb-13 or srb-16 expression in respective null mutants is sufficient to promote sperm navigation . We reasoned that downstream goa-1 and egl-30 Gα pathways should also function in amphid sensory neurons . To test this hypothesis , we expressed goa-1 in male amphids from a transgene using the osm-6 promoter . Sensory neuron goa-1 expression is sufficient to rescue the goa-1 ( sa734 ) sperm navigation defect ( Fig 3 ) . The egl-30 ( tg26gof ) gain of function allele causes a strong navigation defect , suggesting that SRB signaling inhibits EGL-30 ( Fig 1A and 1B ) . egl-30 ( tg26gof ) encodes an R243Q substitution thought to affect guanine nucleotide binding [53] . To determine whether srb signaling is necessary in amphid neurons , we overexpressed EGL-30 R243Q in a wild-type background using the osm-6 promoter . The osm-6p::egl-30 ( tg26gof ) transgene causes a significant sperm navigation defect ( Fig 3 ) . We conclude that SRB signaling is necessary and sufficient in ciliated amphid sensory neurons to promote sperm navigational performance . SRB chemoreceptors promote GOA-1 activity or repress EGL-30 activity ( or both ) . How does SRB chemoreceptor signaling influence sperm navigation ? To help address this question , we used RNA-seq to compare transcriptomes of control males to srb-13 ( xm1 ) and srb-13 , 12 , 16 ( xmDf2 ) males ( Fig 4A and S6A Fig ) . We identified numerous sperm-expressed transcripts that are altered in the mutant datasets compared to the control , including 8 major sperm protein ( MSP ) genes ( S3 and S4 Tables ) . These data raise the possibility that SRB signals are transduced to the gonad , where they control gene expression in transcriptionally-active spermatocytes . Based on genetic analyses ( Fig 1 ) , altered RNA transcripts found in both srb-13 ( xm1 ) and srb-13 , 12 , 16 ( xmDf2 ) males are strong candidates for SRB-13-dependent regulation . We identified 266 altered genes common to both mutant male datasets ( Fig 4B and S4 Table ) , the vast majority of which exhibited similar changes in each mutant line ( Fig 4C ) . The srb-13 transcriptome is highly enriched in genes associated with pathogen defense , including those implicated in cuticle remodeling , detoxification , and intestinal microbial interactions ( Fig 4H and S4 Table ) [54] . Therefore , SRB-13 signaling is likely to influence gene expression in numerous cell types , perhaps as part of a systemic response . To identify potential genes expressed in spermatocytes , we filtered the dataset through the top 1 , 000 genes whose mRNA transcripts are most abundant in purified spermatids ( Fig 4B ) [55] . Thirty-three genes regulated by srb-13 overlap with the sperm dataset ( S4 Table ) , approximately 2 . 5 times the number expected by chance alone . The 7 most abundant transcripts , which are all reduced 2–4-fold in srb-13 ( xm1 ) and srb-13 , 12 , 16 ( xmDf2 ) mutants , share a common feature ( S4 Table ) . They all encode respiratory chain complex subunits derived from the mitochondrial genome ( Fig 4B and S6B Fig ) . These data support the model that SRB chemoreceptors influence gene expression in developing spermatocytes and other cell types . To test whether SRB-13 modulates expression of genes involved in oxidative metabolism in sperm precursors , we isolated spermatids from control and srb-13 ( xm1 ) males . The C . elegans mitochondrial genome encodes 12 protein complex subunits , 2 ribosomal RNAs , and 22 transfer RNAs ( Fig 4D ) [56] . Using RT-qPCR ( real-time quantitative PCR ) to compare RNA levels from isolated spermatids , we detected significant reductions in 9/13 tested transcripts in srb-13 ( xm1 ) mutant spermatids ( Fig 4E ) . The 18S and 23S ribosomal RNAs were unchanged in RNA-seq and qPCR analyses ( Fig 4E and S6B Fig ) . These results suggest that specific mitochondrial transcripts are destabilized in srb-13 mutant sperm , as precursor polycistronic RNA is processed to generate individual polyadenylated transcripts [56] . Consistent with these data , sperm from control and srb mutant males have similar mitochondrial content , visualized using Mitotracker dye ( Fig 1A ) . We conclude that SRB-13 increases mitochondrial electron transport subunit RNA levels in spermatids . To investigate the impact of altered expression of genes encoding respiratory chain complex subunits on sperm motility , we examined the uaDf5 strain [57 , 58] . The uaDf5 mutation is a mitochondrial genome deletion eliminating 4 subunits ( Fig 4D ) . uaDf5 males exhibit stable heteroplasmy from mutant and wild-type mitochondrial genomes , causing overall alterations in several mitochondrial transcripts and mild mitochondrial dysfunction ( S6C–S6E Fig ) [59] . uaDf5 males generate sperm that fail to efficiently navigate the uterus ( Fig 4F and 4G ) . Time-lapse videos show that uaDf5 mutant sperm migrate with reduced velocity and directional velocity , similar to sperm from srb mutant males ( Table 1 ) . srb-13 and uaDf5 mutant males have altered transcripts from multiple respiratory chain complexes , including complex I ( Fig 4E and S6E Fig ) . The nuclear-encoded nuo-6 ( qm200 ) mutation , which mildly reduces complex I function [60] , also causes less efficient sperm navigation ( Fig 4F and 4G ) . Another transcript reduced in both srb mutant datasets encodes the ubiquitous mitochondrial fission mediator drp-1 ( S4 Table ) . Sperm from drp-1 ( tm1108 ) mutant males fail to efficiently target the spermatheca ( Fig 4F and 4G ) . These results support the model that SRB-13 promotes expression or stability of RNAs in developing spermatocytes and/or spermatids that are important for mitochondrial function . Mitochondria in sperm are known to perform at least 2 functions important for navigation: ATP production and Ca2+ buffering . Cytosolic Ca2+ enters the mitochondrial matrix through the mitochondrial Ca2+ uniporter [61] . mcu-1 encodes the C . elegans ortholog of the Mcu ( mitochondrial calcium uniporter ) gene , an essential component of the uniporter [61] . mcu-1 ( ju1154 ) deletion mutant hermaphrodites and males are fertile , although the mutant males have slightly reduced sperm-targeting efficiency compared to control males ( Fig 4G ) . Importantly , mcu-1 ( ju1154 ) suppresses the srb-13 mutant sperm navigation defect and partially suppresses the srb-13 , 12 , 16 ( xmDf2 ) defect ( Fig 4G ) . The mcu-1 data support the hypothesis that srb-13 mutant sperm have altered mitochondrial Ca2+ buffering capacity important for navigation . To better understand how SRB signaling in amphids influences sperm , we screened a panel of 15 neuropeptide gene mutants for those affecting sperm navigation ( Fig 5A and 5B and S5 Table ) . Neuropeptides are often used by C . elegans to integrate chemoreceptor pathways with interneuron and endocrine circuits . Sperm navigation is not affected by mutations in any of 13 genes encoding various neuropeptides , including the daf-7 TGF-β homolog [24 , 62] ( Fig 5A and 5B and S5 Table ) . However , loss of the FMRF ( Phenylalanine-Methionine-Arginine-Phenylalanine ) amide-related neuropeptides flp-18 or flp-21 causes sperm navigation defects similar to srb mutants ( Fig 5A and 5B ) . FLP-18 and FLP-21 are ligands for the neuropeptide Y receptor homolog NPR-1 [36 , 40] . Indeed , npr-1 loss in males impairs sperm navigation ( Fig 5A and 5B ) . As FLP-18 may bind multiple receptors , we focused on FLP-21 [36 , 63] . To test whether flp-21 acts in the same genetic pathway as srb-13 , we constructed srb-13 ( xm1 ) ; flp-21 ( ok889 ) double-mutant males . The double mutants do not exhibit an additive sperm phenotype relative to single mutants , indicating that srb-13 and flp-21 act in the same genetic pathway ( Fig 5A and 5B ) . Similarly , we did not observe additive phenotypes for srb-12 ( xm15 ) ; flp-21 ( ok889 ) double or srb-13 , 12 , 16 ( xmDf2 ) ; flp-21 ( ok889 ) quadruple mutants relative to respective control mutants ( Fig 5A and 5B ) . An examination of the histogram shown in Fig 5B indicates that more sperm target zone 3 from srb-13 , 12 , 16 ( xmDf2 ) ; flp-21 ( ok889 ) males relative to srb-13 , 12 , 16 ( xmDf2 ) males , suggesting that flp-21 has complex actions in multiple srb pathways . These data suggest that SRB chemoreceptors interact with neuropeptide Y receptor circuitry to promote sperm navigation . A major function of NPR-1 is to inhibit signaling output from the GCY-35/GCY-36 hyperoxia sensor [34 , 64] . To test whether the sperm distribution defects in srb mutants are suppressed by gcy-35 loss , we generated srb-13 ( xm1 ) ; gcy-35 ( ok769 ) double and srb-13 , 12 , 16 ( xmDf2 ) ; gcy-35 ( ok769 ) quadruple mutant males . gcy-35 loss alone does not affect sperm navigation ( Fig 5B ) . However , gcy-35 loss fully suppresses the srb-13 ( xm1 ) male defect and partially suppresses srb-13 , 12 , 16 ( xmDf2 ) triple mutant male defect ( Fig 5A and 5B ) . Another NPR-1 function is to promote solitary feeding behavior at 21% O2 [39 , 41] , suggesting that feeding behavior might affect sperm navigation . Contrary to this idea , single and combinatorial srb mutants exhibit solitary behavior , with the sole exception of srb-12 ( xm15 ) worms that mildly aggregate . Furthermore , numerous wild C . elegans isolates that exhibit social feeding behavior have excellent sperm performance ( S7 Fig ) . Therefore , SRB signaling is not essential for all npr-1 functions , and feeding behavior is not responsible for the srb mutant sperm navigation defect . Two additional key conclusions are 1 ) gcy-35 negatively impacts sperm navigation , and 2 ) srb-13 acts upstream of , or in parallel to , gcy-35 to antagonize gcy-35 signaling output . Ambient ( 21% ) O2 found in laboratory worm cultures triggers GCY-35 activation [34 , 39 , 64] , raising the possibility that hyperoxia signaling is responsible for the srb mutant sperm migration defects . To test whether O2 concentration impacts sperm navigation , we exposed control and srb mutant males to 10% O2 and mated them to hermaphrodites raised at ambient O2 . In these experiments , males were exposed to 10% O2 throughout larval development and early adulthood , prior to and during the spermatogenic period . Mating and sperm navigation assays were performed under ambient conditions ( Fig 5C ) . When males are raised at 10% O2 , sperm navigation is like that seen for the 21% O2 control , regardless of the presence or absence of srb genes ( Fig 5A and 5B ) . For instance , sperm from srb mutant males raised under 10% O2 target the spermatheca as efficiently as control males raised at ambient O2 . Importantly , when males are raised at ambient O2 , srb genes are essential for sperm navigation ( Fig 5A and 5B ) . Collectively , these results demonstrate that SRB signaling antagonizes the negative effect of GCY-35 and possibly another hyperoxia sensor on sperm motility . The SRB-13 transcriptome includes genes that encode proteins implicated in various aspects of metabolism , detoxification , and pathogen defense ( Fig 4H and S4 Table ) . We considered the possibility that SRB pathways or sperm are sensitive to environmental , behavioral , or genetic perturbations impacting organismal physiology . Contrary to this notion , growing control males at 10% O2 or at different temperatures ( 16–25°C ) does not affect sperm performance ( S6 Table ) . Aggregation behavior , which can impact O2 exposure and food intake [33 , 34] , does not correlate with sperm performance ( S7 Fig ) . For example , the LSJ1 strain is a sibling stock of N2 isolated from Bristol , England circa 1950 [65] . Although LSJ1 and N2 exhibit differences in aggregation and feeding behavior [65] , males from both strains generate sperm with excellent performance under lab conditions ( S6 Table ) . Three highly divergent C . elegans strains are exceptions ( see Discussion ) . Male diets of E . coli strains such as NA22 , OP50 , or HT115 do not appreciably impact sperm navigation ( S6 Table ) . Furthermore , food deprivation in adult males for 24 hours does not alter sperm performance , provided males are briefly fed to enable mating ( S6 Table ) . Genetic mutations in males such as daf-7 ( m62 ) that alter fat metabolism [66] and clk-1 ( e2519 ) that delay development [67] have little impact on sperm migration ( S6 Table ) . In addition , we did not observe changes in sperm performance in males grown in isolation , males exposed to hermaphrodites or other males , or males in dense populations . These data suggest that SRB-13 affects sperm navigation independent of changes in aggregation behavior , pheromones , and organismal physiology . SRB-13 could affect spermatocytes/spermatids through a direct neuroendocrine pathway or through an indirect pathway ( s ) mediated by other cell types . The timing of SRB-13 signaling could provide an important clue . SRB-13 is expressed throughout larval development and adulthood , whereas spermatogenesis initiates in the mid L4 stage . To determine the critical time period for srb-13 activity , we used the Q system , a drug-inducible binary gene expression system [68 , 69] . QF ( quinic acid 1F transcriptional activator ) binds a 16-base pair sequence called QUAS to activate gene transcription ( Fig 6A ) . QS ( quinic acid 1S transcriptional repressor ) blocks QUAS-dependent transcription mediated by QF . Quinic acid ( QA ) , which is added to plates , inhibits QS repressor activity , thereby activating gene expression that is detectable roughly 6 hours after application [68 , 69] . We used the osm-6 promoter to drive QF and QS expression in male amphid sensory neurons and the QUAS promoter to drive srb-13 expression ( Fig 6A and 6B ) . QA did not have a negative effect on sperm motility in control males lacking the Q system ( Fig 6C and 6D ) . QA treatment to males expressing the Q system is required to produce functional SRB-13 , as evidenced by rescue of the srb-13 ( xm1 ) sperm navigation defect ( Fig 6C and 6D ) . We grew srb-13 ( xm1 ) males that had wild-type srb-13 under Q system control starting from L1 , L3 , L4 , or young adult stages ( Fig 6B ) . Initiating SRB-13 expression at the L1 or L3 stage rescues the srb-13 ( xm1 ) sperm navigation defect ( Fig 6C and 6D ) . In contrast , expressing SRB-13 at the L4 or adult stages does not rescue , despite QA treatment for 48 and 24 hours , respectively ( Fig 6C and 6D ) . Therefore , SRB-13 expression is not sufficient during L4 , when spermatocytes start to form , to improve sperm navigation performance . SRB-13 activity must initiate during early larval stages and is likely to impact the sperm’s mitochondria through lasting effects ( such as epigenetic changes ) to germ cells or other cell types .
The sperm cell is designed to deliver a single chromosome set to a waiting oocyte , whose own chromosomes are ready to pair . The delivery is a difficult journey that depends upon the ability to move long distances , sometimes in hostile environments , and locate a suitable fusion partner . Many animal species make far more sperm than available oocytes [9] . In male mammals , sperm motility parameters can vary extensively in ejaculates collected from different genetic backgrounds and at different times or places [12 , 13 , 70] . Not all animals are so wasteful when it comes to sperm . Drosophila and C . elegans generate sperm with much better fertilization chances [21 , 71] . The C . elegans sperm is well known for prodigious success rate , which requires prostaglandin positional cues provided by oocytes [22 , 23] . Chemical attractants are widely used by female animals to guide sperm towards oocytes [72] . Here , we discover molecular elements of a signaling mechanism coupling environmental cues to sperm success rates . Our results support the following model ( Fig 7 ) . SRB chemoreceptors act in ciliated amphid sensory neurons within the male nose to detect external cues . SRB signaling activates GOA-1 Goα or inhibits EGL-30 Gqα in amphids . These pathways are integrated with neuropeptide Y receptor circuitry that also modulates aerotaxis and food-searching behaviors [33 , 38] . SRB pathways are not essential in low O2 environments , presumably found in dense microbial habitats . In ambient O2 environments , GCY-35 hyperoxia sensor activity increases , triggering an inhibitory effect on spermatogenesis that reduces sperm navigational performance . SRB-13 antagonizes GCY-35 signaling output by impacting the sperm’s mitochondria . These mitochondrial alterations increase sperm migration velocity and decrease reversals within the hermaphrodite uterus . SRB-13 signals may initiate a systemic transcriptional response with trade-off to males dependent on environment and oxidative metabolism ( Fig 7 ) . Consistent with this idea , the SRB-13 transcriptome includes genes expressed in multiple tissues and SRB-13 activity is critical prior to spermatogenesis onset in L4 . SRB chemoreceptors appear to be constitutively active under lab conditions , raising the possibility that environmental cues not normally encountered in the lab antagonize SRB pathways . We propose that SRB-13 signaling counteracts the negative effects of high O2 levels , so as to maintain efficient mitochondrial function and sperm motility . Below we further discuss the data and our interpretations . To find food , C . elegans crawls into hypoxic microbial colonies often located within rotting vegetation [32] . SRB signaling is not required in hypoxic ( 10% O2 ) lab environments , likely due in part to low GCY-35 activity . When grown under ambient conditions that increase GCY-35 signaling , srb mutant males produce morphologically normal-looking sperm that are capable of fertilization , but the sperm target the spermatheca less efficiently than wild-type sperm . In these experiments , mating and sperm navigation assays are done at ambient O2 , so the only environmental condition that is altered is O2 tension during larval development and early adulthood . These data indicate that SRB chemoreceptors antagonize the negative effect that hyperoxia circuitry has on spermatogenesis . They also indicate that SRB pathways are not permissive for spermatogenesis , because they are not essential in all environments . srb-13 acts either upstream of , or in parallel to , gcy-35 and possibly another O2 sensor to affect sperm motility . SRB chemoreceptors appear to integrate external information into neuroendocrine pathways that respond to hyperoxic ( i . e . , ambient ) conditions . Aerotaxis and food-searching behaviors might be involved in the response , but they are unlikely to cause the srb mutant sperm motility defects . RNA-seq data from whole males shows that srb chemoreceptors affect expression of genes involved in pathogen defense , oxidative metabolism , detoxification , and sperm-specific functions . Many of these genes are expressed in diverse tissues , consistent with a systemic transcriptional response [37 , 54 , 73] . For instance , srb-13 promotes expression of the cytochrome P450 cyp-13A12 ( S4 Table ) , which is expressed in pharyngeal marginal cells and regulates a behavioral response to changes in O2 [74] . Specific RNAs derived from the mitochondrial genome that encode complex I , III , IV , and V subunits are reduced in isolated srb-13 mutant spermatids , indicating that SRB-13 affects mitochondrial gene expression . A potential mechanism involves RNA stability , as several RNAs in the same polycistronic transcript are unaffected and mitochondrial content is similar in srb-13 mutant and control sperm . Moreover , electron transport chain complex subunit mRNAs encoded in the nuclear genome are unaffected . Based on analysis of complex I mutants , uaDf5 mutants , and drp-1 mutants , srb-13 loss is hypothesized to alter sperm mitochondrial function . Further support comes from the finding that the srb-13 mutant sperm navigation defect is suppressed by reduced mitochondrial Ca2+ uniporter activity . The working model is that SRB-13 affects the expression or stability of multiple RNA transcripts in spermatids or their precursors , likely through a neuroendocrine mechanism . These SRB-13 targets may act together to influence oxidative metabolism during spermatogenesis and the mature sperm’s Ca2+ buffering capacity . Most natural C . elegans isolates sampled exhibit excellent sperm performance under laboratory conditions , despite differences in aerotaxis and feeding behaviors ( S7 Fig ) . Three exceptions are highly divergent strains from Hawaii and California . N2 Bristol differs from the Hawaiian CB4856 strain in O2-sensing circuit activity , due in part to polymorphisms in npr-1 [34 , 39–41] . CB4856 males generate sperm with poor navigation performance at ambient O2 , suggesting that SRB signaling is suppressed ( S7 Fig ) . The N2 npr-1 allele confers gain of neuropeptide Y signaling that prevents social aggregation . N2 NPR-1 ( 215V ) responds to FLP-18 and FLP-21 ligands [36] , both of which are essential for sperm navigation . CB4856 NPR-1 ( 215F ) only responds to FLP-21 , reducing NPR-1 activity . N2 NPR-1 is thought to diminish aggregation-promoting pheromone signaling in ASK , triggering solitary behavior [41] . Our genetic data are consistent with NPR-1 promoting SRB-13 signaling . Two key points are that SRB pathways are active in N2 and many other isolates under lab conditions and have little effect on aggregation behavior , with the exception of srb-12 . Spermatogenesis in strains like CB4856 appears to be more sensitive to ambient O2 than spermatogenesis in N2 Bristol and related strains . Why does an O2-sensing pathway impact sperm function ? The simplest idea is that there are positive and negative consequences to sperm or other cell types dependent on SRB-13 and environment ( Fig 7 ) . Environmental exposures during early development could be indicative of future stressful conditions . For example , O2 and mitochondria are important for fertility , but they also produce toxic ROS , which is associated with DNA damage and reduced fertilization success in many species [20 , 75] . In environments where ROS levels are likely to be elevated , C . elegans males may try to reduce oxidative damage to germ cells and other cells . In this model , the GCY-35 hyperoxia sensor functions to limit deleterious effects of O2 on spermatogenesis , but at the cost of sperm navigational performance . Another non-mutually exclusive idea is that males respond to stress-related cues by changing energy investment in spermatogenesis . Ambient O2 might be a sign of low food availability . SRB signaling improves male fertility in ambient environments , but possibly at the cost of increased energy needs . A potential complication is that SRB expression is not sexually dimorphic , and brood size defects are found in srb mutant hermaphrodites . SRB pathways may affect spermatogenesis differently in hermaphrodites and males . Future studies are needed to investigate potential costs and benefits of SRB signaling , as well as external cues that modulate SRB chemoreceptors . In conclusion , our results support the unexpected model that environmental exposures to young C . elegans males impact sperm mitochondrial function ( s ) during adulthood . The sperm’s mitochondria are important for efficiently navigating through the female reproductive tract , possibly in part through regulating cytosolic Ca2+ levels that modulate migration velocity and reversal frequency . An important implication for all animals is that environmental conditions during early development might have lasting effects on sperm function in adults . Better understanding of these mechanisms could be used to help prevent male infertility and help overcome detrimental consequences of oxidative metabolism to reproduction .
C . elegans were maintained at 20°C and incubated with NA22 E . coli bacteria , unless otherwise indicated [8 , 24] . A strain list is provided in the Supporting Experimental Procedures ( S1 Text ) . Males were generated by mating spontaneously-occurring males to hermaphrodites or by using mutations , such as fog-2 ( q71 ) , him-5 ( e1490 ) , or him-8 ( e1489 ) that increase male frequency in populations without affecting sperm navigation [76 , 77] . E . coli bacterial strains were grown in LB to an OD600 of 0 . 5 . Cultures were then spread on NGM plates and incubated for 1–2 days . Adult worms were transferred to these plates and allowed to lay eggs . Hatched larvae were grown in the bacteria until adulthood , unless indicated otherwise . For starvation experiments , males were washed extensively and placed on unseeded plated for 24 hours . Mating was performed on a 1-cm food drop for 30 minutes . Synchronized adult males were isolated using 35 μm and 20 μm pore-size nets , centrifuged , and frozen . Worm pellets were homogenized with a Bullet Blender 5 ( Next Advance ) . Total RNA was extracted with Trizol ( Invitrogen ) . cDNAs were synthesized from total RNA using the Cloned AMV 1st strand cDNA synthesis kit ( Invitrogen ) and oligo dT primers . Synchronized adult males were separated from hermaphrodite by using 35 μm and 20 μm pore-size nets [78 , 79] . To liberate spermatids , pressure was applied to males using a large vice and plexiglass plates . The released spermatids were isolated using 10 μm nets and examined under a stereoscope . RT-qPCR was performed with SYBR green real-time PCR master mix in an ABI Prism 7500 system ( Applied Biosystems ) . Sequencing and bioinformatics analyses were performed by Dr . Michael Crowley and Dr . David Crossman at the UAB Heflin Center for Genomic Science Core Laboratories using a NextGen Illumina platform . pXM1- , pXMDF1- , and pXMDF2-targeting vectors were generated by the Multisite Gateway 3-Fragment system ( Invitrogen ) . pXM4 targeting vector was generated by sequential restriction digest . All other vectors were generated by Gibson assembly ( New England Biolabs ) . Plasmids were sequenced for verification . The vector construction primers are listed in S7 Table . The ugt-62p::mCherry::unc-54 3’UTR transgenic array ( from plasmid pUM62 ) was integrated randomly into the C . elegans genome using gamma irradiation ( 4 , 000 rad ) . A line with an X chromosome integration event was backcrossed to the wild type 4 times and used for analysis . All other transgenes were maintained as extrachromosomal arrays . Images were taken using a Zeiss Axioskop equipped with epifluorescence ( Thornwood , NY ) or Nikon 2000U inverted confocal microscope ( Melville , KY ) . srb-13 ( xm1 ) , srb-13 , 12 , 16 ( xmDf2 ) , and srb-2 , 3 , 4 , 5 ( xmDf1 ) knock-outs were generated by MosDEL [43] . The srb-13 ( xm4 ) tdTomato knock-in was generated by removing the Mos1 transposon to generate a double-strand DNA break . srb-16 ( xm10 ) and spe-9 ( xm14 ) tdTomato knock-ins were generated using Cas9 endonuclease to generate breaks [44 , 80] . The C . briggsae unc-119 gene was used as a selection marker . The srb-12 ( xm15 ) knock-out was generated using co-conversion CRISPR [81] . Briefly , CMX-ROS Mitotracker ( Invitrogen ) -stained males were mated to unstained hermaphrodite for 30 minutes [8 , 22] . Hermaphrodites were then isolated and allowed to rest on food for 1 hour . Florescent and DIC snapshots of the uterus were used to determine sperm distribution . The distance from spermatheca to vulva is divided into 3 zones , with zone 3 closest to spermatheca . To directly observe sperm motility , mated hermaphrodites were mounted immediately on a 2% agarose pad for time-lapse fluorescence microscopy . DIC and fluorescence images were taken every 30 seconds . Directional velocity toward the spermatheca was measured by creating a straight line through the uterus from the vulva to the spermatheca . Two-tail Student t test , Mann-Whitney U test , and one-tail Fisher’s exact test were used for statistics . | Habitat loss , disease , climate change , and pollution are thought to negatively affect animal fertility . Sperm are a potential target , but the molecular mechanisms are not understood . The nematode C . elegans is a powerful genetic model to investigate the relationship between environment and male fertility . The hermaphrodite’s transparent epidermis permits the direct visualization of migrating male sperm and fertilization . In this study , we identified multiple serpentine receptor B ( SRB ) chemosensory receptors that are expressed in amphid sensory neurons , which extend cilia through the male nose . These SRB chemoreceptors are necessary to produce sperm that are efficient at navigating the hermaphrodite reproductive tract to the fertilization site . We show that SRB-13 signaling counteracts the negative effect of GCY-35 O2 sensor activity , thereby maintaining sperm mitochondrial function and navigational capacity in hyperoxic conditions . Of particular interest , SRB-13 acts in early larval stage males prior to testis maturation . We propose that young males respond to specific stressful environments by altering SRB neural circuits , which in turn impact sperm mitochondrial function and motility . This chemosensory mechanism may be part of a systemic response in C . elegans males to external environment and oxygen levels . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"uterus",
"meiosis",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"caenorhabditis",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"neuroscience",
"animals",
"reproductive",
"physiology",
"germ",
"cells",
"animal",
"models",
"caenorhabditis",
"elegans",
"model",
"organisms",
"animal",
"behavior",
"experimental",
"organism",
"systems",
"mitochondria",
"bioenergetics",
"zoology",
"cellular",
"structures",
"and",
"organelles",
"sperm",
"research",
"and",
"analysis",
"methods",
"spermatids",
"animal",
"cells",
"chromosome",
"biology",
"behavior",
"biochemistry",
"sensory",
"neurons",
"animal",
"migration",
"cellular",
"neuroscience",
"spermatogenesis",
"cell",
"biology",
"anatomy",
"animal",
"navigation",
"physiology",
"neurons",
"nematoda",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"energy-producing",
"organelles",
"organisms"
] | 2017 | Chemosensory and hyperoxia circuits in C. elegans males influence sperm navigational capacity |
Polyploidy has had a considerable impact on the evolution of many eukaryotes , especially angiosperms . Indeed , most—if not all—angiosperms have experienced at least one round of polyploidy during the course of their evolution , and many important crop plants are current polyploids . The occurrence of 2n gametes ( diplogametes ) in diploid populations is widely recognised as the major source of polyploid formation . However , limited information is available on the genetic control of diplogamete production . Here , we describe the isolation and characterisation of the first gene , AtPS1 ( Arabidopsis thaliana Parallel Spindle 1 ) , implicated in the formation of a high frequency of diplogametes in plants . Atps1 mutants produce diploid male spores , diploid pollen grains , and spontaneous triploid plants in the next generation . Female meiosis is not affected in the mutant . We demonstrated that abnormal spindle orientation at male meiosis II leads to diplogamete formation . Most of the parent's heterozygosity is therefore conserved in the Atps1 diploid gametes , which is a key issue for plant breeding . The AtPS1 protein is conserved throughout the plant kingdom and carries domains suggestive of a regulatory function . The isolation of a gene involved in diplogamete production opens the way for new strategies in plant breeding programmes and progress in evolutionary studies .
Polyploidy , the condition of organisms having more than two sets of chromosomes , has had a considerable impact on the evolution of many fungi , invertebrate , and vertebrate lineages and is particularly prominent in plants [1] , [2] . It is estimated that 95% of ferns are polyploids [3] and that almost all angiosperms have experienced at least one round of whole genome duplication during the course of their evolution [4] . Many important crop plants are currently polyploids or retain the vestiges of ancient polyploid events [5]–[7] . Even plants with small genomes , such as Arabidopsis thaliana , have been affected by polyploidy [8] , [9] . However , the mechanisms involved in polyploid formation are still poorly understood . For a long time , polyploids were thought to originate from somatic chromosome doubling[10] . The realisation that gametes with somatic chromosome numbers ( 2n gametes or diplogametes ) widely occur in diploid populations as a result of meiotic failure , led to a change of paradigm[11]; it is now believed that 2n gametes are the major route for polyploidy formation , in particular by leading to the formation of triploids , which then may serve as a bridge/step towards even ploidy levels [2] , [12]–[15] . 2n gametes are also instrumental in the genetic improvement of several polyploid crops , where useful genes from diploid relatives are incorporated into cultivated genotypes [16] , [17] . Given their importance in evolution and crop improvement , 2n gametes have been the focus of a considerable amount of research [12] , [18] . The best documented and described meiotic abnormalities leading to 2n gamete formation include abnormal cytokinesis , the omission of the first or second division and abnormal spindle geometry . Co-orientation of 2nd division spindles ( parallel spindles or fused spindles ) is perhaps the most common mechanism resulting in 2n spore formation [12] , [18] , most notably in potato [14] , and was first described more than eight decades ago [19] , [20] . Environmental factors , notably temperature and chemical agents , were shown to affect the frequency of 2n gametes [12] , [13] , [21] . However , 2n gamete production is under strong genetic control [13] . The genetic determination of 2n pollen production was studied in several species [12] and usually fits the segregation pattern expected for a major locus in a background of polygenic variation . To date , however , none of the genes contributing to high frequency 2n gametes production were identified and characterised at the molecular level [22] , [23] . This lack of information has slowed down our understanding of the origins of diplogametes , and limited the potential of diplogametes in crop breeding programmes . In this paper we describe the isolation and characterisation of the first gene , called AtPS1 ( Arabidopsis thaliana Parallel Spindle 1 ) , implicated in the formation of a high frequency of diplogametes in plants . We show that meiosis in Atps1 mutants generates diploid male spores , giving rise to viable diploid pollen grains and spontaneous triploid plants in the next generation . Analysis of male meiosis showed that during meiosis II spindles are abnormally orientated , with frequent parallel or fused spindles , leading to the production of two sets of chromosomes instead of four at the end of anaphase II . Genetic analyses of the diploid gametes and epistasis experiments demonstrated that diplogamete formation in Atps1 results from these defects in spindle organisation .
AtPS1 was identified in a screen for genes potentially involved in meiosis using the Expression Angler tool [24] , which selects co-regulated genes , in combination with the AtGenExpress tissue data set [25] . We first chose a subset of known meiotic genes ( AtMER3 [26] , AtDMC1 [27] , SDS [28] , AtMND1 [29] , [30] , AtHOP2 [31] , AtMSH5 [32] and AtSPO11-1 [33] ) for which the expression data appeared to be relevant: when one of these genes was used as the query in the Expression Angler tool ( with default parameters [24] ) , other known meiotic genes appeared among the first hits . We thus selected a list of genes that appeared among the first 60 hits in at least one query , and in the first 100 hits in at least two independent queries , using one of these seven genes as bait . Following an additional manual selection , including elimination of genes with known function or essential character , we ended up with a list of 138 candidate genes . We examined the phenotype of one to three lines carrying an insertion in each of these genes ( 218 lines in total ) [34]–[38] . Thirteen genes were not tested because corresponding mutant lines were not found in the databases . We visually screened ∼50 plants of each line obtained from stock centers [35] , [39] for reduced fruit length , without genotyping . The meiotic products of plants with reduced fertility were then examined . In two independent lines carrying an insertion in the AT1G34355 gene , plants were found to have slightly reduced fertility and unbalanced meiotic products . Chromosome spreads revealed that these plants were polyploid , prompting us to analyze these lines further . Functional characterization of this gene led us to name it AtPS1 ( see below ) . Two other genes with meiotic function were identified in the same screen ( R . M . , unpublished data ) . We amplified the AtPS1 cDNA ( EU839993 ) by RT-PCR on bud cDNA and sequencing confirmed that it is identical to that predicted in the databases ( NM_103158 ) . The AtPS1 gene contains 7 exons and 6 introns ( Figure 1A ) and encodes a protein of 1477 amino acids . BLASTp and PSI-Blast [40] analyses showed that the AtPS1 protein is conserved throughout the plant kingdom and contains two highly conserved regions . An FHA domain ( forkhead associated domain ) was predicted at the N-terminus ( CD-search: 65–140 aa , E-value 2e-11 ) [41] , while the C-terminal conserved region shows similarity to a PINc domain as identified using the SMART outlier homologue search ( BLAST: PINc , 1237–1389 aa , E-value 1 . 00e-84 ) [42] , the InterPro superfamily search ( SSF88723: PIN domain-like , 1235–1412 aa , E-value 8 . 8e-09 ) [43] as well as borderline similarities in CD-search ( smart00670: PINc , 1237–1305 aa , E-value 0 . 21 ) ( Figure 1B and 1C ) . No close homologs of AtPS1 containing both the FHA and PINc domain were found outside of the plant kingdom . An FHA domain is a phosphopeptide recognition motif implicated in protein-protein interactions and is found in a diverse range of proteins involved in numerous processes including intracellular signal transduction , cell cycle control , transcription , DNA repair and protein degradation [44] . The PINc domain has been predicted to have RNA-binding properties often associated with RNAse activity [45] , and this has now been experimentally confirmed [46] . Accordingly , several PINc domain containing proteins are involved in RNAi , RNA maturation , or RNA decay . The highest level of sequence similarity to the AtPS1 PINc domain in eukaryotes was found among others with S . cerevisiae Swt1[47] , mammalian C1orf26 , Drosophila CG7206 and SMG6 protein families [45] , [48] ( Figure 1D ) . We investigated the role of the AtPS1 gene by isolating and characterizing a series of allelic mutants , identified in several public T-DNA insertion line collections [34] , [35] , [37] . The Atps1-1 ( SALK_078818 ) and Atps1-2 ( WiscDsLox342F09 ) insertions are in a Columbia ( Col-0 ) background and are in the fourth exon and first intron , respectively . The Atps1-3 ( FLAG_456A09 ) insertion is in a Wassilewskija ( Ws-4 ) background and is located in the second exon ( Figure 1A ) . RT-PCR was carried out using the pAtpsF/pAtpsR primers ( Figure 1A ) on RNA from the Atps1-3 and Atps1-1 mutants and no detectable levels of the AtPS1 transcript were amplified , indicating that these two alleles are null . When the same primers were used on RNA from the Atps1-2 mutant normal expression levels of this region of the AtPS1 transcript were observed ( data not shown ) . Nevertheless , the phenotype analysis described below strongly suggests that this third allele is also null . In A . thaliana , male meiosis produces a group of four spores , organised in a tetrahedron , called a tetrad . As expected , male meiotic products in wild type were almost exclusively tetrads ( Figure 2 ) . Rarely , ( 13/304 ) groups of three spores were also seen but these were most certainly the result of occasional spore superposition . In contrast , the meiotic products in the three independent Atps1 mutants were characterized by a high frequency of dyads and triads ( Figure 2 ) . Atps1 mutants did not show any other developmental defects . The Atps1-1 and Atps1-2 mutants produced a majority of dyads ( ∼65% ) . The Atps1-3 mutant phenotype appeared to be weaker and only 8% of its meiotic products were dyads . Complementation tests between Atps1-1 and Atps1-2 and Atps1-3 and Atps1-1 showed that these mutations are allelic , and thus demonstrated that the dyads observed in this series of mutants are due to disruption of the AtPS1 gene . The Atps1-3 mutant exhibited a weaker phenotype than the two other alleles , whereas expression analysis suggested that this allele is also null . As this allele was in a different genetic background ( Ws-4 ) to the two others ( Col-0 ) , we tested if this difference could be influencing the strength of the phenotype by introducing the Col-0 mutation into the Ws-4 background and vice versa . As expected for a background effect , the frequency of dyads increased with successive backcrosses when Atps1-3 was introduced into Col-0 ( from 8% to 58% after four backcrosses ) and decreased when Atps1-1 was introduced into the Ws-4 background ( from 64% to 13% after four backcrosses ) . These results clearly indicate that the frequency of diploid gametes is influenced by multiple genes , with AtPS1 acting as a major gene . Pollen grain viability was examined by Alexander staining [49] and showed that in the majority of cases the dyads and triads produced by the mutants result in viable pollen grains ( more than 95% in the different Atps1 mutants : Col: 0 dead pollen grains out of 181 ; Atps1-1: 44 dead pollen grains out of 948 ; Atps1-2: 3 dead pollen grains out of 363 ) . We did observe however that the pollen grains in mutant plants varied in size ( data not shown ) . We then assessed the ploidy level of Atps1-1 and Atps1-2 pollen grains by quantifying spermatic nuclei DNA . Both mutants exhibited two different populations of pollen grains , one corresponding to viable haploid pollen grains ( ∼40% estimated by maximum likelihood ) and another to viable diploid pollen grains ( ∼60% estimated ) ( data not shown ) . These proportions are compatible with the proportion of dyads , triads and tetrads observed in the mutants . In summary , the Atps1-1 and Atps1-2 mutants produce a high frequency of viable diploid pollen grains . Next , we measured the ploidy level of the offspring of diploid Atps1 mutants by flow cytometry . Diploid and triploid plants ( 30% ) , but no tetraploid plants , were found among the progenies of Atps1-1 and Atps1-2 mutants ( Atps1-1: 38 triploids out of 130 plants; Atps1-2: 30 triploids out of 103 plants ) . Flow cytometry results were confirmed by karyotyping a subset of 29 plants which were all confirmed to be triploid . This demonstrated that the diploid gametes produced in the Atps1 mutants are involved in fertilisation and produce viable triploid plants . The appearance of triploids , but not tetraploids , suggests that the Atps1 mutations only affect male meiosis . As expected for the absence of a female meiotic defect we never isolated triploid plants when ovules from plants with the Atps1 mutation were fertilised with wild type pollen grains ( 0 triploids out of 182 plants ) . When mutant pollen was used for the cross we again observed that 30% of the progeny were triploids ( 20 triploids out of 56 plants ) . The observed frequency of triploid plants ( 30% ) among Atps1-1 and Atps1-2 mutant progeny is lower than expected from the frequency of diploid pollen grains produced by these mutants ( ∼60% ) . In parallel , more than 50% of seeds obtained by selfing the Atps1-1 and Atps1-2 mutants were thinner than wild type , abnormally colored and shaped , and germinated at a rate of 57% , compared to 99 . 8% in wild type . We do not believe , however , that this seed mortality phenotype infers a possibly essential role for AtPS1 in embryo development , for the following two reasons: 1 ) 25% ( 56/210 ) of the progeny of selfed heterozygotes were mutant and no dead seed was obtained , showing that the Atps1 mutation does not impair embryo development . 2 ) The same seed defect ( 59% of germination ) is observed when Atps1 is crossed as male with wild type as female , which shows that a seed with one functional AtPS1 allele may show developmental defects . Thus , a likely explanation for the discrepancy between the frequency of diploid pollen grains and triploids in the progeny is abnormal development of triploid seed , which is commonly observed during crosses between plant species with different ploidy levels . These problems are related to the paternal to maternal ratio , which is very important for normal endosperm development [50] . Using C24 and Ler accessions , Scott et al showed that triploid seeds obtained in diploid X tetraploid crosses germinated at a rate of 90% . We obtained stronger germination defects with Col0 , suggesting a background effect on the susceptibility to the paternal/maternal ratio . Another , non-exclusive , explanation for the discrepancy could be that haploid pollen grains out-competed diploid pollen grains , which were shown in some cases to germinate more slowly [51] , [52] . Nevertheless , approximately 25% of the triploid embryos appear to be able to overcome these constraints . To unravel the mechanisms leading to dyad production in Atps1-1 , we investigated chromosome behaviour during meiosis ( Figure 3 ) . Chromosome spreads showed that the meiosis in the Atps1-1 mutant progresses normally and is indistinguishable from the wild type until the end of the telophase I . Synapsis was complete , chiasmata formed ( the cytological manifestation of crossovers ) and bivalents were seen ( compare Figure 3G–I with Figure 3 A–C , for example ) . At metaphase II , however , differences were seen compared to wild type with the 10 chromosomes aligned in a same plane , causing abnormal looking figures , rather than two well separated metaphase II plates containing five chromosomes each ( Compare Figure 3J–K with Figure 3D ) . In rare cases , metaphase II in Atps1 did appear normal however ( Figure 3L ) . At telophase II , we observed dyads ( two sets of 10 chromosomes , Figure 3M ) , triads ( 2 sets of five chromosomes and one set of 10 , Figure 3N ) and normal tetrads ( 4 sets of 5 chromosomes , Figure 3O ) . These observations are consistent with the previous finding that Atps1 meiotic products are a mixture of dyads , triads and tetrads . These results and specifically the alignment of the 10 chromosomes at metaphase II suggested that the meiotic spindles in Atps1 mutants are defective at this stage . We thus examined spindle organisation by immunolocalisation with an alpha-tubulin antibody ( Figure 4 ) . In wild type plants the majority of metaphase II spindles were roughly perpendicular to each other ( Figure 4A ) , leading to four well separated poles at anaphase II ( Figure 4B ) and the formation of tetrads ( Figure 4C ) . In the Atps1 mutant , while individual metaphase II / anaphase II spindles appeared regular in most cases their respective orientation was aberrant . The majority of cells had parallel spindles ( Figure 4D to 4G ) , fused spindles ( Figure 4H and 4I ) or tripolar spindles ( Figure 4J and 4K ) . This defect in spindle orientation explains the appearance of triads and dyads . These conformations cause chromatids , that had been separated at meiosis I , to gather at anaphase II . Occasionally , three to four sets of chromosomes encompassed by a spindle were dispersed in the cell at metaphase II ( Figure 4L ) . This type of defect is probably the cause of the few unbalanced meiotic products observed in the Atps1 mutants . The name AtPS1 for Arabidopsis thaliana Parallel Spindle 1 was chosen due to the high percentage of parallel spindles produced by the corresponding mutants . Thus , parallel spindles at metaphase II in the Atps1 mutants appear to be leading to the formation of dyads . This proposed mechanism implies that unbalanced chromosome segregation at meiosis I would have no impact on the final distribution of chromosomes in the resulting dyad . To test this hypothesis we constructed a double Atspo11-1/Atps1 mutant . The Atspo11-1 mutant ( N646172 , Atspo11-1-3 ) [53] displays an absence of bivalents at meiosis [33] ( Figure 5A ) leading to frequent unbalanced first divisions ( Figure 5B ) that can be associated with lagging chromosomes ( Figure 5C ) . At metaphase II , unbalanced plates are seen ( Figure 5D ) , leading to unbalanced tetrads ( Figure 5E ) . Lagging chromosomes at anaphase II , lead to multiple metaphase II plates and then polyads with more than four nuclei ( Figure 5F ) . In the Atspo11-1/Atps1 background the first division was identical to the single Atspo11-1 phenotype . We observed 10 univalents at metaphase I ( Figure 5G ) , leading to missegregation at anaphase I , with two sets of unbalanced chromosomes ( Figure 5H ) or three sets because of lagging chromosomes ( Figure 5I ) . At metaphase II , we regularly observed two unbalanced metaphase plates , which had a tendency to be parallel instead of perpendicular ( Figure 5J ) . This led to the formation of dyads which were always balanced ( Figure 5K to 5L , n = 44 ) . We also observed triads with one set of 10 chromosomes caused by an unbalanced first division followed by the fusion of two of the four second division products ( Figure 5N ) , which is highly consistent with our proposed mechanism . We also observed unbalanced tetrads ( Figure 5P and 5Q ) , expected since the Atps1 mutation is not fully penetrant , and polyads due to lagging chromosomes at the first division ( Figure 5R ) . Another prediction of the proposed mechanism is that centromere distribution should resemble that seen during mitosis , e . g . , any heterozygosity at the centromeres should be retained in the diploid gametes . Indeed , in Atps1 , the first division is identical to wild type , with the co-segregation of sister chromatids and separation of homologous chromatids . Thus , in the case of a heterozygous genotype , A/a , at the centromere , following the first division the two A alleles will end up at one pole , and the two a alleles at the opposite pole . In wild type , the second division separates the two sisters leading to four spores with one chromatid . In Atps1 , the second division would regroup the products of the first division , thus grouping the a and A allele in each cell , leading to systematic heterozygosis at the centromere . Because of recombination , loci unlinked to centromeres should segregate randomly . We tested this prediction by taking advantage of the two genetic backgrounds of the Atps1-1 ( Col-0 ) and Atps1-3 mutants ( Ws-4 ) . F1 plants bearing the two mutations – thus mutant for AtPS1 and heterozygous for any Col-0/Ws-4 polymorphisms – were crossed as male to a third genetic background Landsberg erecta ( Ler ) . Karyotyping and genotyping of the obtained plants for trimorphic molecular markers provided direct information regarding the genetic make up of the pollen grain produced by the mutant ( Figure 6 ) . All the diploid pollen grains tested had the predicted genetic characteristics . They were systematically heterozygous at centromeres and segregating–because of recombination–at other loci . These results confirm that the “parallel spindle” defect is indeed the cause of at least the vast majority of 2n pollen in Atps1 .
In this study , we identified and described the AtPS1 gene and a corresponding set of mutants that produce pollen grains which are up to 65% diploid and give rise to numerous triploid plants in the next generation . Another Arabidopsis mutant that leads to severe meiotic defects and almost sterility [54] , [55] was recently described and reported to produce diploid female gametes [56] , but at a frequency of several orders of magnitude lower than the frequency of 2n gametes induced by the Atps1 mutation . By combining cytological and genetic analyses , we carried out a detailed investigation of the mechanism responsible for these 2n pollen grains in Atps1 , and established that they result from abnormal orientation of spindles at meiosis II . Interestingly , defects in meiosis II spindles are the most common known mechanisms responsible for the formation of 2n spores , [12] , [18] and are the main source of the 2n pollen which is extensively used in potato breeding programmes [14] . In potato , a major locus called ps was shown to be responsible for the parallel spindle phenotype more than 30 years ago [57] , but the corresponding gene is still to be identified . As was observed in different ps potato lines , Atps1 mutations only affect male meiosis and the frequency of dyads formed depends on the genetic background . The AtPS1 gene is conserved in higher plants ( Figure 1C ) and is therefore a good candidate for the gene behind the major ps locus of potato [14] . The fact that Atps1 mutations only affect male meiosis points to a difference in regulation between male and female 2n gametes production . This phenomenon was previously described for mutations that had a specific impact on either male or female meiosis [12] , [23] , [55] , [56] , [58] . In the case of parallel spindles , it may stem from the 3-dimension organization of the spores ( e . g . tetrahedron in male vs linear or multiplanar arrays in female [59] ) . The AtPS1 protein has two domains , a FHA ( ForkHead Associated ) domain , a phosphopeptide recognition domain found in many regulatory proteins and a PINc domain , which is found in proteins involved in RNA processing [48] . In fungi/metazoa , the AtPS1 PINc domain shows highest similarity with the PINc domains of the Swt1/ C1orf26/ CG7206 and SMG6 protein families followed by SMG5 , Dis3 and others . The mammalian C1orf26 and Drosophila CG7206 genes encode related proteins of unknown function , but Interestingly both are overexpressed in testis and ovaries , which is consistent with a putative meiotic role [60] , [61] . SMG6 is an essential component of the Nonsense Mediated RNA Decay ( NMD ) machinery that degrades mRNAs containing premature translation termination codons . SMG6 also plays a role in RNAi [45] , [48] . The SMG6 PINc domain has RNA degradation activity [46] . These features suggest that AtPS1 plays a regulatory function , perhaps via RNA decay , which may directly control the orientation of metaphase plates/spindles or be related to meiotic cell cycle control . There is growing evidence that NMD and its components have important functions in various cellular processes , including the cell-cycle [62] . A link between RNA decay and the control of meiosis progression was recently suggested because SMG7 , which is a NMD essential component , is involved in progression through meiotic anaphase II in Arabidopsis [63] . Further studies involving AtPS1 should shed light on the poorly understood process of meiosis II . The isolation of a gene involved in 2n gamete production has important implications for deciphering meiosis mechanisms , as well as potentially fundamental applications in evolution studies and plant breeding programmes .
Arabidopsis plants were cultivated as described in [64] . For germination assays and cytometry experiments Arabidopsis were cultivated in vitro on Arabidopsis medium [65] at 21°C with a 16h day/8h night photoperiod and 70% hygrometry . The Atps1-1 ( SALK_078818 ) and Atps1-2 ( WiscDsLox342F09 ) lines were obtained from the European Arabidopsis stock centre [39] . The Atps1-3 ( FLAG_456A09 ) insertion is from the Versailles T-DNA collection[35] . Plants were genotyped by PCR ( 30 cycles of 30 s at 94°C , 30 s at 56°C and 1 min at 72°C ) using two primer pairs . For each line the first pair designated is specific to the wild type allele and the second pair is specific to the T-DNA insertion . Atps1-3: EQM96L ( 5′ACATCTCCCTTGTCGTAAC3′ ) and EQM96U ( 5′ATCTCTCAATCGTTCGTTC3′ ) ; EQM96L and tag3 ( 5′ CTGATACCAGACGTTGCCCGCATAA3′ ) . Atps1-1: N578818U2 ( 5′TCGGAGTCACGAAGACTATG3′ ) and N578818L ( 5′CAGTCTCACTGATTATTCCTG3′ ) ; N578818U2 and LbSalk2 ( 5′GCTTTCTTCCCTTCCTTTCTC3′ ) . Atps1-2: N851945U ( 5′AAGGCTGATATTCTGATTCAT3′ ) and N851945L ( 5′CTCTTGTTGGTCCGTATCTTA3′ ) ; N851945U and P745 ( 5′AACGTCCGCAATGTGTTATTAAGTTGTC3′ ) . spo11-1-3: N646172U ( 5′AATCGGTGAGTCAGGTTTCAG3′ ) and N646172L ( 5′CCATGGATGAAAGCGATTTAG 3′ ) ; N646172L/ LbSalk2 . Genetic markers used to genotype Atps1-1/Atps1-3×Ler F1 triploid and diploid plants ( 40 cycles of 20 s at 94°C , 20 s at Tm and 30 s at 72°C ) : Microsatellite msat1 . 29450 ( located on chromosome I at position 29450001 ) was amplified ( Tm = 57°C ) using 5′TCCTTTCATCTTAATATGC3′ and 5′TCTGTCCACGAATTATTTA3′ primers . Microsatellite Msat4 . 35 ( Tm = 58°C ) ( located on chromosome 4 at position 7549125 ) was amplified using 5′CCCATGTCTCCGATGA3′ and 5′GGCGTTTAATTTGCATTCT3′ primers . Microsatellite NGA151 ( Tm = 58°C ) ( located on chromosome 5 at position 4669932 ) was amplified using 5′GTTTTGGGAAGTTTTGCTGG3′ and 5′CAGTCTAAAAGCGAGAGTATGATG3′ primers . The 2 primer pairs specific for the Atps1-1 and Atps1-3 TDNA borders were used as a centromeric marker of the chromosome 1 . CAPS markers Seqf16k23 ( physical position: 14481813 ) and CAPSK4 51 ( physical position: 5078201 ) were used as centromeric markers for chromosome 1 and 4 , respectively . CAPS Seqf16k23 was amplified ( Tm = 60°C ) using 5′GAGGATACCTCTTGCTGATTC3′ and 5′CCTGGCCTTAGGAACTTACTC3′ primers and observed after TaqI digestion . CAPS CAPSK4 51 was amplified ( Tm = 60°C ) using 5′CAATTTGTTACCAGTTTTGCAG3′ and 5′TGAGTTTGGTTTTTTGTTATTAGC3′ primers and observed after MnlI digestion . Final meiotic products were observed as describe in [28] and viewed with a conventional light microscope with a 40× dry objective . Chromosomes spreads and observations were carried out using the technique described in [33] . The DNA fluorescence of spermatic pollen nuclei was quantified using open LAB 4 . 0 . 4 software . For each nucleus the surrounding background was calculated and subtracted from the global fluorescence of the nucleus . Meiotic spindles were observed according to the protocol described in [55] except that the DNA was counter-stained with DAPI . Observations were made using an SP2 Leica confocal microscope . Images were acquired with a 63× water objective in xyz and 3D reconstructions were made using Leica software . Projections are shown . Cells were imaged at excitation 488 nm and 405 nm with AlexaFluor488 and DAPI respectively . Arabidopsis genome sizes were measured as described in [66] using tomato Lycopersicon esculentum cv “Montfavet” as the standard . ( 2C = 1 . 99 pg , %GC = 40 . 0% ) . Arabidopsis total RNA was extracted using the QUIAGEN RNA kit . Reverse transcription was done on 5 µg of total RNA using oligo ( dT ) 18 as primer . The RevertAid M-MuLV Reverse Transcriptase enzyme ( Fermentas ) was used according to the manufacturer's instruction . RT-PCR was carried out on 1 µl of cDNA using the pAtpsF and pAtpsR primers and the following PCR conditions: 30 cycles of 30 s at 94°C , 30 s at 56°C and 1 min at 72°C . | In the life cycle of sexual organisms , meiosis reduces the number of chromosomes from two sets ( 2n ) to one set ( n ) , while fertilization restores the original chromosome number . However , in case of failure of meiosis to reduce the chromosome number , the fecundation involving the obtained 2n gametes can lead to the formation of an organism with more than two sets of chromosomes ( polyploid ) . Polyploidization occurred widely in the course of evolution of eukaryotes , especially of plants . Besides , many crops are current polyploids , and 2n gametes have been useful for their genetic improvement by allowing crosses between 2n and 4n species . 2n gametes formation is known to be under genetic control but none of the genes involved were identified . We have isolated and characterised a gene ( AtPS1 ) involved in controlling diploid ( 2n ) gamete formation in A . thaliana . In the Atps1 mutant , the second division of meiosis is disturbed , leading to the gathering of chromosomes that had been separated at the first division . Consequently , Atps1 mutants produce 2n male gametes and spontaneous triploid plants in the next generation . The isolation of a gene involved in diplogamete production opens the way for new strategies in plant breeding programmes and progress in evolutionary studies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/plant",
"genomes",
"and",
"evolution",
"cell",
"biology",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression",
"genetics",
"and",
"genomics/chromosome",
"biology"
] | 2008 | Mutations in AtPS1 (Arabidopsis thaliana Parallel Spindle 1) Lead to the Production of Diploid Pollen Grains |
Evolutionary responses to environmental change depend on the time available for adaptation before environmental degradation leads to extinction . Explicit tests of this relationship are limited to microbes where adaptation usually depends on the sequential fixation of de novo mutations , excluding standing variation for genotype-by-environment fitness interactions that should be key for most natural species . For natural species evolving from standing genetic variation , adaptation at slower rates of environmental change may be impeded since the best genotypes at the most extreme environments can be lost during evolution due to genetic drift or founder effects . To address this hypothesis , we perform experimental evolution with self-fertilizing populations of the nematode Caenorhabditis elegans and develop an inference model to describe natural selection on extant genotypes under environmental change . Under a sudden environmental change , we find that selection rapidly increases the frequency of genotypes with high fitness in the most extreme environment . In contrast , under a gradual environmental change selection first favors genotypes that are worse at the most extreme environment . We demonstrate with a second set of evolution experiments that , as a consequence of slower environmental change and thus longer periods to reach the most extreme environments , genetic drift and founder effects can lead to the loss of the most beneficial genotypes . We further find that maintenance of standing genetic variation can retard the fixation of the best genotypes in the most extreme environment because of interference between them . Taken together , these results show that slower environmental change can hamper adaptation from standing genetic variation and they support theoretical models indicating that standing variation for genotype-by-environment fitness interactions critically alters the pace and outcome of adaptation under environmental change .
With human activities contributing to climate change [1] , it has become urgent to pinpoint the ecological and evolutionary mechanisms by which natural populations adapt at different rates of environmental change . It is generally accepted that lower rates of environmental change allow more time for beneficial mutations to appear , to be selected , and , as a consequence , to promote adaptation and rescue populations before environmental degradation leads to their extinction [2–6] . Experimental evolution results from studies with microbes that depend on de novo mutation support the idea that slower environmental change facilitates adaptation [7–10] . Unlike microbial experimental evolution , however , most species in nature have small populations , are genetically structured by geography , breeding mode or reproduction system , and might have long generation times . In all these cases , adaptation to changing environments will likely depend on standing genetic variation , and less so on de novo mutation [11 , 12] . Adaptation to changing environments from standing genetic variation is conditional on how each extant genotype performs within the environments that may be encountered in the near future ( Fig 1A ) . Depending on the shape of these “fitness reaction norms” [3 , 10 , 13] , and previous evolutionary history responsible for standing genotype frequencies [5 , 14] , natural selection may initially favor genotypes at intermediate challenging environments that are not necessarily the best at the more extreme environments . In other words , adaptation will depend on standing genotype-by-environment ( GxE ) fitness variance [11] , until de novo mutations or new recombinant genotypes escape genetic drift and start to be selected upon [15] . Understanding the population genetic dynamics of adaptation from standing genetic variation to changing environments has only been recently formalized using a moving polygenic trait optimum model [11] . In contrast to evolution from de novo mutation , one of the strongest conclusions from ref . [11] was that slower environmental change can restrict adaptation when evolving populations depend on standing genetic variation . One reason for this is that the maintenance of standing genetic variation for longer periods can result in reduced fitness variance and thus reduced rates of adaptation [16 , 17] ( Stage II in Fig 1A ) . Another reason is that since all populations are finite , and may suffer bottlenecks in the novel environments , the longer it takes to reach the most extreme environments ( Stages I and II in Fig 1A ) the more probable it is that the best genotypes are lost by genetic drift or by founder effects and thus be unavailable when populations reach the extreme environments ( Stage III in Fig 1A ) . Because of standing variation for GxE fitness interactions , this later process will be more pronounced if the best genotypes at the extreme environments are initially selected against in the less extreme environments ( Stage I in Fig 1A ) . In general , whether or not a population has standing genetic variation is expected to greatly affect the tempo and mode of adaptation in changing environments . Given a fixed amount of standing genetic variation , and assuming no input of de novo mutation or new recombinant genotypes during evolution , we here experimentally test how the rate of environmental change affects adaptation . We investigate whether slower environmental change can constrain adaptation because of the loss of extant genotypes that would perform best in the most extreme environments . These genotypes may be lost during the period of environmental change via genetic drift or founder effects . To this end we performed experimental evolution under a sudden or gradual environmental change ( Fig 1B ) , using populations of the nematode Caenorhabditis elegans with standing genetic variation and where individuals can only reproduce by self-fertilization . In this situation , we expect that asexual population genetic dynamics will be followed and that they will depend on standing GxE fitness variance . At several periods we collected genome-wide single-nucleotide polymorphism ( SNP ) data and used these to infer the fitness reaction norms of the genotypes that were present in the ancestral population as well as their expected frequency changes during experimental evolution due to selection . We performed a second set of evolution experiments where we test for the repeatability of adaptation in the most extreme environment to show that genetic drift and founder effects during prior gradual evolution can lead to the loss of the best genotypes and impact selection efficacy .
As previously reported in ref . [18] , we performed experimental evolution for 50 generations in the nematode C . elegans under different rates of change in the NaCl ( salt ) concentration that individuals experience from early larvae to adulthood ( Fig 1B ) . In one regime , populations were suddenly placed in high salt concentration conditions ( 305 mM NaCl ) and then maintained in this environment for 50 generations ( see Materials and Methods and S1 Text section 1 . 1 ) . In another experimental evolution regime , populations faced gradually increasing salt concentrations for 35 generations , being thereafter maintained in constant high salt for an extra 15 generations . For the “sudden” regime , 4 replicate populations undergoing independent evolution were followed , while for the “gradual” regime we followed 7 replicate populations ( S1 Table ) . All populations were derived from a single ancestor population adapted for 140 generations to lab conditions ( 25 mM NaCl ) , after initial hybridization of several wild isolates [19] , that has abundant genetic diversity ( expected SNP heterozygosity of ~0 . 3 , for 1 SNP per kbp on average , presumably maintained in excess by balancing selection at overdominant loci , see [20 , 21] ) but where individual hermaphrodites reproduce exclusively by self-fertilization ( obtained by the introgression of a male-killing mutant to the lab adapted population , cf . [18] ) . Hermaphrodites are expected to be mostly homozygous throughout their genome before the start of experimental evolution in changing salt environments [18 , 22] . Except for salt concentrations , the same life-cycle of discrete and non-overlapping generations at stable census population sizes of 104 hermaphrodites at the time of reproduction were maintained as during lab adaptation . Lab adaptation occurred under partial self-fertilization and outcrossing , with an estimated effective population size of the order of 103 [20] . With exclusive self-fertilization this number should be halved to at least 500 [22] . A control regime with 3 replicate populations was also maintained at the 25 mM NaCl conditions of lab adaptation . Given exclusive self-fertilization , the expected effective population sizes , and the time span of experimental evolution , de novo mutation or new recombinants from standing genetic variation should not contribute much to adaptation to high salt concentrations [11 , 23] . During experimental evolution , we measured the frequency of biallelic SNPs obtained from genotyping hermaphrodites in the ancestral population and the evolved populations at generations 10 , 35 and 50 ( Figs 1B and 2A ) . All replicate populations from the control and sudden regimes were genotyped , while from the gradual regime we genotyped 4 out of the 7 replicates ( see Materials and Methods and S1 Text section 1 . 6 for the genotyping protocol , and S1 Fig for SNP density and sample sizes ) . SNPs were chosen based on the known diversity present in the 140 generation lab adapted population [21] , and the expected genetic distance between them [24] ( S1A Fig ) . Given the limited amount of genomic DNA per hermaphrodite to perform whole-genome genotyping at a high density , we chose to genotype each hermaphrodite only in a pair of chromosomes ( C . elegans is diploid with six similarly-sized chromosomes , for a genome of 100 Mbp ) , with the objective of sampling haplotypes at relatively low frequencies ( Fig 2A ) . With self-fertilization and complete linkage disequilibrium , the number of observed chromosome-wide haplotypes ( CWH ) should be similar to the number of observed “region-wide” haplotypes ( RWH ) , each defined by a pair of homozygous chromosomes in each hermaphrodite ( Fig 2A ) . We estimate , however , that linkage disequilibrium is not complete since when we take the data from all populations and time points into consideration between 5% and 21% more RWHs than CWHs are found , depending on the region ( Fig 2B ) , with the majority of them being at low frequencies across the dataset ( S2 Fig ) . We estimate that the ancestral population must have segregated at least 212 RWHs ( the minimum observed number ) , and by extrapolation at least the same number of whole-genome haplotypes although many more are possible ( see S1 Text section 1 . 11 ) . To facilitate computation , we grouped minor frequency region-wide haplotypes ( RWHs ) in each replicate population into a single class ( see S1 Text section 1 . 10 , and S2 and S3 Figs ) . We find that the majority of RWHs in the ancestral population are quickly selected against under all experimental evolution regimes ( Fig 3 ) . By generation 50 , all populations are dominated by a single RWH in each of the two-chromosome regions . Populations faced with a sudden change in the first generation followed by constant high salt ( 305 mM NaCl ) consistently show a single haplotype sweeping and nearing fixation by generation 50 . In contrast , populations faced with a gradual increase in salt until generation 35 showed a different haplotype initially sweeping but then reverting in frequency when they were kept in the target high salt environment for another 15 generations . Control populations also show that a single RHW per genomic region sweeps through them . This is the same haplotype as that found during initial gradual evolution , suggesting continued lab adaptation under exclusive self-fertilization independently of salt [18] . Since experimental evolution occurred under exclusive self-fertilization and we assume complete homozygosity , the fitness reaction norms of genome-wide haplotypes , here defined as “lineages” , are the key variables for describing selection in changing salt environments , and thus the eventual outcome of adaptation to the extreme high salt environment ( Fig 1 ) . The non-monotonic RWH frequency dynamics observed in the gradual populations in particular ( Fig 3 ) can be explained by the crossing of fitness reaction norms somewhere along the salt gradient but , conceivably , also by negative frequency-dependent selection among segregating lineages [15] . To detect selection in changing environments , we adapted standard population genetics modeling [25] to infer the fitness reaction norms of segregating lineages and their expected frequency dynamics ( S1 Text section 1 . 8 ) . We model a single additive multi-allelic locus in effectively asexual populations , and thus do not specifically account for dominance or epistasis . We further modeled deterministic environmental and population genetics ( i . e . , there are no genotype frequency changes and no genotype extinction/fixation due to random environmental fluctuations or finite population sizes ) , with discrete non-overlapping generations and viability selection . We consider that the environment faced in a given generation is represented by a single environmental value x , in our case corresponding to the NaCl concentration ( Fig 1B ) . A population is composed of G genome-wide lineages , with the fitness reaction norm for lineage k being described by λk ( x ) ( Fig 4 ) , corresponding to the expected absolute number of live offspring produced under environment x . Selection is defined by the per generation growth multiplier ( growth rate ) of each lineage relative to mean population fitness–with the frequency of each lineage expected to follow a deterministic logistic frequency trajectory [25 , 26] . Our model allows for any parameterization of the fitness reaction norms although we only investigate linear and quadratic functions . To infer the frequency dynamics of the lineages during experimental evolution , we developed a maximum-likelihood model that estimates the parameters describing the fitness reaction norms of these lineages ( S1 Text section 1 . 9 ) . For this , we rely on genotyping data , consisting of the number of each RWH observed when genotyping the populations in various time-points . Since the model is parameterized on absolute fitness , we also rely on fitness data , which serves to properly scale the estimated parameters . Inference is done in several steps , illustrated in Fig 4 . We first sample the lineages that likely compose the ancestral population , taking the sample sizes and estimated RWHs frequencies into consideration , since the true lineage identities and their starting frequencies are unknown ( S4 Fig and S1 Text section 1 . 11 ) . We then estimate the parameters for the fitness reaction norms of the various RWHs constituting a lineage ( each determined by the combination of sampled RWHs , assuming linkage equilibrium among them ) , and define that the lineage parameters are the sum , in log space , of their constituent RWH parameters ( Fig 4 ) . The final likelihood depends on the probability of observing the mean ancestral population fitness ( in low and high salt , see below ) and the observed RWH time series ( for all populations and regimes ) , given the sampling done to identify the lineages and their frequencies in the ancestral population . We initially modelled linear fitness reaction norms and found that two lineages dominate the population genetic dynamics . The measured RWH frequency dynamics ( Fig 3 ) are consistent with a single lineage sweeping through the sudden populations ( Fig 5 ) , which we label L28 ( see below ) . In contrast to the sudden populations , the gradual populations had an initial increase of a lineage other than L28 ( labeled L11 ) , but then started to be overtaken by L28 after the 15 generations of high salt ( Fig 5; see S5–S7 Figs for detailed frequency dynamics of major constituent RWHs in each genomic region in all replicate populations and regimes ) . L11 clearly shows a non-monotonic trajectory in the gradual populations , initially being positively selected and later being negatively selected . Under all experimental evolution regimes , a few other lineages are predicted to also explain population genetic dynamics , although these lineages do not regularly approach a frequency above 15% at any period ( S8 Fig: see , for example , lineages 13 and 20 in the sudden and gradual populations , or lineage 470 in the control populations ) . We reach the same conclusions regarding RWHs ( S9 Fig ) and lineage ( S10 Fig ) frequency dynamics when we used a quadratic parameterization for the reaction norms . S11 and S12 Figs show the expected dynamics of the mean and the variance in population fitness under linear and quadratic models , respectively . Under the linear model , adaptation to intermediate salt conditions in the gradual regime results in a great loss of fitness variance . At the same time , mean population fitness also decreases , a result that is consistent with the existence of an adaptive “lag load” , cf . [2 , 11] , since L28 is for most periods not being selected . In the sudden regime , mean population fitness strictly increases while L28 is being positively selected . Under the quadratic model , dynamics are more idiosyncratic in the gradual regime , but mean population fitness decreases to similar levels as in the linear model , and then recovers at a similar pace . We measured the ancestral population absolute fitness as the growth rate over one generation at 25 mM and 305 mM NaCl to help with the inference of fitness reaction norms ( see previous section , S1 Text section 1 . 3 ) . We first sought to validate the analysis by measuring the ancestral population absolute fitness at an intermediate salt concentration ( 225 mM NaCl ) . Results show that there is a large difference between the expected and observed fitness values at 225 mM NaCl ( Fig 6A ) , although they are intermediate to 25 mM and 305 mM NaCl . The discrepancy between observed and expected fitness values was anticipated since our inference at 225 mM NaCl was only informed by the observed RWHs frequencies in the gradual populations at generation 25 ( Fig 1B and S5–S7 Figs ) . More directly , we sought to validate the analysis by measuring the fitness reaction norms of the two lineages ( L11 and L28 ) that appear to dominate the population genetic dynamics during experimental evolution ( Fig 5 and S8 Fig ) . Using whole-genome sequencing data on 100 lineages derived from two gradual populations at generation 50 ( as reported in [21] ) , we identified those corresponding to L28 and L11 ( S13 Fig and S2 Table ) . Our model predicts that the linear or quadratic fitness reaction norms of these two lineages cross between 200–250 mM NaCl ( Figs 5A and S10A ) . To test this prediction , we revived L28 and L11 from frozen stocks and assayed their absolute fitness at 25 mM , 225 mM and 305 mM NaCl . Absolute fitness was measured as the growth rate over two generations under non-competitive conditions . We find a close agreement with the model in that the lineages’ reaction norms cross at about 225 mM NaCl ( Fig 6B and 6C ) , even if the observed values are higher than the predicted ones . At 25 mM NaCl there is a larger difference between observed and expected fitness values than at other salt concentrations . Differences between observed and expected fitness values can be explained by the low frequency of these two lineages in the ancestral and control populations ( those that experienced 25 mM NaCl ) , and the gradual populations when at 225 mM NaCl by generation 25 . Supporting our interpretation , the observed fitness values at 305 mM NaCl closely match the expected fitness values ( Fig 6B and 6C ) , particularly for the L28 lineage . In this case the inference was mostly informed by the lineages segregating in the sudden populations , as they always experienced this salt concentration during experimental evolution ( S8 Fig ) . It is possible that non-transitive interactions between standing genetic variation , in particular because of negative frequency-dependence , could in part also explain the discrepancies between observed and expected absolute fitness values in the ancestral population and the L28 and L11 lineages . To test for this possibility , we conducted head-to-head competitive ( relative ) fitness assays between L28 and L11 ( S1 Text , section 1 . 4 ) . In these competition assays , performed for 2 consecutive generations , both lines were initially placed at 1:1 ratios at the usual population sizes , noting that these frequency ratios between L28 and L11 were never realized during experimental evolution ( Fig 5B ) . The results from the competition assays are qualitatively similar to those under non-competitive conditions ( Fig 6D , compare with Fig 6C ) . Non-transitive interactions between L28 and L11 therefore do not appear to be significant in explaining differences between observed and inferred fitness values . Besides the uncertainty in estimating the frequency of segregating lineages , the discrepancy between observed and expected ancestral and lineage fitness can be explained by how well the parameterization of the reaction norms is done . For example , in the linear model variance in fitness as a function of salt levels must be strictly correlated while in the quadratic model the extra parameter allows the variance in fitness to differ between salt levels . Since power to infer fitness at low salt is generally weak , predictions with the quadratic model will necessary be less precise . So far , our experiments and modeling demonstrate that the population genetic dynamics under different rates of environmental change are contingent on the GxE fitness variance present in the ancestral population . We found that lineage L28 is the best genotype in high salt , and therefore–assuming no de novo mutation or recombinants–adaptation can be hindered under slow rates of environmental change if the loss of this lineage by genetic drift or founder effects is more probable than under fast rates of environmental change ( see Introduction and Fig 1A ) . From our model , we expect the L28 frequency in the ancestral population and in the gradual populations at generation 35 to be negligible ( Fig 5 ) . The model assumes deterministic frequency dynamics and infinite population sizes and thus cannot be verified with the experimental data , since , for example , some of the replicate gradual populations could have lost L28 by the time they reached generation 35 . Although some of the region-wide haplotypes constituting the L28 lineage are observed in the 4 replicate gradual populations at generation 35 ( S5–S7 Figs ) , they not only are at low frequencies but could also be detected as part of other lineages ( S8 Fig and S2 Table ) . To address if genetic drift and founder effects can be implicated in the loss L28 under slower rates of environmental change , we revived frozen stocks from the 7 replicate gradual populations at generation 35 ( Figs 1B and 7A ) , and performed a new set of evolution experiments at two different population size regimes , 104 and 2·103 , for 30 generations in constant high salt ( Fig 7A , see Materials and Methods and S1 Text section 1 . 5 ) . In this second set of experiments , we refer to each of the 7 gradual populations as ancestrals #1–7 ( S1 Table ) . Two main factors , prior genetic drift or selection , could lead to differences in the adaptive responses observed from each of the new ancestral populations , as well as between population size regimes . First , the best high salt lineage determined from the first set of evolution experiments , L28 , may have been lost by genetic drift before the second set of experiments started . The freezing and reviving process of the populations could also have resulted in L28 loss; in this case a population size bottleneck would cause a founder effect for the second set of experiments . The second factor is that the efficacy of selection on the best lineages should be lower because of stronger genetic drift in small populations [22] . At two time points during this second set of evolution experiments , a small number of SNPs across the genome were genotyped in pools of individuals , chosen to maximize the ability to distinguish lineage L28 in large samples ( S14 and S15 Figs ) . We then calculated the probability of a L28 sweep under the inferred fitness reaction norms of segregating lineages found above , given the pooled genotyping data ( S1 Textsection 1 . 12 , S16 and S17 Figs ) . Under our genotyping protocol and analysis , an L28 sweep does not imply its fixation during the time frame of experimental evolution–indeed , with deterministic dynamics we predict that after 30 generations at high salt L28 frequency would only reach 50% ( Fig 5 ) –nor are we able to determine if lineages other than L28 sweep through the populations . We found that the evolutionary responses from the 7 ancestral populations fell into four distinct categories ( Fig 7 ) . The first category demonstrates the consequences of a founder effect on adaptation to high salt since the L28 lineage did not sweep through any population ( Fig 7B and S16 and S17 Figs ) . Yet , from at least the first two ancestrals , another unidentified lineage appears to have responded more rapidly at large population sizes than at small population sizes , a result indicating higher selection efficacy at larger population sizes . From the third ancestral , we can only conclude that the L28 lineage was lost before starting the second set of experiments . A second category of responses more directly illustrates the effects of genetic drift on adaptation ( Fig 7C ) . From the fourth ancestral , the L28 lineage swept rapidly in the high population size regime , while at smaller population sizes the response was more restricted and L28 probably lost during evolution in high salt . The third category also illustrates the effects of genetic drift to adaptation , although in the opposite sense ( Fig 7D ) . From the fifth and sixth ancestrals , we find that the L28 lineage swept in a fraction of the populations , but exclusively in those with small population sizes . This seemingly puzzling result can be explained if one postulates that , together with L28 , the ancestor populations segregated at a relatively high frequency other unidentified lineages that were almost as fit as L28 in high salt ( such as lineage 13 , see S8 Fig ) . Interference at large population sizes could have transiently kept L28 at a lower frequency than that expected [27] , possibly promoting its extinction by genetic drift [28 , 29] . In contrast , at small population sizes the loss of high fit lineages ( or maintenance at very low frequencies ) by genetic drift might have in turn freed selection to sometimes favor L28 unconstrained . Despite population size regime , all populations derived from the seventh ancestral showed rapid sweeping of L28 ( Fig 7E ) . This indicates that L28 was initially at a relatively high frequency in the seventh ancestor , when compared to the fourth ancestor ( where L28 rapidly sweep only in the large population ) . At generation 35 of gradual experimental evolution , we observed that one of the constituent region-wide haplotypes of L28 was present in the seventh ancestor , while absent in the fourth ancestor , in line with the expected initial frequency differences in L28 between them ( S5–S7 Figs ) . However , without further genome-wide SNP sampling at high densities and sizes , we cannot assess how prior gradual evolution impacted L28 frequency for continued experimental evolution in constant high salt ( S16 and S17 Figs ) .
Adaptation to extreme environments under different rates of environmental change is expected to depend on ancestral GxE fitness variance and thus on the shape of fitness reaction norms and relative frequencies of extant genotypes . Ignoring the input from de novo mutations and of new recombinant genotypes during adaptation , our experiments in a gradually changing environment show that genotypes initially favored by selection are later selected against when they are overtaken by better genotypes as the environment becomes more extreme . Further , because adaptation to intermediate environments during gradual evolution decreases the frequencies of the genotypes that are most adapted to the extreme environments , these best genotypes can be lost before populations reach such extreme environments . During the last decade there has been a substantial effort in the development of inference methods to detect selection on DNA sequence diversity during experimental evolution [30–32] , although no prior work has explicitly dealt with changing environments . Without directly assaying fitness of each individual genotype , our approach allowed us to infer the distribution of standing GxE fitness variance , inference of both genotype frequencies and the genotypic effects across an environmental ( salt ) gradient . Based on the inferred distribution we could have predicted the outcome of selection under any rate of environmental change , although we only explored the experimental evolution regimes that were actually performed . Future studies could therefore investigate how different distributions of ancestral GxE fitness variance–in the amount of diversity and shape of reaction norms–determine the loss of genetic variance during environmental change and , for example , the mean population fitness lag load [2 , 11] . Our preliminary results indicate that independently of the specific parameterization of fitness reaction norms , slower environmental change transiently results in maladaptation and ultimately delays adaptation . Reaction norms with more flexible parameterizations , however , seem to generate complex fitness variance dynamics , presumably because genotypes favored at early periods can become neutral at other periods and then again positively selected at later periods . For example , the loss of fitness variance at intermediate salt levels is more pronounced under linear than quadratic functions , although by generation 50 the mean population fitness is actually higher under the linear than the quadratic model . Despite our approach allowing for arbitrary parameterizations of the reaction norms , one can of course argue that the decision to model particular reaction norm shapes should first hinge on an understanding of individual development and physiology in the relevant environments . The most obvious limitation of our inference method is that population size was not included as a parameter and thus we could not account for the effects of genetic drift . Such extension of the model would allow explicit predictions about the loss of genetic variance with variable population sizes and thus the probability of extinction to deteriorating environments , an especially important problem in the context of changing environments , e . g . , [33] . An approach by Nené and colleagues [34] , focused on the case of evolution of new haplotypes in a population via mutation and positive selection in a constant environment , could perhaps be adapted to detect selection in changing environments with stochasticity . They developed a phenomenological "delay-deterministic” model where an "effective" mutation rate was conditioned on the current frequency of the focal haplotype , with a given threshold mutation rate being parameterized to mimic the effects of genetic drift . Under a limited set of simulated data , the addition of the delay term to their deterministic model better reproduced the frequency dynamics and produced better estimates of selection coefficients . We anticipate , however , that methods explicitly accounting for stochasticity , for example Bayesian models estimated using MCMC techniques , will be necessary in order to manage computational constraints and allow for hypothesis testing and model fit evaluation . Another future extension of our approach should be to apply it to outcrossing haploids and diploids . The model could be adapted to account for mating and recombination by finding genomic regions at high to complete linkage disequilibrium during the relative short periods of experimental evolution and treating them as we did here the “region-wide haplotypes” ( RWH ) . But expanding our model with recombination represents a considerable challenge since it requires characterizing the degree of polygenicity for fitness [21] and whether or not accounting for dominance and epistasis is necessary [35] . With selection on new genotypes generated by recombination , as with de novo mutation , adaptive rates may increase if it takes longer for a population to reach extreme environments . The net effect of loss of genotypes during adaptation to intermediate environments and the production of new genotypes by recombination is not immediately clear [11] , and thus reconciling experimental evolution results that depend on standing genetic variation with and without recombination with those where adaptation occurs from de novo mutation is a major future task . Experimental evolution studies with microbes that depend on de novo mutation suggest that adaptive gains become smaller with each mutational event , and therefore that adaptation involves diminishing-returns epistasis for fitness [36 , 37] . Microbial experiments further indicate that slower environmental change allows more time for the exploration of mutational “space” and the possibility to fix mutations at intermediate environments that predispose subsequent fixation of additional mutations at more extreme environments . Such outcomes should depend on the empirical fitness relationship between alleles related by single mutational steps [38 , 39] . In the study of Gorter and colleagues [10] , under some stressors , slow environmental change retarded adaptation but not the fitness gains in the most extreme environments . In the study of Lindsey and colleagues [40] the populations that survived a sudden environmental change had higher fitness than those that survived a more gradual change , suggesting , just as in our experiments from standing genetic diversity , a key role of GxE fitness interactions . In changing environments , GxE fitness interactions appear to be sufficient to explain adaptation to extreme environments when evolution occurs from standing genetic variation ( without recombination ) , while both GxE interactions and epistasis are important when evolution occurs from de novo mutation . Clearly , we were unable to determine if epistasis played a role in adaptation since , by definition , there was only selection between non-recombining genotypes . Little theoretical work has focused on understanding the population genetics of adaptation from standing genetic variation in changing environments . An exception is the study by Matuszewski and colleagues [11] , which explored the distribution of fitness effects of fixed alleles starting from standing variation , and with mutational input , under a moving trait under stabilizing selection and epistasis for fitness . The trait was modeled as polygenic with additive interactions between alleles ( effectively a biallelic infinite-site and continuum of alleles model ) , with recombination rates following a Poisson distribution and cross-overs a uniform position along the genome . Matuszewski and colleagues found that populations facing a fast environmental change show larger trait changes than those facing a slow environmental change , due to increases in both the expected number of fixations and the expected trait effect per allele substitution . Although they did not analyze situations of an abrupt environmental change under complete linkage ( no recombination ) , as in our sudden evolution experiments , they nonetheless predicted a higher number of fixations under faster environmental change , and that adaptation would be deterred under slower environmental changes . Matuszewski and colleagues further found that while fast environmental change eliminates sets of de novo mutations , it also helps to keep standing genetic variation until it can be picked up by selection . On the other hand , under slow environmental change , most large effect alleles are already eliminated by genetic drift ( or stabilizing selection ) before they could contribute to adaptation . Although the mathematical assumptions of the model of Matuszewski and colleagues do not closely match our experimental conditions , some of their predictions are consistent with the results obtained . We found that slower environmental change allows populations to maintain more genotypes for longer than faster environmental change , and that this can compromise adaptation . Besides loss by genetic drift , one reason for compromised adaptation is that when fitness reaction norms cross , the fitness variance is reduced and adaptive rates diminished [16] . Previous demography , form of selection and degree of environmental variability will determine standing levels of genetic variation and thus from where along the environmental gradient adaptation will ensue [5] ( Fig 1 ) . If the population has already exhausted standing GxE variance , then the rate of environmental change will not affect the loss of relevant genotypes simply because they are not present in the population . Selective “interference” is yet another process that could in part determine hindered adaptation under slower environmental change , and that could also explain why the best genotype was not favored at high population sizes in some of the high salt continued evolution experiments . In this scenario , since slower environmental change can promote the maintenance of polymorphism for longer periods , it is possible that reduced selection efficacy on the best genotypes kept them at low frequencies and caused in turn their loss by genetic drift before populations reached the most extreme environment . With recombination , interference between the best genotypes should be diminished [28 , 29 , 41] , and hence adaptation to the extreme environments will probably not be constrained as when there is limited recombination . Selective interference has been theoretically and empirically studied for microbial evolution experiments in constant environments where the mutational supply is high enough for competing asexual lineages to interfere with each other and retard fixation of the best mutations [42 , 43] . Other findings posit an important role for interference and stochasticity in maintaining the long-term standing genetic variation in sexual organisms [29 , 41] , in particular those reproducing by self-fertilization and with greatly reduced effective recombination rates [44 , 45] . However , the importance of interference between beneficial genotypes and stochasticity in promoting their loss in changing environments remains to be explored . Understanding the outcome of selection in changing environments is complicated because the historical sequence of population genetic changes , recombination and mutational input will determine the way populations respond later in evolution . Since our experimental design used fixed standing genetic variation , with little opportunity for new mutations or recombinants , we were able to examine in isolation the interaction between the sequence of environmental change and the ancestral variation in fitness reaction norms . We demonstrated that under gradual environmental change the genotypes most adapted to the extreme environments do not rise to high frequency during the early periods at less extreme environments . This then opens the door for stochastic loss of genotypes by genetic drift and founder effects , as revealed by our continued evolution experiments . Ultimately , the combination of these processes results in greater adaptation under faster environmental change .
All populations employed are ultimately derived from a hybrid population of 16 wild isolates [19] , followed by 140 generations of laboratory domestication to a 4-day non-overlapping life-cycle under partial self-fertilization ( self-fertilization ) at census sizes of N = 104 at the time of reproduction [15 , 19] , and introgression and homozygosity of the xol-1 ( tm3055 ) sex determination mutant allele at high populations sizes for 16 generations to generate an ancestral population capable of reproduction only by self-fertilization [18] . Experimental evolution in changing environments has been previously reported ( Fig 1B , [18] ) . Large samples of the ancestral population were revived from frozen samples [46] , expanded in numbers and first larval staged ( L1s ) individuals seeded at the appropriate densities to three regimes . The “sudden” regime was characterized by the same conditions to which previous lab-adaptation occurred , except that the NGM-lite media ( US Biological ) where worms grew was supplemented with NaCl ( 305 mM ) from the start and for 50 generations ( 4 replicate populations; S1 Table ) . For the “gradual” regime plates were supplemented with increasing concentrations of NaCl from 33 mM at generation 1 to 305 mM NaCl at generation 35 and onwards until generation 50 ( 7 replicate populations ) . A “control” regime was maintained in the ancestral environmental conditions without any salt supplement ( 3 replicates ) . Individual hermaphrodites from the ancestor population and generation 10 , 35 and 50 from the sudden , gradual and control populations were handpicked for genotyping . All 7 replicate populations from the gradual regime at generation 35 were revived from frozen stocks , expanded in numbers for two generations , and then split into two regimes: large population sizes of N = 104 and small population sizes of N = 2·103 at the time of reproduction ( Fig 7A ) . From each of the 7 gradual populations at generation 35 , one replicate was maintained at large population sizes and three replicates were maintained at small population sizes . All populations were kept at constant 305 mM NaCl for 30 generations . Over 103 L1s were collected per population at generation 15 and 30 , for pool-genotyping . The ancestral population was thawed from frozen stocks and individuals reared for two generations at 25 mM NaCl before they were exposed to the three salt treatments: 25 mM , 225 mM or 305 mM NaCl ( Fig 6 ) . Following the usual culture protocol during experimental evolution , on the third generation , five Petri dishes per NaCl treatment were seeded each with 103 L1s . These five plates constituted one technical replicate , and there were four for each salt treatment . After 66 h , individuals were harvested and exposed to a 1 M KOH:5% NaOCl solution ( to which only embryos survive ) . After 16 h , debris was removed and the total number of live L1s estimated by repeated sampling of small volumes . Statistical analysis was done based on the log-transformed per-capita L1-to-L1 growth rate values , using a linear model with the assay environment as a categorical variable . For this , the assay environment for the i-th measurement is denoted as Ei , and given by: Ei = 0 , for 25 mM NaCl; Ei = 1 , for 225 mM NaCl and Ei = 2 , for 305 mM NaCl . In this way , the 25 mM NaCl is taken as the reference environment . The model then takes the form: ξi=β ( Ei ) =β0+β1I ( Ei , 1 ) +β2I ( Ei , 2 ) where I ( Ei , j ) is the indicator function: I ( Ei , j ) ={1 , ifEi=j0 , otherwise and β0 , β1 and β2 are coefficients to be estimated . The data was analyzed in R [47] , using the following formula to specify the model in the lm function: log ( growthRate ) ~ saltTreatment Least-square estimates of the expected log-growth rates in each of the three assay environments were then obtained using the R package lsmeans [48] . Note that for inferring fitness reaction norms only the ancestral fitness estimates at 25 mM and 305 mM were used ( see below ) . During experimental evolution in changing environments , one lineage ( whole-genome haploid haplotype ) swept through the sudden populations , while another lineage was initially sweeping though the gradual populations when they were at intermediate salt concentrations ( Fig 5 ) . From two gradual populations at generation 50 , we derived in [21] , by repeated single hermaphrodite self-fertilization for >10 generations , 100 “lines” which were the whole-genome sequenced . Comparing the >300k SNPs in the lines with the 761 SNPs collected during experimental evolution ( see below ) , we identified lines L28 and L11 as representatives of the lineages predicted to explain the experimental population dynamics ( S11 Fig and S2 Table ) . We also conducted absolute fitness assays for L28 and L11 ( Fig 6 ) , in a similar manner and replication as for the ancestral population , except that L1-to-L1 growth rate data were collected for two generations . Statistical analysis was done based on the log-transformed per-capita L1-to-L1 growth rate values , with the value obtained for the i-th measurement denoted as ξi . Since the data were gathered over two generations , we accounted for the potential presence of transgenerational effects by using a mixed effects model [49] , with environment , lineage and a transgenerational component as fixed effects , and assay block ( defined by when the lineages were revived from frozen stocks ) as a random effect: ξi=β ( Ei , Li ) +α ( Li ) gi+γ ( Bi ) where Ei denotes the assay environment ( Ei = 0 , for 25 mM NaCl; Ei = 1 , for 225 mM NaCl and Ei = 2 , for 305 mM NaCl ) , Li denotes the line ( L11 or L28; Li = 0 , for L28; Li = 1 , for L11 ) , gi corresponds to the transgenerational component ( described below ) and Bi is the assay block ( Bi ∈ {1 , 2 , 3} ) . Ei , Li and Bi are categorical variables , while gi is a continuous variable . In this model , the transgenerational value gi is given by: gi= ( ci−25305−25 ) ( ti−1 ) where ci is the NaCl concentration , in mM , and ti ∈ {1 , 2} is the generation assayed . The various terms of the model correspond to: i ) β ( Ei , Li ) , the statistical interaction between environment and line; ii ) α ( Li ) , the line-dependent transgenerational effect; and iii ) the intercept-based effect of block . The data was analyzed in R [47] , using the following formula to specify the model in function lmer from package lme4 [49]: log ( growthRate ) ~ saltTreatment * line + line * tGenComp + ( 1 | block ) With the R package lsmeans [48] being then used to obtain estimates of interest: ~ saltTreatment * line pairwise ~ line | saltTreatment In both cases , the estimates obtained do not include contributions of transgenerational effects ( by evaluating the model at gi = 0 , via parameter tGenComp = 0 ) . L28 and L11 were further assayed in head-to-head competitions ( Fig 6D ) . Lineages were revived and reared for two generations at 25 mM NaCl before they were set up at three NaCl concentrations: 25 mM , 225 mM and 305 mM . On the third generation , L1 larvae from the two lineages were mixed in 1:1 ratio , at a density of 103 L1s in each of two Petri dishes per replicate assay . Each replicate assay was maintained for two generations . At both the assay generations , L1 samples were collected for pool-genotyping of single nucleotide polymorphisms ( SNPs ) . Assays were performed in three blocks , with 3 replicates per salt concentration in each of two blocks , and 4 replicates in the third block . The data for analysis was based on the L28 and L11 SNP frequency values obtained after doing calibration curves where the ratio of both lines was known ( S12 Fig ) . For analysis , the estimated frequencies for L28 were forced to be in the interval ( 0 . 005 , 0 . 995 ) . To estimate relative fitness we calculated the selection coefficients of L28 with respect to L11 , for the three assay environments considered , using a mixed effects model per SNP [49] . Each model included salt treatment and generation as fixed effects , and replicates as a random effect: yi=β0+α ( Ei ) ti+γ ( Ri ) where yi is the logarithm of odds-ratio of the L28 allele: yi=log ( pi/ ( 1−pi ) ) where Ei denotes the assay environment ( Ei = 0 , for 25 mM NaCl; Ei = 1 , for 225 mM NaCl and Ei = 2 , for 305 mM NaCl ) , ti denotes the generation , and Ri is the replicate ( Ri ∈ {1 , 2 , ⋯ , 30} ) . The data was analyzed in R [47] , using the following formula to specify the model in function lmer from package lme4 [49] log ( OdssRatioL28Allele ) ~ generation : saltTreatment + ( 1 | replPop ) The selection coefficients in each of the three assay environments were obtained via the point estimates for the corresponding parameters of the model . Individual L4 genomic DNA was prepared with the ZyGEM prepGEM Insect kit following [20] . A total of 925 biallelic SNPs across the genome were assayed by iPlex Sequenom MALDI-TOF methods [50] . We chose the SNPs known to segregate in the lab adapted population , following [21] . Due to the limited amount of genomic DNA , each individual was assayed for two of the six C . elegans chromosomes , each pair of chromosomes being referred to as a region ( chromosomes I and II: region 1; III and IV: region 2; V and VI: region 3 ) . 64 L4s from the ancestral population and 16 L4s from each of the evolved populations at generations 10 , 35 and 50 were sampled per region ( 3 replicate control populations , 4 replicate sudden , 4 replicate gradual ) . Quality control was based on discarding SNPs with a high frequency of heterozygous calls , SNPs with a high frequency of genotyping failures ( > 30% ) , and individuals in which many SNPs failed genotyping ( > 25% ) . The 761 SNPs that passed quality control were imputed into chromosome-wide haplotypes using fastPHASE [51] . Genomic DNA from pooled samples was prepared using the Qiagen Blood and Tissue kit , and genotyped for 84 SNPs in chromosomes I , IV and V , using the iPlex Sequenom methods in 3 technical replicates for each SNP assay . In parallel , pooled gDNA was prepared to calibrate SNP L28 allele frequencies when mixed with L11 or the ancestor population at several known proportions ( 8–14 technical replicates each ) . After quality control , we retained 29 SNPs , 18 of which differentiating L28 and L11 . We interpolated expected L28 frequencies from the calibration curves ( S14 Fig ) , using Levenberg-Marquardt algorithm in R package minpack . lm [52] . For the principal component analysis of the matrix containing the frequency of the alternative alleles in each sample ( Fig 7 ) , the function prcomp in R was used . We model an asexual population of a haploid organism , and consider deterministic environmental and population dynamics , discrete non-overlapping generations and viability selection , with the only environmentally-relevant variable being the NaCl concentration . We assume an infinite population size , such that any given lineage ( genome-wide haploid haplotype ) never goes extinct , and that there are no density- or frequency-dependencies , and that transgenerational effects are absent . Following for example ref . [25] , a population is composed of G lineages , such that the frequency of the k-th lineage in generation t + 1 , denoted by gk ( t+1 ) , is given by: gk ( t+1 ) ∝λk ( x ( t+1 ) ) gk ( t ) [1] where x ( t ) is the environment value faced in generation t , and λk ( x ) the expected number of live offspring produced by lineage k when faced with the environment x . The function λk ( x ) thus defines the fitness reaction norm for lineage k . Following the genotyping setup , the genome is divided into L non-overlapping regions , and we refer to the haplotype in a region as a region-wide haplotype ( RWH ) . A “lineage” k is described by a tuple Sk , indicating the RWHs in each region , such that Sk = ( lk , 1 , lk , 2 , ⋯ , lk , L ) , and where lk , i is the RWH located in region i in lineage k . We assume that the fitness reaction norm of a lineage is an additive function of the fitness reaction norms of the RWHs in that lineage such that: ξk ( x ) =log ( λk ( x ) ) =log ( λ ( x|Θ , Sk ) ) =∑l∈Skf ( x|θl ) , f ( x|θl ) ∈R [2] where Θ is a vector of parameters for the region-wide haplotypes , θl the parameters for RWH l , and f ( x|θl ) the parametric function describing the fitness reaction norm for a single RWH . We here consider f ( x|θl ) to be a linear f ( x|θl ) = alx + bl , such that θl = ( al , bl ) or quadratic function f ( x|θl ) = alx2 + blx + cl , such that θl = ( al , bl , cl ) of the environmental value x . Given genotyping data at H time-points plus the ancestral , we consider distinct epochs of the experimental evolution , evaluated at generations T0 , T1 , ⋯ , TH ( such that T0 = 0 , T1 = 10 , T2 = 35 and T3 = 50 ) . To denote the epoch to which a certain variable corresponds , a superscript inside square brackets is used . For a single population , the frequency of lineage k in epoch h , denoted by gk[h] , follows from the frequencies of the lineages in the previous epochs: gk[h]∝exp ( ∑t=1+Th−1Thξk ( x ( t ) ) ) gk[h−1] , h=1 , 2 , ⋯ , H [3] where x ( t ) is the environment faced in generation t . The ancestral population , consisting of G lineages , is described by two variables: A = ( S1 , S2 , ⋯ , SG ) , corresponding to the RWHs present in each lineage; and g[0]= ( g1[0] , g2[0] , ⋯ , gG[0] ) , specifying the frequency of each lineage ( such that ∑k=1Ggk[0]=1 ) . For inferring the lineage fitness reaction norms , λk ( x ) , we consider that A and g[0] are known . Since this is not the case in the analysis of the experimental data , we sample the pair ( A , g[0] ) , given the experimental data , and then estimate the RWH parameters Θ , repeating these two steps multiple times ( sections 1 . 7 . 6 and 1 . 7 . 7 of the S1 Text ) . Under the population genetics model used , all replicate populations within a single evolutionary regime c have the same dynamics of the lineage frequencies gk[h] . Let Xc= ( Xc[1] , Xc[2] , ⋯ , Xc[H] ) denote the sequence of environmental values in regime c , where Xc[h]= ( x ( t1[h] ) , x ( t2[h] ) , ⋯ , x ( tTh−Th−1[h] ) ) , ti[h]=i+Th−1 . Inference is framed in a maximum likelihood context , with contributions from each evolutionary regime , given the fitness and genotyping data . We consider without loss of generality that fitness and genotyping data are available for all epochs T0 , T1 , ⋯ , TH for each regime . The case in which data is available only for certain epochs is treated by evaluating the corresponding likelihood function only for those epochs . The S1 Text details how the input data , at the level of the replicate populations , is converted to that at the level of each regime . Let Wc= ( Wc , 1 , Wc , 2 , ⋯ , Wc , NE ) denote the fitness data on regime c , with NE assay environments , with xm being the environmental value , and ϕc , m[h] the observed population-averaged fitness value of a population from regime c in epoch h in the m–th assay environment . We assume a log-normal model for noise in the observed values ϕc , m[h] . The log-likelihood for the RWH parameter vector Θ given the fitness data on regime c is then: LW ( Θ|Wc , Xc , A , g[0] ) ∝−∑h=0H∑m=1NElog2 ( 1ϕc , m[h]∑k=1Gλk ( xm ) gk[h] ) [4] Let Dc= ( Dc[1] , Dc[2] , ⋯ , Dc[H] ) be the genotyping data on regime c ( note that it does not include the data on the ancestral ) , such that Dc[h]= ( nc , l1[h] , nc , l2[h] , ⋯ , nc , lM[h] ) , where nc , l[h] is the number of copies of RWH l that were observed in epoch h in regime c . Then , the log-likelihood given the genotyping data on regime c is given by: LD ( Θ|Dc , Xc , A , g[0] ) ∝∑h=1H∑lnc , l[h]log ( ∑k=1GI ( l , Sk ) gk[h] ) [5] where I ( l , Sk ) is an indicator function , equal to 1 if lineage k has RWH l , or equal to 0 otherwise . Considering all evolutionary regimes C , the log-likelihood is then obtained by combining Eqs [4] and [5]: ∑c∈CLW ( Θ|Wc , Xc , A , g[0] ) +LD ( Θ|Dc , Xc , A , g[0] ) [6] Model fitting is then performed by maximizing Eq [6] , using a gradient-based optimization algorithm , starting from random initial conditions . All data and code for analysis has been archived in Dryad . org: doi:10 . 5061/dryad . 76n6f7c . The archive consists of the following sets of files , each with a README . md for instructions on setting up the analysis and running the code: 1 ) input_data-genotp_data_NaCl . zip: raw genotyping data on the initial NaCl experiment ( 50 generations ) ; 2 ) analysis_code-genotp_data_NaCl . zip: R code for preparing and summarizing the genotyping data on the initial NaCl experiment in changing environments . 3 ) input_data-growth_rate_data_NaCl . zip: raw growth-rate data on the ancestral population and the lines L28 and L11; 4 ) analysis_code-growth_rate_data_NaCl . zip: R code for the analysis of the growth-rate data . This is necessary for inference of the RWH parameters and the lineages , since the inference relies on fitness data on the ancestral population . 5 ) analysis_code-inferring_RWH_params . zip: R code for the inference of RWH parameters and the lineages , given the genotyping data during the NaCl experiment and the fitness data on the ancestral . 6 ) analysis_results . zip: the overall results of the analysis in the paper , which was the source for the figures; 7 ) input_data-genotp_data_NaCl_continuation . zip: raw genotyping data for the second set of experiments ( 30 generations ) ; 8 ) analysis_code-genotp_data_NaCl_continuation . zip: R code for the analysis of the data on the second set of experiments . | Adaptation under environmental change is expected to depend on the time available for the sequential fixation of mutations , but also on standing variation in genotype-by-environment fitness interactions . In the later circumstances , some genotypes might be initially favored but then disfavored and overtaken by other genotypes that are better at more extreme environments if they were not lost by genetic drift or founder effects in the meantime . We addressed this idea with experimental evolution in Caenorhabditis elegans populations with standing variation for genotype-by-environment fitness interactions and by developing a model to describe natural selection on extant genotypes during experimental evolution . We find that under slower environmental change , the genotypes that are initially selected are not the best at the most extreme environments , and as a consequence , that these best genotypes can be lost due to genetic drift and founder effects . We further find that the longer polymorphism is maintained the more likely that selective interference will reduce the best genotypes to low frequencies and increase the chances for their loss through genetic drift . We conclude that under slower environmental change adaptation will be deterred if populations can only rely on standing genetic variation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"population",
"genetics",
"genetic",
"mapping",
"evolutionary",
"adaptation",
"population",
"biology",
"molecular",
"biology",
"techniques",
"genotyping",
"research",
"and",
"analysis",
"methods",
"genetic",
"polymorphism",
"molecular",
"biology",
"evolutionary",
"genetics",
"genetic",
"drift",
"population",
"metrics",
"haplotypes",
"population",
"size",
"heredity",
"genetics",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"evolutionary",
"processes"
] | 2018 | Slower environmental change hinders adaptation from standing genetic variation |
Rabies virus ( RABV ) is enzootic throughout Africa , with the domestic dog ( Canis familiaris ) being the principal vector . Dog rabies is estimated to cause 24 , 000 human deaths per year in Africa , however , this estimate is still considered to be conservative . Two sub-Saharan African RABV lineages have been detected in West Africa . Lineage 2 is present throughout West Africa , whereas Africa 1a dominates in northern and eastern Africa , but has been detected in Nigeria and Gabon , and Africa 1b was previously absent from West Africa . We confirmed the presence of RABV in a cohort of 76 brain samples obtained from rabid animals in Ghana collected over an eighteen-month period ( 2007–2009 ) . Phylogenetic analysis of the sequences obtained confirmed all viruses to be RABV , belonging to lineages previously detected in sub-Saharan Africa . However , unlike earlier reported studies that suggested a single lineage ( Africa 2 ) circulates in West Africa , we identified viruses belonging to the Africa 2 lineage and both Africa 1 ( a and b ) sub-lineages . Phylogeographic Bayesian Markov chain Monte Carlo analysis of a 405 bp fragment of the RABV nucleoprotein gene from the 76 new sequences derived from Ghanaian animals suggest that within the Africa 2 lineage three clades co-circulate with their origins in other West African countries . Africa 1a is probably a western extension of a clade circulating in central Africa and the Africa 1b virus a probable recent introduction from eastern Africa . We also developed and tested a novel reverse-transcription loop-mediated isothermal amplification ( RT-LAMP ) assay for the detection of RABV in African laboratories . This RT-LAMP was shown to detect both Africa 1 and 2 viruses , including its adaptation to a lateral flow device format for product visualization . These data suggest that RABV epidemiology is more complex than previously thought in West Africa and that there have been repeated introductions of RABV into Ghana . This analysis highlights the potential problems of individual developing nations implementing rabies control programmes in the absence of a regional programme .
Viruses belonging to the genus Lyssavirus , family Rhabdoviridae , cause the disease rabies . Rabies virus ( RABV ) is enzootic throughout Africa with the domestic dog ( Canis familiaris ) being the principal vector [1] . Sylvatic rabies is also reported in a number of wildlife hosts , particularly in southern Africa [2] , [3] , [4] , [5] . Rabies remains the only disease known to have a 100% mortality rate and has a high DALY ( disability adjusted life years ) score compared with other ‘neglected zoonoses’ [1] , [6] , [7] . Dog rabies is estimated to cause 24 , 000 ( 7000–46000 , 95% percentiles ) human deaths per year in Africa [1] , however , this figure is still considered to be a conservative estimate as rabies cases in humans are widely under-reported in parts of Africa [8] , [9] . Rabies has been present within the dog population of Ghana for decades [10] , [11] . Previously , control methods including dog vaccination and stray dog removal have been intermittent and not sustained . Unfortunately , as in several other developing African countries , rabies diagnostics within the Ghanaian veterinary services remains limited to non-Lyssavirus species specific staining techniques , including the Sellers' stain and fluorescent antibody test ( FAT ) [12] . Currently , only individual owners vaccinate their dogs for their ( owner and dog ) protection . Between 1970 and 1974 , an average of 72 cases of canine rabies were reported annually throughout the country [10] . Between 1977 and 1981 this number increased to over 100 cases annually , with an incidence of human rabies cases rising to 27 in 1981 [11] . Since 1981 there have been no further published reports of rabies in Ghana , and rabies viruses from the country have not been included in phylogenetic analyses of rabies in Africa [13] , [14] . The virus is believed to cause disease in approximately 0–60% of those patients that are exposed depending on route of exposure [8] . Despite this , 123 clinically-confirmed human cases were recorded by public health officials between 2000 and 2004 ( unpublished results ) . Moreover , ‘suspect’ human rabies cases are rarely confirmed using a laboratory-based diagnosis , relying solely on a clinical diagnosis [9] . The first phylogenetic study of rabies viruses from sub-Saharan Africa established three genetically distinct lineages ( Africa 1 , 2 , and 3 ) [15] . Sub-lineage Africa 1a dominates northern and eastern Africa , but has also been detected in Nigeria , Gabon and Madagascar , suggesting a very broad distribution . Sub-lineage 1b is found in eastern , central and southern Africa and lineage 2 is present in an uninterrupted band across West Africa as far east as Chad [13] , [16] . Africa 1 and 2 lineages have been detected in a range of domestic and wild carnivore species . While domestic dogs appear to be the only population essential for maintenance of canid variants in some parts of Africa [17] , [18] , wild canids have been suggested to contribute to sustaining canine rabies cycles in specific geographic loci in South Africa and Zimbabwe [19] , [20] , [21] . A third lineage ( Africa 3 ) is thought to be maintained within viverrid species in southern Africa [22] , [23] , [24] . This phylogenetic distinction has been supported by studies investigating rabies across Africa [13] , [25] , epidemiological studies of rabies within specific countries [3] , [16] , [18] , [26] , studies on wildlife populations [5] , [27] , [28] and investigations into the origin of human rabies [29] , [30] . More recently another distinct lineage , Africa 4 , has been identified in northern Africa [31] . The principal objectives of this study were to characterise the lyssaviruses causing rabies in Ghana and to understand the evolutionary history of the circulating viruses . We also assessed the performance of a novel isothermal amplification technique for the detection of rabies virus for use in African laboratories . The low threshold of technology required to use this technique for diagnosis of animal diseases in Africa has been advocated [32] , [33] .
The Republic of Ghana is on the southern coast of West Africa ( Figure 1 ) . It shares borders with Togo ( east ) , Ivory Coast ( west ) , and Burkina Faso ( north ) . Ghana has several ecosystems broadly attributed to the patterns of rainfall and geological topology [34] . The south eastern coastline consists of mostly low plains and scrubland , and separates the upper and lower Guinea African forest systems . Southwest and south central Ghana is a semi-deciduous forested plateau . Savannah dominates the northern part of the country . There are geographical features that may represent barriers to rabies spread in Ghana . The highest point in Ghana is only 885 m above sea level along the eastern border , however , the world's largest artificial lake , Lake Volta , separates much of eastern Ghana from the rest [34] . Ghana's population has rapidly increased in the last few decades . A census in 1961 recorded 6 . 7 million people , however , the current estimate is approximately 24 million [35] . Brain samples were derived from dogs ( 74 ) and cats ( 2 ) brought to the central diagnostic veterinary laboratory ( Veterinary Services Laboratory , VSL ) in the capital of Ghana , Accra , on suspicion of being rabid ( Table S1 ) . The samples used in this study were obtained by the Ghanaian government's veterinary services laboratory from naturally infected rabid animals in Ghana . No samples were obtained from , nor animals used in , an experimental study . All samples were obtained from animals within 142 km of Accra . Infection with RABV was suspected from clinical signs and from test results using either Sellers' staining ( n = 69 ) of Negri bodies or the FAT ( n = 7 ) in the VSL [12] . The panel were assigned numbers randomly and transferred from the VSL to the Veterinary Laboratories Agency ( VLA ) , Weybridge , UK , where further molecular analysis was undertaken . Total RNA was extracted from each brain sample using Trizol ( Invitrogen ) following the manufacturer's protocol . Pellets were resuspended in 10 µL of HPLC grade water . Reverse transcription and polymerase chain reaction were performed using previously published methods to amplify a 600 bp region of the nucleoprotein gene [36] . A novel reverse-transcription loop-mediated isothermal amplification assay ( RT-LAMP ) was applied to a limited panel of ten samples systematically taken from the larger randomly numbered Ghanaian panel . Previous reports applied this technique to viruses from a range of countries [37] , [38] or to fixed rabies virus [32] . The assay is composed of two sets of primers ( Table 1 ) . The first , designated Rab1 , amplifies viruses belonging to the cosmopolitan lineage . The second , Rab4 , amplifies viruses belonging to the arctic lineage . A reaction mixture incorporating a combination of all 12 primers amplifies viruses from both groups ( data not shown ) . 1 µg of each RNA sample was added to a reaction mixture containing each of the 12 primers at the final concentration indicated in Table 1 , Isothermal Mastermix ( GeneSys Ltd ) and 0 . 12 units Thermoscript reverse transcriptase ( RT ) ( Invitrogen ) in a final reaction volume of 25 µl . A cosmopolitan RABV obtained from a Turkish dog that had been used to develop the assay ( data not shown ) was included as a positive control . A no-template control sample ( HPLC grade water ) was used as a negative control . The reaction was incubated at 65°C for 1 hour . A 10 µl aliquot was removed and mixed with 2 µl sample loading buffer and loaded onto a 1% agarose gel containing ethidium bromide and separated at 80 volts for 1 hour . The amplification products were visualized by UV irradiation . The RT-LAMP assay was further adapted for use with a lateral flow device ( LFD ) for visualization of RT-LAMP products . The assay was run with the above conditions and reagents , but with the alternative loop primer sets ( Table 1: Rab1 FLOOPFlc , Rab1 BLOOPBtn , Rab3 FLOOPFlc , Rab3 BLOOPBtn , Forsite Diagnostics ) . The LFD ( Forsite ) uses a mouse anti-biotin monoclonal antibody ( MAb ) in the “get wet” strip to indicate the LFD run succeeded and a mouse anti-fluorescein MAb to bind the LAMP product to the fluorescein tag to show a positive result . The product was diluted in 1∶500 volumes of HPLC grade water and 60 µl added to the LFD test well . Direct consensus DNA sequencing of a 405 bp region of the nucleoprotein ( N ) gene was undertaken as previously described [39] . Sequences produced were edited using SeqMan ( DNAstar Lasergene ) and aligned ( ClustalW , Megalign , DNAstar Lasergene ) . Further analysis of the newly derived sequences was undertaken using Bayesian Markov chain Monte Carlo ( MCMC ) phylogenetic analysis using BEAST software ( version 1 . 6 . 1 ) [40] with a panel of pan-African RABV selected from GenBank ( Table S2 ) . Sequences were aligned in ClustalX2 ( version 1 . 2 ) . A relaxed-clock ( uncorrelated lognormal ) [41] was employed in conjunction with a general time reversible ( GTR ) model of substitution with gamma distributed variation in rates amongst sites and a proportion of sites assumed to be invariant . This method allows the evolutionary rate of each branch to vary without assuming these rates are correlated among adjacent branches . A model of constant population size was employed for the phylogeographic analysis , motivated by a preliminary analysis of the data using a non-parametric model of growth under which suggested no significant deviation from the constant size . The MCMC was run for 30 , 000 , 000 steps with parameters and trees sampled every 6 , 000 steps . Parameter effective sample sizes were >100 and posterior distributions were inspected to ensure adequate mixing in Tracer ( version 1 . 5 ) . A phylogeographic approach was not taken to analyze the correlation between lineage and distance , due to all animals reportedly originating within close proximity from central Accra . To infer the temporal and spatial diffusion of Africa 1 and Africa 2 clades into Ghana , a continuous-time Markov chain ( CTMC ) process over discrete sampling locations was employed in a phylogeographic analysis of each clade using BEAST . The sampling origin for each sequence was considered to be the centroid of the country from which the sequence was sampled [14] . The same models of nucleotide substitution , growth and clock rate were employed as before , but an MCMC chain length of 100 million steps was used to ensure sufficient mixing and convergence of all phylogeographic parameters , and trees were logged every 20 , 000 steps . An appropriate ( maximum 10% ) burn-in was removed from each and the sampled trees were summarized as maximum clade credibility ( MCC ) trees . All sequences reported in this study ( Table S1 ) were deposited in GenBank .
Seven of the 69 samples from suspected rabies cases tested at the VSL Accra were negative by Sellers' stain , whereas each of the seven tested by FAT was positive . Due to clinical signs exhibited by the animals , all 76 samples were included for further analysis at VLA-Weybridge and were subsequently positive by RT-PCR for RABV . Sequence analysis demonstrated that all viruses belonged to lineages previously reported from Africa . Twenty-seven samples were from the Africa 2 lineage , 48 samples from the Africa 1a sub-lineage , and a solitary sequence ( sample G13 ) belonged to the Africa 1b sub-lineage ( Table S1 , Figure 2 ) . The MCMC tree of a 405 bp region of the 76 RABV N gene sequences analyzed with 20 African RABV sequences from GenBank is shown in Figure 2 . The topology is similar to other analyses of African RABV N genes [15] that included Africa 1 , 2 , and 3 lineages . Rabies viruses from Ghana clearly form two lineages , Africa 1 ( 49 viruses ) and 2 ( 27 viruses ) . Within each lineage sequences are separated into sub-lineages , in the case of Africa 1 , or clades in that of the Africa 2 lineage . Our analysis estimates that the Africa 2 lineage diverged approximately 181 years ago ( 73–313 yrs , 95% HPD ) . Within the Africa 2 lineage we detected three clades in the sample of viruses from Ghana . In order to test the hypothesis that these clades entered Ghana from different West African countries and to understand these viruses' evolutionary history , we re-analyzed the Africa 2 sequence data with 139 Africa 2 sequences alone , including the eleven used previously ( Table S2 , Figure 3 ) . Thirteen Africa 2 viruses form a clade with a virus from Benin , with a time to the most recent common ancestor ( TMRCA ) estimated between 23 and 73 years ( 95% HPD ) and there is a considerably higher posterior probability ( 0 . 442 ) for the ancestor of this clade to have originated in Benin than any other sampled country ( Figure 3 ) . A further thirteen Africa 2 viruses form a clade with viruses from Niger and Burkina Faso , with a TMRCA estimated to be between 22 and 53 years . It is most likely that this clade entered Ghana from Niger ( posterior probability = 0 . 464 ) . A single Africa 2 virus ( G6 ) shares a common ancestry with viruses from Ivory Coast and Burkina Faso and has a more recent ancestry of between 1 and 20 years . There is very high support for the ancestor of this clade to have originated in the Ivory Coast , before entering Ghana . Phylogeographic analysis of the newly sequenced Ghana Africa 1 sequences with pan-African 1 sequences ( Table S2 , Figure 4 ) confirmed a monophyletic group of Africa 1a viruses ( Figure 4 ) . This clade is estimated to have emerged 23–31 years ago from Gabon ( posterior probability = 0 . 944 ) . The spatial analysis also provides high support for the introduction of the single Africa 1b virus from Kenya ( posterior probability = 0 . 937 ) 15–22 years ago ( 95% HPD ) ( Figure 4 ) . For ten randomly selected samples from the cohort , RT-LAMP detected RABV from each sample with a similar banding pattern to the positive control when separated by agarose gel electrophoresis ( Figure 5 ) or when biotinylated products were applied to a LFD ( Figure 6 ) . This group comprised three Africa 2 and seven Africa 1 viruses ( Table S1 ) . The cost of this assay was calculated at approximately $3 per assay .
Each of the 76 brain samples used in this study was positive for RABV antigen . The overall topology of the phylogenetic tree produced by our analysis of the RABV N-gene sequence data available from a sample of rabid African dogs and cats in Ghana was consistent with those previously described [13] , [15] , [42] . This analysis of Ghanaian rabies cases is the first phylogenetic analysis of RABV from Ghana . Where this analysis is distinct from reports of RABV in other West African nations is in the diversity of viruses detected within Ghana . The samples were all taken from a relatively small geographical region with those samples not from within the greater Accra region originating from towns relatively close to Accra . These included eight viruses from Tema and five from Cape Coast ( 25 and 142 km from Accra , respectively ) . There was no evidence of infection with Africa 3 RABV ( detected in mongoose in southern Africa ) [22] , [23] , [24] , Africa 4 RABV ( detected in north-eastern Africa ) [31] or other Lyssavirus species such as Lagos bat virus , against which a high seroprevalence of antibodies has been detected in bats from Accra [43] . However , our analysis suggests that rabies epidemiology is much more complex than at first thought from previous studies within West Africa . Indeed , whilst West African countries typically have defined lineages circulating within them , only Nigeria and the Central African Republic have previously been described as having Africa 1 and 2 lineage viruses co-circulating within their national borders [13] , [16] . We detected both in Ghana , and propose that Ghana's recent history and geography may explain why both virus lineages were detected . Africa 2 viruses appear to have been present within the dog populations of West Africa , including Ghana , for decades . This is derived from the close relationships between the RABV characterized in Ghana and those reported in other West African countries , such as Benin , Ivory Coast , Burkina Faso and Niger . Our results support the findings of others that the Africa 2 virus lineage has been circulating within Africa for less than 200 years [13] . Within Ghana , our analysis suggests the Africa 2 clades now co-circulating in Ghana have different evolutionary histories . From the Africa 2 phylogenetic analysis ( Figure 3 ) , we hypothesize that the three Ghanaian Africa 2 clades co-circulate in Ghana , but share evolutionary histories with viruses from other West African countries . Whilst we cannot be certain of the direction of the virus spread , we believe that there have been three different introductions of Africa 2 viruses to Ghana . We found support for the hypothesis that one clade that circulates in Ghana and in the northeasterly West African countries of Niger and Burkina Faso was originally imported from Niger and subsequently entered both Ghana and Burkina Faso ( Figure 3 ) . Another clade of viruses share a common ancestry with a Beninese isolate from the east and likely entered the country from Benin or via neighboring Togo . The evolutionary history of those viruses from the east and northeast may be due to Lake Volta providing a physical obstacle to virus transmission between dog populations . Further analysis of this phylogenetic relationship is precluded , however , by the lack of additional published sequences from Benin , and none from neighboring Togo . A single virus , G6 , forms a clade with isolates from the Ivory Coast . This virus appears to be a recent introduction , sharing a TMRCA of just 1 to 20 years with viruses from the Ivory Coast to the west . A possible reason for fewer viruses being from the Ivory Coast may be the large tropical forest system along the Ghana-Ivory Coast border providing a barrier to dog movements . The border with the Ivory Coast was historically the most forested area of Ghana , however rapid deforestation and increasingly easy “between country” travel may have led to the trans-boundary movements of this virus . Due to the historical dominance of Africa 1 viruses in the northern , eastern and southern parts of Africa , we believe it reasonable to hypothesize that Africa 1 viruses have entered Ghana from those regions , and that transmission has not been from Ghana to those regions . This hypothesis is supported by the phylogeographic analysis which suggests that the virus sub-lineage Africa 1a was transmitted from central African counties to Ghana . If we accept this , the origin of the Ghanaian Africa 1a sub-lineage viruses may be explained simply by virus transmission through dog ( and potentially other vector ) populations from central African nations to Ghana ( Figure 4 ) . Indeed , in our analysis the Ghanaian Africa 1a viruses share an ancestry with a virus from Gabon with a TMRCA estimated to be 23–31 years ago . This would require viruses to be transmitted at an approximate rate of between 39 to 53 kilometers per year . The large number of Africa 1a viruses in our sample suggests that this sub-lineage is well established in the Accra region , however further virus sequences from nations between Ghana and Gabon are required to confirm the evolutionary history of this sub-lineage . The presence of an Africa 1b sub-lineage RABV in our analysis is the first reported from West Africa . Analysis of the Africa 1 lineage viruses suggests that this virus shares an ancestry with viruses from East Africa , in particular , those from Kenya ( Figure 4 ) . The presence of this virus may be explained in one of two , not exclusive , ways . Firstly , sub-lineage 1b viruses may simply have been transmitted within the populations of dogs and other susceptible animals from eastern African countries to Ghana . Transmission from Kenya ( with Nairobi approximately 4200 km from Accra ) would require virus transmission at a rate of approximately 190–279 kilometers per year with the TMRCA estimated to be 18 years ( 15–22 years , 95% HPD ) . Given the distance infected dogs and potential wildlife hosts may travel , this is theoretically possible , but highly unlikely given that rabies spread in red foxes and raccoons in Europe and North America was estimated to be typically 30–60 kilometers a year [44] , [45] . Therefore , we hypothesize that the more likely reason for this virus' presence in Ghana is that an infected animal was translocated from the east , thus introducing a new sub-lineage to the region . Indeed , we believe that this may be the first report of molecular evidence of a long distance translocation of a rabies sub-lineage in Africa . Spatio-temporal models of rabies in eastern and southern Africa show large-scale synchrony of rabies epidemics across both regions [46] . The analysis by Hampson et al provided evidence that movement of infectious animals , or animals in the incubation period , and localized regional or national vaccination campaigns during epidemics , are likely to lead to rabies synchrony [46] . However , evidence provided by rabies control programmes in both Europe and the Americas show that large-scale control programmes can be successful [47] , [48] , [49] , [50] . A study of rabies in Tanzania also suggested dog rabies control was feasible , but was hampered by perceived problems that were largely unfounded [7] . A subsequent analysis by Hampson et al suggested that regular regional pulsed vaccination programmes would be required to eliminate dog rabies [51] . Despite the analysis estimating the basic reproductive rate of domestic dog rabies throughout the world to be low ( R0<2 ) , the rapid turnover of dog populations led to enough susceptible hosts for rabies to be maintained [51] . Our molecular study suggests introductions of RABV from neighboring countries into Ghana are not infrequent , demonstrating that without substantial support for continuous vaccination or coherent regional cooperation , Ghana will be unable to eliminate rabies and maintain a rabies-free status . In addition to this , our analysis provides evidence of a virus that shares a recent common ancestry with viruses from East Africa , therefore providing further evidence that regional control programmes must be implemented and that once rabies is eliminated , vigilance and technical expertise must be maintained in order for new introductions to be controlled [46] . Currently rabies diagnostics within the Ghanaian veterinary services remain limited to non-Lyssavirus species specific staining techniques , including the Sellers' stain and , when FITC conjugate is available , FAT . Inadequate government and financial commitments and a resource limited veterinary infrastructure are restrictive factors that preclude a sustainable rabies diagnostic service in Ghana . Surveillance activities should be given a higher priority to maintain an effective diagnostic service with the co-operation of other national and international organizations . Each of the 76 brain samples used in this study was positive for RABV infection by RT-PCR at VLA Weybridge . Of the 76 samples full histories were available for 72 positive rabies cases . However , seven samples were negative when tested by Sellers' stain at the VSL . The VSL recorded 66 humans being bitten by those 72 dogs for which histories were recorded ( data not shown ) , including six bites to humans by the seven RABV positive cases that tested negative in the VSL . Further training and the availability of FITC conjugate for the FAT or use of the direct rapid immunohistochemical test ( dRIT ) [12] , [52] , [53] may have overcome some of the diagnostic problems . However , given that low cost isothermal RT-LAMP assays have been developed for a number of viruses affecting livestock in Africa , including Rift Valley Fever virus [33] and African Swine Fever virus [54] , we developed and tested the RT-LAMP for use in African laboratories . The RT-LAMP may be prone to some of the same problems as other molecular techniques , such as cross-contamination , however it is a cheap molecular technique that produces a product that is available for further analysis such as sequencing of the approximately 200 bp product . We developed the novel RT-LAMP on randomly selected RABV samples , including both Africa 1 ( a cosmopolitan ) and 2 lineages . This assay successfully amplified viral genetic material producing a measurable DNA product for both Africa 1 and 2 lineage viruses . This isothermal diagnostic assay negates the need for thermal-cyclers for molecular diagnosis of RABV . The assay reagents costs approximately $3 per assay and therefore may prove a useful alternative assay for those laboratories that already have molecular expertise and adds to the range of rapid cost-effective diagnostic assays that will be fundamental if developing countries wish to develop their own RABV diagnostic capabilities . Whilst “snap test” LFD tests have previously been reported [55] our adaptation of the RT-LAMP assay to use an LFD platform , instead of UV illumination , further reduces the technology required for RABV diagnosis in African laboratories . Additional validation of this method will require comparison with the gold standard assays , assessment of larger panels of samples from throughout Africa , as well as evaluation of its sensitivity in detecting RABV in brain samples from OIE reference laboratories . These preliminary findings , however , demonstrate proof-of-concept and suggest that this technique has the potential to provide African laboratories with a cheap and rapid molecular detection method . We conclude that our analysis of rabies virus sequences derived from Ghana has furthered the understanding of RABV epidemiology in West Africa . In particular , our analyses suggest that both Africa 1 and Africa 2 RABV lineages are present in Ghana . Africa 1b sub-lineage had previously not been reported in West Africa , and its detection , along with evidence of an additional four further clades circulating in Ghana support previous analyses that suggest that only sustained regional level approaches to rabies control will be successful in rabies elimination . In addition , we have developed an African RABV RT-LAMP assay , which can be adapted for use with LFD platforms that we advocate will provide an additional diagnostic tool for African regional laboratories . | Rabies virus ( RABV ) is widespread throughout Africa , with the domestic dog being the principal vector . Dog rabies is estimated to cause 24 , 000 human deaths per year in Africa , however , this estimate is still considered to be conservative . Two sub-Saharan African RABV lineages ( Africa 1 and 2 ) are thought to circulate in western and central Africa . We confirmed the presence of RABV in a cohort of 76 brain samples obtained from rabid animals in Ghana collected from 2007 to 2009 . In addition we developed and tested a novel molecular diagnostic assay for the detection of RABV , which offers an alternative RABV diagnostic tool for African laboratories . Our analysis of the genetic sequences obtained confirmed all viruses to be RABV , however , unlike previous studies we detected two sub-Saharan African RABV viruses ( Africa 1 and 2 ) in this cohort , which included a single virus previously undetected in West Africa . We suggest that there has been repeated introduction of new RABVs into Ghana over a prolonged period from other West African countries and more recently from eastern Africa . These observations further highlight the problems of individual developing nations implementing rabies control programmes at a local , rather than regional level . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"virology/vaccines",
"virology/diagnosis",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"virology/animal",
"models",
"of",
"infection",
"virology",
"virology/emerging",
"viral",
"diseases",
"infectious",
"diseases/viral",
"infections",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases"
] | 2011 | Evolutionary History of Rabies in Ghana |
A key to the pathogenic success of Mycobacterium tuberculosis ( Mtb ) , the causative agent of tuberculosis , is the capacity to survive within host macrophages . Although several factors required for this survival have been identified , a comprehensive knowledge of such factors and how they work together to manipulate the host environment to benefit bacterial survival are not well understood . To systematically identify Mtb factors required for intracellular growth , we screened an arrayed , non-redundant Mtb transposon mutant library by high-content imaging to characterize the mutant-macrophage interaction . Based on a combination of imaging features , we identified mutants impaired for intracellular survival . We then characterized the phenotype of infection with each mutant by profiling the induced macrophage cytokine response . Taking a systems-level approach to understanding the biology of identified mutants , we performed a multiparametric analysis combining pathogen and host phenotypes to predict functional relationships between mutants based on clustering . Strikingly , mutants defective in two well-known virulence factors , the ESX-1 protein secretion system and the virulence lipid phthiocerol dimycocerosate ( PDIM ) , clustered together . Building upon the shared phenotype of loss of the macrophage type I interferon ( IFN ) response to infection , we found that PDIM production and export are required for coordinated secretion of ESX-1-substrates , for phagosomal permeabilization , and for downstream induction of the type I IFN response . Multiparametric clustering also identified two novel genes that are required for PDIM production and induction of the type I IFN response . Thus , multiparametric analysis combining host and pathogen infection phenotypes can be used to identify novel functional relationships between genes that play a role in infection .
A key to the pathogenic success of Mycobacterium tuberculosis ( Mtb ) , the causative agent of tuberculosis ( TB ) , is the capacity to survive within host macrophages . Although several Mtb factors required for this survival have been identified [1] , a comprehensive knowledge of such factors and how they work together to evade host clearance mechanisms remains elusive . Because cellular models of Mtb infection are tractable for high-throughput applications and facilitate mechanistic follow-up studies , these models are useful for identifying and understanding Mtb factors that drive the outcome of infection at the host-pathogen interface . Several screening approaches have identified Mtb factors required for its intracellular survival . One approach has focused on early cellular events , specifically Mtb’s inhibition of phagosome maturation and acidification . Using large mixed pools of Mtb mutants , Pethe et al identified mutants that co-localized with iron-containing lysosomes [2] . Similarly , using high-content imaging to screen a library of 11 , 000 randomly selected , arrayed transposon mutants , Brodin et al identified 10 mutants unable to block phagosome maturation based on co-localization with LysoTracker-stained acidified lysosomes [3] . An alternative genomic approach focused on Mtb growth in macrophages and compared input and output pools of transposon mutants to identify several attenuated mutants [4] . Interestingly , there is little overlap between the sets of Mtb genes identified as important in macrophages using each of these approaches . Of the 10 genes identified by Brodin et al . , only one ( pstS3 ) was identified in the screen by Rengarajan et al . , and only one ( fadD28 ) was identified by Pethe et al . There is no overlap between genes identified by Rengarajan et al . and those identified by Pethe et al . To more systematically identify Mtb factors required for intracellular growth , we screened a 2660 member arrayed , non-redundant Mtb ( H37Rv strain ) transposon mutant library by high-content imaging to characterize the mutant-macrophage interaction . Taking a systems-level approach to understanding how the genes disrupted in these mutants might work , we profiled host cytokine secretion in response to infection with each of the 361 mutants most impaired for intracellular survival based on the imaging assay . Subsets of these mutants induced strikingly different host responses , suggesting that the disrupted genes play distinct roles in infection . Using a guilt-by-association approach , we combined clustering by the distinct infection phenotypes of imaging outcome and induced macrophage cytokine response , and identified several groups of Mtb mutants predicted to be functionally related . Strikingly , mutants defective in two well-known virulence factors , the ESX-1 protein secretion system and the virulence lipid phthiocerol dimycocerosate ( PDIM ) , clustered together , suggesting a potential functional relationship . ESX-1 type VII protein secretion has long been known to be a critical virulence function of Mtb in macrophages [5] [6] . ESX-1 has been proposed to permeabilize the Mtb-containing phagosome , thereby facilitating the transport of Mtb genomic DNA into the cytosol and its subsequent detection by the cytosolic surveillance program ( CSP ) [7] . CSP detection of bacterial DNA through the pathogen recognition receptor cGAS then triggers induction of the macrophage type I interferon ( IFN ) response [8] [9 , 10] . Until very recently , ESX-1 was the only bacterial function described as required for phagosomal permeabilization and the subsequent induction of the macrophage type I IFN response . The cell-surface lipid PDIM has long been linked to virulence as well [11 , 12] , although the mechanism has not been fully determined . Situated predominantly in the cell envelope , PDIM has been proposed to alter membrane properties important for uptake by macrophages [13] and to mask recognition of the bacterial cell by pathogen-recognition receptors ( PRRs ) in a MYD88-dependent fashion [14] . Here , we find that ESX-1 mutants and PDIM synthesis and export mutants share a common multiparametric phenotype of infection . While it is not surprising that they share similar characteristics of attenuation within infected macrophages , remarkably , they also both failed to induce the macrophage type I IFN response upon infection—a phenotype previously attributed solely to ESX-1 mutants . Based on this shared phenotype , we hypothesized a functional relationship between ESX-1 and PDIM . Indeed , we demonstrate that PDIM production and export are required for the coordinated secretion of ESX-1 substrates . We show that PDIM is required for secretion of ESAT-6 , which has previously been implicated in phagosomal rupture [15 , 16] , but is not mandatory for secretion of its presumed obligate heterodimeric partner , CFP-10 . Of the other two Mtb type VII secretion systems associated with virulence , PDIM is similarly required for secretion of ESX-5 substrates PPE41 and EsxN , but is not required for secretion of the ESX-3 substrates EsxG and EsxH , suggesting a relatively specific relationship between PDIM and secretion of individual substrates . Extending our prediction of functional relationships based on multiparametric clustering , we tested several mutants of genes of unknown function that also clustered together with PDIM and ESX-1 to determine whether they impact PDIM production or export , ESX-1-mediated secretion , or were required for the type I IFN response through an independent , complementary mechanism . Indeed , we found two mutants ( Rv0712 and hrp1 ( Rv2626c ) ) that fail to induce the type I IFN response because of a defect in PDIM production , thereby assigning novel roles in pathogenesis to these two respective genes . Thus , multiparametric clustering of infection combining detailed bacterial phenotype and host response to Mtb mutants can allow a guilt-by-association analysis to identify novel functional relationships between genes that play a role in infection .
To perform high-throughput monitoring of intracellular growth of a library of Mtb transposon mutants ( S1 Table ) , we significantly modified our previously developed high-content imaging assay designed for high-throughput chemical screening [17] in order to accommodate heterogeneous bacterial growth among arrayed mutants and the need for external bacterial fluorescent staining by auramine-rhodamine for visualization ( full details of assay and imaging pipeline development in S1 Text ) . Using rifampin in dose response to inhibit Mtb growth in macrophages , we developed a training set of images that represented a range of intracellular growth inhibition as defined by colony forming units ( CFU ) . Based on correlation with CFU , we selected three readily-interpretable features as imaging metrics: percent of total macrophages infected with Mtb ( “Percent infected” ) , normalized and integrated bacterial fluorescence intensity ( “Mtb FI” ) , and macrophage cell count ( “Macrophage count” ) ( S1A Fig ) . Of note , very similar metrics were used in a previous high-content imaging assay of Mtb growth in macrophages applied to small molecule screening [18] . A pilot screen of 190 mutants was then performed to assess assay performance ( S1B Fig ) . The pilot screen data was additionally used to determine a linear combination of all three imaging metrics that would best identify true positives as determined by CFU retesting of predicted hits in comparison with wild-type control . The first principal component ( PC1 ) from a principal component analysis computed on the data was observed to outperform all other metrics ( S1C and S1D Fig ) . Using our optimized assay and analysis ( Fig 1A ) , we then screened the remainder of the arrayed , non-redundant Mtb 2660 transposon mutant library ( Fig 1B , S1E and S2 Figs ) . Because Mtb mutant growth in macrophages is a graded rather than binary phenotype , we defined a threshold for calling hits for targeted follow-up studies . For each mutant , we calculated a weighted average of the 3 imaging outputs ( PC1 ) , and the 361 mutants ( 13 . 4% of the mutants screened ) with the lowest PC1 scores were selected for additional study as our defined set of growth-impaired mutants ( S2 Table , Fig 1C ) . In addition to 15 mutants identified in our screen that had already been confirmed to be growth-impaired in macrophages in the literature , we retested 73 of the top mutants by enumerating surviving bacteria by plating for CFU in comparison with wild-type Mtb . Although the biology captured by CFU measurements is limited in comparison with complex imaging phenotypes , CFU is considered the gold-standard measurement of comparative bacterial growth , and was thus used as a metric by which our imaging output was optimized and against which we tested hits . Combining the 47 CFU-confirmed and 15 literature-confirmed hits , a true positive rate of 72% was obtained for identifying mutants defective for intracellular growth ( S3 Table ) . Several hits from previous screens [2–4] were among the identified mutants , thereby providing biological validation of our results ( S2 Table ) . Several of the identified mutants fit well into the broader context of pathways and functions known to be important for Mtb survival in the host ( S4 Table ) . Our screen identified genes in the ESX-1 type VII protein secretion system , perhaps the best-characterized mycobacterial virulence factor [5–7 , 19 , 20] . We identified mutants in loci that produce known virulence lipids , including PDIM [11 , 12] . We additionally identified multiple PE and PPE family genes [21] , genes required for synthesis of molybdopterin cofactor [22] , the two-component regulator senX3 [23] , and regulators of Mtb metal content [24] . Host cholesterol is an important carbon source for intracellular Mtb [25 , 26]; we identified multiple enzymes in the cholesterol catabolic pathway . Finally , we identified several genes involved in nitrogen acquisition or metabolism , supporting the recent recognition of the importance of Mtb nitrogen metabolism in infection [27] . A more comprehensive knowledge of the Mtb factors required for growth in macrophages is an important step in identifying the bacterium’s strategies for success . However , understanding the contribution of each gene to overall virulence and induced host responses as well as the networks that coordinate their functions would offer significant breadth and depth to our current understanding of the complex dynamic between pathogen and host . We thus sought to characterize the mutants identified in the screen both by the phenotype of bacterial growth restriction and by elicited host response . By combining these orthogonal phenotypes in a multiparametric analysis , we hoped to identify , through unbiased cluster analysis , genes that might be functionally related or act in a coordinated manner during infection . While mutants of interest were initially selected based on an analysis pipeline trained to identify mutants with decreased CFU , in fact the biology of mutants captured in images is richer and more complex than a simple CFU measurement . To fully capitalize on the phenotypic complexity of bacterial growth restriction afforded by imaging , we sought to analyze the mutants based on individual imaging metrics that allow us to distinguish different mutants and the roles of the corresponding genes in infection . We ranked all screened mutants into deciles by score for each of the three individual imaging metrics used in the primary screen PCA and then grouped the mutants based on the similarities of their scores across all three . The majority of hits behaved as one might intuitively expect: higher macrophage survival , lower percent macrophages infected , and lower bacterial fluorescence intensity than the vast majority of screened mutants . However , some mutants behaved differently , eliciting more macrophage cell death or higher percent macrophage infection than the majority of mutants . To characterize the host response elicited by each mutant , we then used a multiplexed Luminex assay to quantify macrophage production of cytokines in response to infection with each of the 361 mutants identified in the primary screen . Twelve cytokines were accurately quantifiable and differed significantly between wild-type-infected and subsets of mutant-infected cells ( S3 Fig ) . These cytokines reflect processes known to be important for Mtb macrophage infection , including the type I IFN response ( e . g . CCL5 ) and pro-inflammatory response ( e . g . MMP9 , TNF-α ) . As reflected in these 12 cytokines , macrophage responses to infection with the mutants were in fact quite varied ( S4 Fig ) ; this variability did not reflect either the input ratio of bacteria to macrophage or relative degree of intracellular growth impairment ( S4B Fig ) , suggesting that the cytokine response to each mutant is independent of relative attenuation in macrophages . We hypothesized that combining imaging and cytokine phenotypes for each mutant would yield the most information about how those mutants might work together in networks interacting with aspects of the host response to infection ( Fig 2A ) . To cluster mutants based on both the imaging phenotype , which we considered to reflect primarily bacterial outcomes , and cytokine phenotype , which we considered to reflect primarily host response , we thus sought to combine the two phenotypes in a multiplexed analysis . For this clustering analysis , we had to explicitly balance the contribution of the imaging and cytokine features to avoid either source dominating the analysis . A straightforward approach to doing this is to select the same or similar number of measurements from both sources . To reduce the number of cytokine features , we grouped the features using hierarchical clustering and found that they fell into four groups ( cyt-1 ( CXCL10 and CCL5 ) , cyt-2 ( TNF-α , CXCL2 , MMP9 , and Lipocalin ) , cyt-3 ( CCL2 , CCL4 , IGF-1 , and BAFF ) , and cyt-4 ( CCL3 and osteopontin ) S4C and S4D Fig ) . Validating the biological relevance of these groupings , cytokines reflecting the type I IFN response clustered together , and pro-inflammatory cytokines clustered together in a distinct group . The features in each group were combined by averaging; we used this grouping for further analysis . To maximize our ability to distinguish phenotypically unique sets of mutants , we limited analysis from this point to the 113 mutants impaired for intracellular growth with macrophage responses most distinct from wild-type infected cells . Of note , confirmed true positives ( by CFU ) were represented in both this set of 113 mutants and mutants that induced macrophages responses more similar to wild-type Mtb , supporting the idea that the induced macrophage response is a distinct phenotypic readout . Given that the bacterial imaging and host response phenotypes provide distinct information about the mutants , we hypothesized that mutants similar in both metrics would be the most closely functionally related . To identify such phenotypically similar groups of mutants , we then performed a two dimensional unsupervised clustering based on the combined host response features and bacterial imaging features ( Fig 2B ) . This clustering identified five groups of mutants ( S5 Table ) . To determine objectively whether cytokine and imaging features contributed overlapping or distinct information to the analysis of each mutant , we next performed a principal component analysis to determine how features were related to one another . Interestingly , the first principal component ( PC1 ) was comprised almost entirely of host response features ( cyt-1 , cyt-2 , cyt-3 , and cyt-4 ) , while the second principal component ( PC2 ) was comprised almost entirely of bacterial imaging phenotype features ( Fig 2C ) . These results suggest that the bacterial imaging phenotypes and host response phenotypes are in fact nearly orthogonal , and provide complementary information about the tested mutants . Plotting the mutants by PC1 and PC2 , we then determined that the mutants indeed segregate in two-dimensional space , indicating that the multiparametric analysis can provide insights into points of divergence in the host-pathogen interaction ( Fig 2D ) . Given the unique phenotypic signature of each cluster in this combined analysis , we hypothesized the mutants within each cluster may be functionally related . Notably , mutants in pathways for two well-known virulence factors , ESX-1 and PDIM , clustered together by this multiparametric analysis combining imaging and induced cytokine phenotypes . The ESX-1 protein secretion system has been proposed to permeabilize the phagosomal membrane , facilitating recognition of Mtb by the macrophage cytosolic surveillance program ( CSP ) and triggering a type I IFN response [7–10] . On balance , accumulating evidence suggests that this response benefits the bacterium . As expected , we found that ESX-1 mutants failed to induce a type I IFN response ( Fig 3A ) . Surprisingly , PDIM mutants similarly failed to induce a type I IFN response ( Fig 3A ) . Previously , ESX-1 secretion was the only Mtb function linked to the macrophage type I IFN response . Although the ESX-1 ATPase EccCa1 has been proposed to bind to a variety of enzymes required for M . marinum lipid biosynthesis , including some of the enzymes required for PDIM biosynthesis [28] , a functional link has not previously been proposed between ESX-1 and PDIM . Our results suggest that PDIM functionally interacts with ESX-1 , as it also plays a role in the intracellular events that culminate in induction of type I IFNs . Critical for Mtb growth in the host [11 , 12] , PDIM is a large Mtb cell-surface lipid . The chromosomal locus responsible for producing and exporting PDIM encodes machinery for acyl chain production , acyl activation , post-production modification , and transport . Specific functions for PDIM have only recently been proposed , including mediating interactions between the bacterial cell and host plasma membrane [13] and masking MYD88-dependent detection of pathogen-associated molecular patterns ( PAMPs ) on the mycobacterial surface [14] . Our results indicate that PDIM is additionally required for intracellular processes leading to induction of the macrophage type I IFN response . We hypothesized that either a distinct PDIM locus gene function ( such as function of a single transporter ) or fully intact , properly localized PDIM could be required to induce a type I IFN response . To systematically identify the PDIM locus functions required , we profiled expression of type I IFN-induced genes following macrophage infection with mutants that contained disruptions in many genes across the entire PDIM locus ( Fig 3B ) . Mutants defective in each of the functions of the PDIM locus including acyl chain production ( ppsD , ppsD , mas ) , acyl chain activation ( fadD28 ) , and transport ( drrABC , mmpL7 ) failed to induce a type I IFN response . The only PDIM locus mutant that induced wild-type levels of type I IFN was lppX . Although proposed to be required for PDIM transport [30] , lppX is in a different operon with pks1/15 genes [31] . Our results suggest that transporters drrABC and mmpL7 have functions distinct from lppX in lipid localization and ultimate function . The loss of a type I IFN response following macrophage infection with all other available mutants in the locus suggest that intact and properly localized PDIM is necessary for induction of type I IFNs . To confirm that loss of the type I IFN response was in fact attributable to individual genes within the PDIM operons and provide additional confirmation of roles for both PDIM biosynthetic and transport genes , we used two mutant complementation strategies . First , we introduced an integrating , inducible allele of transporter drrC to complement drrC disruption in the corresponding transposon mutant . Loss of PDIM export was confirmed to be commensurate with what has previously been described for a comparable drrC transposon mutant [32] ( S5 Fig ) . Second , we used a published mutant with a single loss-of-function point mutation in biosynthetic gene ppsD ( ppsD ( G44C ) ) and complemented mutant strains with either a chromosomal reversion of the mutation or episomal expression of the wild-type allele [33]; the published loss and restoration of PDIM in the mutant and revertant were confirmed using LC-MS ( S6A Fig ) . Infection with either Tn::drrC or ppsDmut failed to induce the type I IFN response; in both cases , complementation restored the response ( Fig 3C and 3D ) . As expected , the imaging phenotypes of the mutants in infected macrophages also reverted to wild-type by complementation , confirming that PDIM production and transport genes are required for full virulence ( Fig 3E ) . Bulk secretion of IFN-β was more modest than the induced transcriptional response but followed a similar trend for each mutant ( S7 Fig ) . We next investigated how PDIM might facilitate induction of the type I IFN response . To explore whether PDIM is a PAMP recognized by the macrophage CSP after ESX-1 permeabilizes the phagosomal membrane , we treated macrophages with a liposomal formulation of PDIM . No type I IFN response was elicited even at high concentrations of PDIM ( S8 Fig ) , consistent with recent work suggesting that bacterial genomic DNA is instead the relevant CSP-detected PAMP triggering type I IFNs [8–10] . One step previously described as critical for induction of the macrophage type I IFN response to Mtb infection is disruption of the phagosomal membrane , which allows mixing of cytosolic and phagosomal contents and detection of bacterial products by the CSP [7] . To test whether PDIM facilitates phagosomal permeabilization , we sought to determine whether permeabilizing the phagosome by an alternative mechanism would render PDIM dispensable for induction of the type I IFN response . Using a previously described method for creating pores in the phagosomal membrane [7 , 34] , we used listeriolysin O ( hly ) to permeabilize the phagosome in an ESX-1-independent manner . A ppsD clean deletion mutant ( S6B Fig ) was transformed with an integrating plasmid expressing hly ( S9 Fig ) . Expression of listeriolysin O in PDIM mutant bacteria indeed restored the type I IFN response ( Fig 4A ) , suggesting that PDIMs contribution to this response is at the level of phagosomal permeabilization . Two other groups have recently reported similar observations using flow cytometry to quantitate phagosomal permeabilization [35 , 36] . ESX-1 protein secretion is the only other Mtb factor described to be required for phagosomal permeabilization . To determine whether PDIM is required for ESX-1 function itself , we tested whether mutants in PDIM biosynthesis and export were able to secrete the ESX-1 effector ESAT-6 , which until very recently had been implicated as the likely pore-forming secreted effector of the ESX-1 system . Using Western blot analysis of bacterial supernatants and pellets , we found that ESAT-6 secretion was indeed impaired in both the ppsDmut biosynthesis mutant and the Tn::drrC export mutant ( Fig 4B ) . ESAT-6 secretion was similarly impaired in strains with clean deletions of ppsD and the PDIM biosynthetic enzyme mas ( S6B and S10 Figs ) . We then tested whether secretion of the heterodimeric partner of ESAT-6 , CFP-10 , was similarly impaired in PDIM mutants . Distinct from the phenotype observed for ESAT-6 where its secretion was impaired in all PDIM mutants , CFP-10 secretion was impaired in the drrC mutant and mas clean deletion , but was enhanced in the ppsD mutants ( Fig 4B; S10 Fig ) . Wild-type phenotypes were restored by complementation of the mutations in all cases . Of note , simultaneous analysis of the lysed bacterial pellets indicated that total ESAT-6 and CFP-10 protein production was not affected by the absence of PDIM and , secretion of the Sec substrate antigen 85 was not impacted by the absence of PDIM , suggesting that PDIM does not globally impact secretion . To determine whether PDIM’s disruption of ESX-1-mediated secretion was generalizable to other ESX secretion systems implicated in virulence [37 , 38] , we next tested whether loss of PDIM disrupts secretion of ESX-3 and ESX-5 substrates . ESX-3 secretion was elicited by iron starvation [39]; ESX-5 was elicited by phosphate starvation [40] . While PDIM mutants did not show impaired secretion of ESX-3 substrates EsxG and EsxH ( S11 Fig ) , the ppsD point mutant and drrC transposon mutant did demonstrate impaired secretion of ESX-5 substrates PPE41 and EsxN ( Fig 4C ) . Simultaneous detection from bacterial pellet lysates indicated that production of ESX-5 substrates was not changed in either mutant . Thus , while the dependence of secretion on PDIM is not restricted solely to ESX-1 substrates but also impacts the secretion of ESX-5 substrates , it does demonstrate some specificity as it is not generalizable to all ESX secretion systems . To further test whether mutant clustering by multiparametric analysis can predict novel functional relationships between genes , we next investigated the relationship between the functions of other genes represented in Cluster V and the functions of PDIM and ESX-1 . We first sought to confirm that the observed , shared phenotype could be truly accounted for by the transposon-disrupted gene in each mutant by complementing several of the additional 8 mutants that appear in Cluster V with episomal copies of the respective wild-type genes , and tested for reversion to wild-type production of the type I IFN response . Single-gene complementation of two Cluster V mutants , Tn::Rv0712 and Tn::hrp1 ( hypoxic response protein 1 , Rv2626c ) , restored the type I IFN response ( Fig 5A and 5B ) , suggesting that each of these genes is required for induction of type I IFNs . Rv0712 is a formylglycine-generating enzyme ( FGE ) , required for activation of type I sulfatase enzymes [41]; however , the physiologic substrates and role of sulfatases in infection is unknown [42] . Rv0712 has been predicted to be essential in mice based on Tn-Seq experiments [43] . hrp1 is part of the DosR regulon , which is highly expressed upon exposure to nitric oxide or hypoxic conditions , and proposed to play a role in dormancy [44–46] . Hrp1 is a secreted protein with two cystathionine beta synthase ( CBS ) domains [47] but its biological function and role in infection is also unknown . Neither gene has previously been linked to mycobacterial lipids , ESX-mediated secretion , or the macrophage type I IFN response . Based on its phenotypic clustering , we hypothesized that Rv0712 and hrp1 could be required for PDIM biosynthesis thereby facilitating ESX-1 secretion and phagosomal permeabilization , for ESX-1 secretion independent of PDIM , or for an altogether independent function influencing the type I IFN response , such as the production or exposure of a bacterial PAMP or a role in host CSP signaling . Using quantitative mass spectrometric analysis of total cell wall mycobacterial lipids [48] , we determined that in fact , both Tn::Rv0712 and Tn::hrp1 produced significantly less PDIM than wild-type H37Rv ( Fig 5C; S12A and S12B Fig ) . PDIM production in the mutants was restored with complementation . We then tested whether the observed loss of PDIM in the mutants would correlate with changes in secretion of ESX-1 substrates . Secretion of ESAT-6 was diminished in both the hrp1 and Rv0712 mutants and restored in the complemented strains ( Fig 5D ) ; secretion of CFP-10 was diminished in the Rv0712 mutant but unchanged in the hrp1 mutant . Thus this multiparametric clustering successfully predicted that these two genes would functionally interact with PDIM and ESX-1 in their induction of the macrophage type I IFN response .
A deeper , systematic understanding of the Mtb host-pathogen interface is a necessary prerequisite to developing a new generation of anti-TB therapeutics that target the functions most critical for bacterial survival and growth in the host setting . We used a multiparametric analysis combining imaging phenotypes and macrophage cytokine responses to infection to systematically identify Mtb mutants impaired for intracellular survival and predict novel functional relationships among subsets of those genes based on shared complex phenotypes . We have demonstrated the strength of this approach by demonstrating its ability to identify a previously unknown relationship between two well-known virulence factors , ESX-1 and PDIM , and to assign function to two genes not previously described to be important for intracellular survival , now linking them to the function of these two known virulence factors . We anticipate that our approach can be easily applied to identify additional functional relationships between genes . Given the increasing ability to perform systematic high-throughput profiling of cellular phenotypes such as infection using arrayed libraries of bacterial mutants , we propose that this type of multi-parametric analysis can be extended to other pathogens and additional phenotypes to identify genes that are functionally linked and assign functions to genes of unknown function . Given the high percentage of bacterial genes with unknown function in many pathogens including Mtb , the ability to functionally link known and unknown genes is a powerful approach for building our understanding of pathogenesis . During Mtb infection , the mechanisms that the bacterium requires to ensure its own survival are critical to determining the outcome of infection . Disruption of the phagosomal membrane , which permits mixing of phagosomal and cytosolic contents , has complex downstream consequences , simultaneously triggering pro-inflammatory and anti-inflammatory programs in the macrophage [9] . Proposed benefits of this permeabilization for the bacterium include increased access to macronutrients and micronutrients . Additionally , this permeabilization leads to induction of the type I IFN response upon CSP detection of Mtb gDNA . Although the direct pathogenic relevance of type I IFNs in Mtb infection remains a matter of active scientific debate , growing evidence suggests that on balance , type I IFNs benefit Mtb in the bacterial/host standoff [49 , 50] . The role of the type I IFN response and in particular the molecular details of the host factors that lead to induction of type I IFNs and downstream effects have been an active area of recent investigation [7–10 , 51] . While details of the events on the host side of the Mtb and macrophage interaction that culminate in production of type I IFNs have been well-elucidated , until very recently , ESX-1-mediated phagosomal permeabilization was the only mycobacterial event implicated [7] . Two recent reports have suggested that PDIM is involved in phagosomal permeabilization [35 , 36] . Here , we also present evidence that is consistent with the finding that the Mtb virulence lipid PDIM is a second Mtb factor contributing to the permeabilization of the phagosome . Our data additionally suggest that this effect is at least in part due to its impact on ESX-mediated secretion . Intact ESX-1 secretion has been shown to be required for phagosome permeabilization [7 , 52 , 53] with ESAT-6 thought to be the responsible effector [15 , 16] . Whether ESAT-6 is truly the secreted ESX-1 effector that mediates phagosomal disruption has very recently been called into question [54] . In that report , at non-acidic pH , purified ESAT-6 was unable to lyse membranes without residual detergent from the preparation . In contrast , at acidic pH , ESAT-6 alone was sufficient to lyse membranes , consistent with earlier reports . Whether PDIM serves a detergent function to facilitate ESAT-6-mediated phagosomal lysis at relatively non-acidic pH in the early phagosome as suggested by Augenstreich et al . , whether focal acidic conditions at points where the Mtb membrane meets the phagosomal membrane facilitate ESAT-6-mediated membrane lysis , or whether an entirely distinct secreted ESX-1 effector such as EspC is responsible for phagosomal membrane disruption as suggested in the discussion by Conrad et al . , remains to be determined . Although PDIM and ESX-1 are two of the best-described Mtb virulence factors , until very recently they had not been understood to contribute directly to the same pathogenic process . A potential role for ESX-1 in regulating the essential cell wall mycolic acids has been suggested [28] , and genetic disruptions of the ESX-1 and ESX-5 systems have been described to render the mycobacterial cell wall more susceptible to detergent and antibiotic-mediated disruption [55 , 56] . In contrast , the reverse interaction , i . e . , the impact of PDIM on secretion , has been less clear . Though loss of PDIM has been reported to abrogate the essentiality of individual ESX-5 secretion system components during macrophage infection [10] , the concept that a surface lipid facilitates protein secretion has not previously been proposed for Mtb or any other bacteria . The complete mechanistic basis of type VII secretion is an ongoing area of active investigation , and the current model for how ESX-mediated secretion occurs is largely limited to bacterial cytosolic and inner membrane events [52] . In the context of what is understood about ESX-mediated secretion , our determination that PDIM is required for secretion of some ESX substrates i . e . , ESAT-6 , PPE41 and EsxN but not others i . e . , CFP-10 , EsxG , and EsxH , is intriguing . The position of PDIM in the cell envelope raises the question of whether PDIM facilitates the steps for secretion of individual substrates beyond the inner membrane , such as transport across outer layers of the cell , dissociation of the heterodimeric secretion pairs , or release from the cell surface . This conjecture would be consistent with the observed distinct secretion phenotypes for different substrates . The disparate CFP-10 secretion phenotypes observed for different PDIM mutants suggest that distinct parts of the lipid molecule may interact with individual substrates . A role facilitating transit across the outer layers of the cell or release from the cell surface would also explain why mycobacteria , including Mycobacterium smegmatis , that do not produce PDIM have no secretion defect . While the core ESX-1 secretion machinery is nearly identical between Mtb and Msmeg , the outer envelope has significantly less similarity . Given that the interdependence of ESX-1 substrates for secretion has made studying the role of individual effectors in both secretion and pathogenesis challenging , strains with distinct secretion phenotypes offer an opportunity for additional dissection of the role of individual effectors in pathogenesis . Although the uniquely rich repertoire of Mtb lipids has long been recognized as important for pathogenesis , ascribing defined molecular roles to those lipids has proven challenging , in part because of the more limited range of tools available for studying lipids relative to proteins or nucleic acids . Here , we ascribe roles to PDIM as facilitating both phagosomal permeabilization and secretion of individual ESX substrates . More broadly , our results illustrate a novel relationship in which bacterial protein secretion is dependent upon a bacterial lipid at the cell surface . As new technologies including comparative lipidomics [48] open the door to understanding the molecular functions of individual lipids , we anticipate that paradigms for the role of bacterial lipids in infection will fundamentally change . Our multiparametric clustering additionally facilitated the determination that two additional genes required for virulence , Rv0712 and hrp1 act upstream of PDIM production in facilitating the type I IFN response . While additional work remains to fully determine how Rv0712 and hrp1 contribute to complex lipid production , the identification of a relationship between each of these genes , PDIM production , and ESX-mediated secretion confirms that clustering by host and pathogen phenotypes can suggest functions for genes with an unknown role in virulence . Specifically considering Rv0712 , our work suggests that type I sulfatase activity is important for Mtb infection of macrophages . Notably , neither our screen nor previous screens have identified individual sulfatases as essential in host cells , suggesting possible redundancy among the type I sulfatases with Rv0712 potentially acting as a functional regulator . While PDIM and sulfated lipids share the common methylmalonyl CoA precursor , the relationship between these two lipids has previously been shown to be inversely coupled through anabolic pathways [57] . The requirement of Rv0712 for PDIM production suggests a more complicated relationship of PDIM and sulfate metabolism pathways . We suggest that the dataset arising from this multiparametric cluster analysis offers the opportunity for the discovery of other novel functional interactions among genes required for infection . For example , as the macrophage type I IFN response was a major contributing factor in the generation of Cluster V , further investigations of mutants in this cluster may reveal bacterial factors that interact more directly with the host CSP that triggers the type I IFN response . Similarly , as production of cytokines such as TNF-α , MMP9 , and MIP-1α was a major factor in formation of Clusters I-IV , genes involved in these clusters are likely mostly involved in host interactions downstream from cell-surface and phagosomal PRRs , including TLRs . Yet the patterns of induction or suppression of these cytokines are distinct in the different clusters of mutants . Thus , this analysis presents the opportunity to dissect the different sets of bacterial factors that interact with and modulate the responses of these PRRs in distinct and complex ways . Particularly given that the bulk of work studying PAMP/PRR interactions has been undertaken with purified PAMPs , we thus further anticipate that the generated dataset characterizing the comprehensive infection of macrophages with Mtb mutants will offer opportunities for unraveling the complexities of PAMP/PRR interactions within the context of an intact bacterium , in which each interaction contributes to the ultimate response . Finally , these results and approach have implications for future drug development . With increasing interest in not simply targeting essential factors of Mtb but also alternative strategies that take into account host responses , understanding the impact of targeting different bacterial factors on local innate immune responses will be important .
The transposon mutant library was made in Mtb strain H37Rv . For genetic manipulations , Mtb was growth in Middlebrook 7H9 broth ( Difco ) supplemented with Middlebrook OADC ( BD ) , glycerol 0 . 2% , and tween-80 0 . 05% . Mtb strains ppsD ( G44C ) , ppsD ( G44C ) revert and ppsD ( G44C ) pMV::ppsD were previously published strains [33] . The inducible drrC construct was generated by cloning the drrC gene into pMC1S [58] . The hly-expressing plasmid was generated by cloning Listeria hly into pMC1S . The ppsD and mas clean deletion strains were generated using mycobacterial recombineering to replace the entire ppsD or mas gene with a hygromycin cassette [59] . Tn::Rv0712 and Tn::hrp1 were selected from the arrayed , annotated transposon library and complemented with each individual gene in the episomal , constitutively expressing vector JP118 [39] . Gene-specific primers are listed in S1 Text . Mtb strain H37Rv was transposon mutagenized using a Himar-based transposon according to the protocol described in Sassetti et al [60] . Following selection on kanamycin-containing plates , approximately 26 , 000 individual colonies were selected and arrayed as glycerol stocks in 96-well plates . Colony PCR was then performed , using a two-step process with one primer internal to the transposon and one random primer for each PCR step to identify each insertion site . Each PCR product was then subjected to Sanger sequencing . Arrayed library mutants were selected after processing the data with a BLAST alignment-based script that incorporates a number of criteria , including Sanger read sequence quality , the uniqueness of genome sequence match , location of alignment within the sequence read and e-value of the BLAST match . In cases of genes demonstrating multiple insertion sites , priority was assigned to insertion sites greater than 10 nucleotides into an ORF or more than 50 nucleotides upstream of the stop . Ultimately 2660 transposon mutants , each with insertion in a different gene , were selected and arrayed in 28 96-well plates . The Tn mutant library was inoculated into 96-well plates containing 100μL Middlebrook 7H9 broth supplemented with Middlebrook OADC , glycerol 0 . 2% , and tween-80 0 . 05% . Plates were then incubated at 37°C . Every 2–3 days , wells were resuspended by pipetting and OD600 was determined using a SpectraMax plate-reader ( Molecular Devices ) . Growth rates ( υ ) were calculated for each mutant by fitting a sigmoid function to the growth curve ( OD600 over time ) and estimating the slope . Most mutants in the library grew similarly in 96-well plates , and attenuated mutants did not grow differently than all mutants ( S13A and S13B Fig ) . Full assay development details are in S1 Text . Briefly , transposon mutants were grown in 96-well plates in 7H9 media with OADC , glycerol , and tween-80 supplementation . On the day of infection , plates were centrifuged to pellet bacteria and media was removed . Pellets were resuspended in an equal volume of PBS , and plates were again centrifuged to pellet bacteria . Supernatants were removed , and cells were resuspended thoroughly in PBS . A low-speed spin was performed to pellet cell clumps . The supernatant was moved to another 96-well plate , and OD600 for each well was recorded . The average bacterial number per ml across the plate was calculated , and bacteria were added to DMEM with 20% heat-inactivated horse serum ( HS ) to give an average number of CFU/ml of 62 , 500 ( averaged across all wells on the plate ) . The bacteria in DMEM/HS were then added to J774A . 1 macrophages in 96-well plates to give an average MOI of 1:1 . Control bacteria were prepared in parallel , and added to macrophages at an MOI of 2:1 , 1:1 , 1:2 , and 1:4 . After 4 hours for phagocytosis , media was aspirated , infected cells were washed once with PBS , and media was added back . Cells were incubated for 3 days , then media was aspirated off . Cells were again washed with PBS to remove extracellular bacteria , then fixed with 4% paraformaldehyde . Wells were then stained with auramine-rhodamine to visualize bacteria and DAPI ( Sigma ) to visualize macrophage nuclei . Plates were imaged on an ImageXpress high-content microscope ( Molecular Devices ) . Images were then analyzed using CellProfiler automated image analysis [29] . The basic operation of the image analysis workflow is similar to that used previously [17] . Briefly , a pre-processing CellProfiler pipeline was used to determine the illumination correction of each fluorescent channel . A second pipeline then used the results of the first pipeline to correct each channel for uneven illumination , followed by detection and exclusion of fluorescent debris , and collection of whole-image features from the corrected images including the green fluorescent pixel intensity and area integrated across the field of view , the macrophage number per field of view and the percentage of macrophages that are infected . This pipeline additionally collected a variety of image-based features for each auramine-rhodamine-labeled mycobacterial clump and the associated macrophage DNA-labeled nuclei , in order to produce rich , quantitative profiles in an unbiased manner . These features included mycobacterial and nuclear size and shape , intensity , and texture statistics . All pipelines used for this paper are publically available at http://www . cellprofiler . org/published_pipelines . shtml . The features extracted from the image analysis pipeline were used to select hits as well as identify phenotypically distinct groups of mutants within the hits . By analyzing the features in the pilot screen , we identified three features ( macrophage count , Mtb fluorescence intensity , percentage of infected macrophages ) to be best predictive of CFU ( S1 Text ) . To account for the small differences in MOI across replicates , the residues of the feature values regressed against the MOI were used in all further analyses . We combined the three features into a single readout by computing a linear combination for which the variance in the data was maximized ( i . e . the first principal component “PC1” of the data ) . We found that PC1 was a better predictor of CFU compared to the individual features ( S1C Fig , S1 Text ) or a combination of all measured features ( S1D Fig ) and therefore used it to select mutants for follow up experiments . The 361 attenuated Mtb Tn mutants were grown in 4 96-well plates to mid-log phase . J774A . 1 macrophages were seeded in 96-well plates overnight . Cells were then infected with the attenuated mutants or controls in a 96-well format , prepared as described above . The average MOI across the plate was 1:1 . Infection was allowed to progress for 3 days ( or 48 or 72 hours for the timecourse ) , then supernatants were harvested and used in a Luminex assay for detection of 34 multiplexed cytokines according to the manufacturer’s protocol . All mutants included in the primary screen were ranked in deciles for each of the three imaging features: bacterial fluorescence intensity , macrophage cell count , and percent macrophages infected . Host cytokine were clustered in an unsupervised manner into four groups based on behavior across all hit mutants . The 113 mutants eliciting cytokine responses most distinct from wild-type infection were then clustered using hierarchical clustering with Pearson correlation as the similarity metric . For the clustering , each mutant was represented by a 7-dimensional feature vector representing , both , imaging ( 3 features ) and cytokine response ( 4 feature groups ) . Mtb strains were grown in Middlebrook 7H9 broth ( Difco ) supplemented with Middlebrook OADC ( BD ) , glycerol 0 . 2% , and tween-80 0 . 05% to mid log-phase . For ESX-1 blotting , an equal number of cells from each culture were pelleted by centrifugation and resuspended in Sauton’s media supplemented with OADC and glycerol for 24 hours . For ESX-3 blotting , an equal number of cells from each culture were pelleted by centrifugation , washed three times in chelated Sauton’s medium [61] , and resuspended in chelated Sauton’s medium for 48 hours . For ESX-5 blotting , an equal number of cells from each culture were pelleted by centrifugation , washed and resuspended in medium phosphate Sauton’s medium ( 250μM phosphate ) with tween for 48 hours , then washed and resuspended in low phosphate Sauton’s medium ( 25μM phosphate ) without tween for 48 hours ( modified from [40] ) . Supernatants were then harvested and proteins were concentrated 500-fold . Concentrated supernatants were run on an Tris-glycine gel and probed with antibodies to ESAT-6 ( Abcam ) , CFP-10 ( Abcam ) , Antigen 85 ( Abcam ) , GroEL ( Abcam ) , EsxGH [61] , a kind gift of Dr . Jennifer Philips , Washington University , EsxN , or PPE41 . Blots were stained with Ponceau prior to antibody detection to ensure equal protein loading of all lanes . Pellets were lysed by bead beating in protein extraction buffer ( 50mM TrisCl , pH 7 . 5 , 5mM EDTA , 1mM 2-mercaptoethanol , protease inhibitor ( Amresco ) ) prior to use for Western blotting . Full-length esxN was amplified from M . tuberculosis Erdman genomic DNA by PCR with primers esxNHisF2 ( 5’gcattcatgacgattaattaccagttcgggga3’ ) and esxNHisR2 ( 5’ gcatctcgagggcccagctggagccga3’ ) , digested with BspHI and XhoI and cloned in pET28b+ ( Novagen ) between the NcoI and XhoI restriction enzyme sites to generate pET28-EsxNHis6 encoding EsxN with a C-terminal His6 tag . A plasmid for co-expression of PPE41-His6 and PE25 was described previously [62] . Recombinant proteins were produced in E . coli BL21 ( DE3 ) and purified by Ni2+-NTA affinity chromatography ( Qiagen ) . PPE41-His6 was bound to the column under native conditions in 20 mM HEPES buffer , 300 mM NaCl , pH 7 . 8 and eluted in 20 mM HEPES buffer , 500 mM NaCl , pH 7 . 8 containing 50–150 mM imidazole . EsxN-His6 was purified under denaturing conditions; protein was bound to the column in 20 mM sodium phosphate buffer , 500 mM NaCl , 6 M guanidine hydrochloride , pH 7 . 8 and eluted in 20 mM sodium phosphate buffer , 500 mM NaCl , 8 M urea , pH 4 . Contaminant proteins that co-purified with EsxN-His6 were removed by passing eluted fractions through a 50 kDa cut-off Amicon Ultra centrifugal filtration unit ( Millipore ) . Polyclonal antisera against purified EsxN-His6 and PPE41-His6 proteins were generated in rabbits by Pierce Custom Antibodies ( Thermo Scientific ) using TiterMax Gold adjuvant ( Sigma ) . Murine bone marrow-derived macrophages ( BMDM ) were prepared as previously described [17] from C57BL6 mice ( Jackson Laboratories ) . BMDM were seeded in 24 well plates overnight in DMEM with 20% FBS and 25ng/ml rmM-CSF ( R and D Systems ) . Mtb strains were grown to mid-log phase , then used to infect BMDM at an MOI of 2:1 . After 4 hours of phagocytosis , cells were washed once with PBS and media was added back . RNA was harvested 24 hours after infection with TRIzol ( ThermoFisher Scientific ) and prepared according to the manufacturer’s protocol . cDNA was prepared using SuperScript III ( ThermoFisher Scientific ) according to the manufacturer’s protocol . qPCR was performed using primers specific to the indicated genes . Liquid chromatography-mass spectrometric quantitation of PDIM was performed on total cell wall extracts of Mtb according to published protocols [48] . In brief , Mtb strains were grown in 7H9 supplemented with Middlebrook OADC ( BD ) and glycerol 0 . 2% . Cells were then pelleted and extracted in chloroform:methanol and an equal quantity of total lipids were run through an established LC-MS protocol using an Agilent Technologies 6520 Accurate-Mass Q-Tof and a 1200 series HPLC system with a Monochrom diol column [48] . PDIM species were identified using positive mode MS based on predicted retention time [48] , highly accurate mass matching to known PDIM species as listed in the figures , and confirmed based on collision-induced dissociation mass spectrometry with major fragments as listed in S1 Text . Protocols for care and use of mice for bone marrow macrophage preparation were approved by the Massachusetts General Hospital Institutional Animal Care and Use Committee . The approved protocol number is 2007N000048 . The procedure for euthanasia approved as part of this protocol is in accordance with American Veterinary Medical Association Guidelines for the Euthanasia of Animals . | Tuberculosis ( TB ) remains a significant global health problem . One barrier to developing novel approaches to preventing and treating TB is an incomplete understanding of the strategies that the causative bacterium , Mycobacterium tuberculosis ( Mtb ) , uses to survive and cause disease in the host . To systematically identify Mtb genes required for growth in infected host cells , we screened an annotated , arrayed library of Mtb mutants in macrophages using high-content imaging . We then used multiplexed cytokine analysis to profile the macrophage response to each mutant attenuated for intracellular growth . Combining imaging parameters reflective of intracellular infection with the macrophage response to each mutant , we predicted novel functional relationships between Mtb genes required for infection . We then validated these predictions by demonstrating that production and export of a cell envelope lipid is required for coordinated virulence-associated protein secretion , phagosomal membrane rupture , and production of the macrophage type I interferon response . Extending our prediction of functional relationships to unknown genes , we demonstrated that two genes not previously linked to virulence also act in this pathway . This work demonstrates a broadly applicable approach to elucidating and relating bacterial functions required for pathogenesis and demonstrates a previously unknown dependence of Mtb virulence-associated protein secretion on an outer envelope lipid . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"blood",
"cells",
"bacteriology",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"cytokines",
"pathogens",
"immunology",
"microbiology",
"physiological",
"processes",
"developmental",
"biology",
"secretion",
"systems",
"molecular",
"development",
"genetic",
"elements",
"bacteria",
"lipids",
"white",
"blood",
"cells",
"genomics",
"microbial",
"physiology",
"animal",
"cells",
"actinobacteria",
"immune",
"system",
"biochemistry",
"bacterial",
"physiology",
"cell",
"biology",
"mycobacterium",
"tuberculosis",
"virulence",
"factors",
"phenotypes",
"physiology",
"secretion",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"transposable",
"elements",
"macrophages",
"mobile",
"genetic",
"elements",
"organisms"
] | 2017 | Systematic, multiparametric analysis of Mycobacterium tuberculosis intracellular infection offers insight into coordinated virulence |
Throughout the last several decades , vaccination has been key to prevent and eradicate infectious diseases . However , many pathogens ( e . g . , respiratory syncytial virus [RSV] , influenza , dengue , and others ) have resisted vaccine development efforts , largely because of the failure to induce potent antibody responses targeting conserved epitopes . Deep profiling of human B cells often reveals potent neutralizing antibodies that emerge from natural infection , but these specificities are generally subdominant ( i . e . , are present in low titers ) . A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes . Here , we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein . In addition , the epitope-focused immunogen efficiently boosts antibodies targeting the palivizumab epitope , resulting in enhanced neutralization . Overall , we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies .
The development of vaccines has proven to be one of the most successful medical interventions to reduce the burden of infectious diseases [1] , and their correlate of protection is the induction of neutralizing antibodies ( nAbs ) that block infection [2] . In recent years , advances in high-throughput B cell technologies have revealed a plethora of potent nAbs for different pathogens that have resisted the traditional means of vaccine development for several decades , including HIV-1 [3] , influenza [4] , respiratory syncytial virus ( RSV ) [5 , 6] , Zika [7 , 8] , dengue [9] , and others [10–12] . A major target of these nAb responses is the pathogen’s fusion protein , which drives the viral and host cell membrane fusion while undergoing a conformational rearrangement from a prefusion to a postfusion state [13] . Many of these nAbs have been structurally characterized in complex with their target , unveiling the atomic details of neutralization epitopes [7 , 14 , 15] . Together , these studies have provided comprehensive antigenic maps of the viral fusion proteins , which delineate epitopes susceptible to antibody-mediated neutralization and provide a road map for rational and structure-based vaccine design approaches . The conceptual framework to leverage nAb-defined epitopes for vaccine development is commonly referred to as reverse vaccinology [16–18] . Although reverse vaccinology-inspired approaches have yielded a number of exciting advances in the last decade , the design of immunogens that elicit such focused antibody responses remains challenging . Successful examples of structure-based immunogen design approaches include conformational stabilization of RSV fusion protein ( RSVF ) in its prefusion state , which induces superior serum neutralization titers when compared to immunization with RSVF in the postfusion conformation [19] . In the case of influenza , several epitopes targeted by broadly neutralizing antibodies ( bnAbs ) were identified within the hemagglutinin ( HA ) stem domain , and an HA stem–only immunogen elicited a broader nAb response than full-length HA [20 , 21] . Commonly , these approaches have aimed to focus antibody responses on specific conformations or subdomains of viral proteins . In a more aggressive approach , Correia and colleagues [22] computationally designed a synthetic immunogen presenting the RSV antigenic site II and provided a proof of principle for the induction of site-specific , RSV nAbs , using a synthetic immunogen . The absence of a potent and long-lasting immune response upon natural infection is a major challenge associated with RSV , influenza virus , and other pathogens . Whereas a single exposure to pathogens like poliovirus confers life-long immunity , RSV , influenza , and other pathogens have developed mechanisms to subvert the development of a durable and potent nAb response , thereby allowing such pathogens to infect humans repeatedly throughout their lives [23] . One of the major factors hindering the induction of long-lasting protection after the first infection is related to the antibody specificities induced . Upon exposure to a pathogen , such as influenza , the human antibody responses predominantly target strain-specific antigenic sites , whereas potent bnAbs are subdominant [24] . This phenomenon is generally referred to as B cell immunodominance , which describes the unbalanced immunogenicity of certain antigenic sites within an antigen , favoring strain-specific , variable , nonneutralizing epitopes to the detriment of conserved , neutralization-sensitive epitopes [25] . The factors that determine the antigenicity of specific epitopes remain unclear , making the categorization of immunodominant and subdominant epitopes an empirical classification based on serological analysis . The presence of high levels of antibodies directed against immunodominant epitopes can sterically mask surrounding subdominant epitopes that may be targeted by bnAbs , preventing the immune system from mounting productive antibody responses against subdominant epitopes and potentially limiting vaccination efficacy [24–27] . The immunodominance hierarchy is established within the germinal center , where B cells undergo a binding affinity–based competition for available antigen and subsequently initiate a clonal expansion stage , ultimately becoming long-lived plasma cells or memory B cells [28] . Controlling this competition and driving antibody responses toward the increased recognition of subdominant , neutralizing epitopes is of primary importance to enable development of novel vaccines against pathogens that have resisted traditional strategies . One of the few strategies to guide antibody maturation was tested in the HIV field and is referred to as germline targeting , which relies upon the activation and expansion of rare but specific B cell lineages in naïve individuals [29 , 30] . In contrast , under conditions of preexisting immunity acquired during natural infection or previous vaccination , the challenge is to manipulate already established B cell immunodominance hierarchies and reshape serum antibody responses toward desired specificities . In an indirect approach toward increasing subdominant B cell populations , Silva and colleagues [31] have shown that the targeted suppression of immunodominant clones during an active germinal center reaction can allow subdominant B cell populations to overtake the germinal center response . Other approaches have used heterologous prime–boost immunization regimens with either alternative viral strains or rationally modified versions of the priming immunogen in order to steer antibody responses toward more conserved domains [32–35] . However , leveraging structural information of defined neutralization epitopes to guide bulk antibody responses toward specific , well-characterized single epitopes remains an unmet challenge . Here , we investigate whether , under conditions of preexisting immunity , a computationally designed immunogen presenting a single epitope is able to reshape serum antibody responses toward increased recognition of a specific neutralizing epitope . To mimic a scenario of preexisting immunity against a relevant pathogen , we immunized mice with a prefusion-stabilized version of RSVF and found that antibody titers against RSV antigenic site II were present in very low levels—i . e . , a subdominant site II–specific response was elicited . Based on a previously developed epitope-focused immunogen for RSV site II ( FFL_001 ) [22] , we engineered an optimized nanoparticle presenting this immunogen and investigated the potential of a rationally designed epitope-focused immunogen to boost these subdominant levels of site-specific antibodies . We show that multivalent presentation of a designed epitope-focused immunogen elicits superior levels of epitope-specific antibodies compared to prefusion RSVF in naïve mice , indicating that the subdominance of a particular epitope can be altered through its presentation in a distinct molecular context . Repeated immunizations with RSVF failed to increase site II–specific antibodies and instead further dampened site II–specific responses . In contrast , heterologous boosts with an epitope-scaffold nanoparticle enhanced serum responses toward the subdominant site II epitope , and the boosted antibodies neutralized RSV in vitro . For the first time , to our knowledge , we provide compelling evidence that synthetic immunogens comprising a single epitope can efficiently redirect specificities in bulk antibody responses in vivo and enhance subdominant nAb responses . Such strategy may present an important alternative for pathogens in which future vaccines are required to reshape preexisting immunity and elicit finely tuned antibody specificities .
In a previous study , a computationally designed , RSV site II epitope-scaffold nanoparticle was shown to elicit serum neutralization activity in nonhuman primates ( NHPs ) [22] . Despite the fact that very potent monoclonal antibodies were isolated from the immunized NHPs , the neutralization potency at the serum level was modest , indicating low titers of the potent antibodies . Therefore , our first aim was to take the best previously tested immunogen ( FFL_001 ) and further optimize delivery and immunization conditions to maximize the induction of site II–specific antibodies . A comparative study of four different adjuvants revealed that alum , an adjuvant approved for human use , yielded the highest overall immunogenicity and elicited antibodies cross-reactive with prefusion RSVF in four out of five mice ( S1 Fig ) . Next , we sought to develop an improved , easily produced nanoparticle to multimerize the epitope-scaffold for efficient B cell receptor cross-linking . Previously , Correia and colleagues [22] employed a chemical conjugation strategy of FFL_001 to a hepatitis B core antigen–based nanoparticle , which resulted in a difficult construct with a laborious purification process . Recently , several studies have reported the use of the RSV nucleoprotein ( RSVN ) as a nanoparticle platform for immunogen presentation [36 , 37] . When expressed in Escherichia coli , RSVN forms nanorings , 17 nm in diameter , containing 10 or 11 RSVN protomers [38] . We reasoned that RSVN would be an ideal particle platform to multimerize an RSV epitope-scaffold , as RSVN contains strong , RSV-directed T-cell epitopes [37] . However , our initial attempts to genetically fuse FFL_001 to RSVN yielded poorly soluble proteins that rapidly aggregated after purification . We therefore employed structure-based protein resurfacing [39] , attempting to improve the solubility of this site II epitope-scaffold when arrayed in high density on RSVN . To guide our resurfacing design process , we leveraged information from a sequence homolog of the ribosomal recycling factor ( Protein Data Bank [PDB]: 1ISE ) , the structural template originally used to design FFL_001 . Based on a sequence alignment of the mouse homolog ( National Center for Biotechnology Information [NCBI] reference: NP_080698 . 1 ) and FFL_001 , we exchanged the FFL_001 amino acids for the mouse sequence homolog and used Rosetta fixed backbone design [40] to ensure that the mutations were not energetically unfavorable , resulting in 38 amino acid substitutions ( 34 . 2% overall ) . We named this variant FFLM , whose expression yields in E . coli showed a 5-fold increase when compared to FFL_001 , and it was confirmed to be monomeric in solution ( S2 Fig ) . To confirm that the resurfacing did not alter the epitope integrity , we measured the binding affinities of FFLM to motavizumab , a high-affinity variant of palivizumab [41] , and to a panel of human site II nAbs previously isolated [5] , using surface plasmon resonance ( SPR ) . All antibodies bound with high affinity to FFLM , indicating broad reactivity of this immunogen with a diverse panel of human nAbs ( Figs 1B and S3 ) . The tested nAbs showed approximately one order of magnitude higher affinity to the epitope-scaffold as compared to the latest version of prefusion RSVF , originally called DS2 [42] , suggesting that the epitope is properly presented and likely further stabilized in a relevant conformation . The FFLM-RSVN fusion protein expressed with high yields in E . coli ( >10 mg/liter ) , forming a nanoring particle , dubbed NRM , that was monodisperse in solution , with a diameter of approximately 21 nm ( Fig 1C ) . Negative stain electron microscopy confirmed the ring-like structure as suggested by the model ( Fig 1D ) . Although we cannot fully rationalize the factors that contributed to the solubility improvement upon multimerization , our strategy to transplant surface residues from a sequence homolog to synthetic proteins may prove useful to enhance the solubility of other computationally designed proteins . We next tested the immunogenicity of NRM and its ability to elicit site II–specific antibodies . Three groups of 10 mice were subjected to three immunizations with 10 μg of NRM , monomeric FFLM , and prefusion RSVF [42] , which is currently the leading immunogen for an RSV vaccine ( Fig 2A ) . Based on the results of our adjuvant screen ( S1 Fig ) , all the immunogens were formulated in alum . As compared to FFLM , NRM showed a higher overall immunogenicity ( directed both against RSVN and FFLM ) ( Fig 2B ) . A key aspect of epitope-focused vaccines is to understand how much of the antibody response targets the viral epitope presented to the immune system . Therefore , we sought to measure the site II–specific antibody titers elicited by NRM and FFLM and compare these epitope-specific antibody responses to those elicited by prefusion RSVF . We established an SPR competition assay ( described in the Methods and shown in S4 Fig ) to quantify the fraction of site II–specific antibodies elicited by each immunogen ( FFLM , NRM , or prefusion RSVF ) . Briefly , the respective antigen was immobilized on the sensor chip surface , and the fraction of the serum antibody response competed by motavizumab was measured , serving as a proxy for site II–specific antibodies . We observed that NRM elicited site II–specific antibody responses superior to those elicited by RSVF ( Fig 2C ) . This was surprising , given that the ratio of site II epitope surface area to overall immunogen surface is similar in both NRM and RSVF ( S2 Fig ) . To confirm this finding through a direct binding assay rather than a competitive format , we measured the binding levels of sera to the site II epitope in a peptide ELISA , where the site II peptide was immobilized on a streptavidin-coated surface . Peptides mimicking site II are known to be conformationally flexible [43] ( S5 Fig ) but have been show to adopt a very similar conformation upon antibody binding to the one presented in the context of pre- and postfusion RSVF [44] . Consistent with the previous experiment , we found that NRM elicited site II–specific responses that were two orders of magnitude higher than those of RSVF ( Fig 2D ) . Together , we concluded that an epitope-focused immunogen , despite similar molecular surface area , can elicit substantially higher levels of site-specific antibodies compared to a viral fusion protein . Given the substantial site II peptide–specific serum titers elicited by NRM in mice , we investigated whether these antibodies cross-reacted with prefusion RSVF and were sufficient to neutralize RSV in vitro . Following three immunizations with NRM , all the mice ( n = 10 ) developed detectable serum cross-reactivity with prefusion RSVF ( mean serum titer = 980 ) ( Fig 3A ) . Sera also cross-reacted with the postfusion conformation of RSVF ( mean serum titer = 380 ) , but binding to virus-infected cell lysate was negligible for mice immunized with the epitope-scaffold ( S6 Fig ) . The overall quantity of RSVF cross-reactive antibodies elicited by immunization with an immunogen presenting a single epitope was found to be more than two orders of magnitude lower than those of mice immunized with prefusion RSVF , which comprises at least six antigenic sites [5] . Similarly , a B cell enzyme-linked immunospot assay ( ELISpot ) revealed that NRM-immunized mice presented prefusion RSVF-reactive antibody-secreting cells , but their frequency was approximately one order of magnitude lower than it was upon immunization with prefusion RSVF ( Fig 3C ) . The major determinant for antibody specificity is attributed to the heavy chain complementarity-determining region 3 ( HCDR3 ) [45] . Whereas for certain classes of nAbs the antibody lineages and their sequence features are well-defined ( e . g . , HIV neutralizing VRC01 class antibodies [46] or RSV-neutralizing MPE8-like antibodies [47] ) , antibodies targeting RSV antigenic site II seem to be derived from diverse precursors and do not show HCDR3 sequence convergence in humans [5] . Although we did not expect to find dominant lineages or HCDR3 sequence patterns in mice , we used next-generation antibody repertoire sequencing ( NGS ) [48] to ask whether NRM could elicit antibodies with similar sequence signatures to those elicited by prefusion RSVF . Indeed , we found 300 clonotypes , defined as antibodies derived from the same VH gene with the same HCDR3 length and 80% sequence similarity , that overlapped between NRM and the prefusion RSVF–immunized cohort , suggesting that at the molecular level , relevant antibody lineages can be activated with the NRM immunogen ( S7 Fig ) . Nine out of the 20 most expanded clonotypes in the NRM cohort were also present in mice immunized with prefusion RSVF , albeit not as expanded ( Fig 3B ) . This finding might reflect the enrichment of site II–specific antibodies in the NRM cohort ( Fig 2D ) . We further investigated whether these low levels of prefusion RSVF-binding antibodies were sufficient to neutralize RSV in vitro . Although three immunizations with prefusion RSVF elicited potent RSV-neutralizing serum titers ( mean IC50 = 10 , 827 ) , for NRM we only detected low levels of RSV-neutralizing serum activity in three out of 10 mice ( Fig 3D ) . This result is consistent with that of Correia and colleagues [22] , who observed no serum neutralization in mice but succeeded in inducing nAbs in NHPs with prior RSV seronegativity . Altogether , we concluded that despite NRM’s superior potential to induce high levels of site II–specific antibodies , the majority of antibodies activated from the naïve repertoire are not functional for RSV neutralization . A potential explanation , stemming from structural comparison between the epitope-focused immunogen and RSVF , is that these antibodies do not recognize the site II epitope in its native , quaternary environment in prefusion RSVF or on virions in sufficient amounts and with high enough affinity to potently neutralize RSV . Although vaccination studies in naïve animal models are an important first step to validate novel immunogens , previous studies [22] and results presented here imply that epitope-scaffolds may not be able to elicit robust RSV-neutralizing serum activity from a naïve antibody repertoire . However , given the high affinity of the epitope-scaffold toward a panel of site II–specific nAbs , together with the ability to elicit high titers of site II–specific antibodies in vivo , we hypothesized that such an epitope-focused immunogen could be efficient in recalling site II–specific B cells in a scenario of preexisting immunity , thereby achieving an enhanced site-specific neutralization response . Our initial immunization studies with prefusion RSVF showed that site II–specific responses were subdominant ( Fig 2C and 2D ) . Given that subdominance is a common immunological phenotype for many of the neutralization epitopes that are relevant for vaccine development [49] , we sought to test if NRM could boost subdominant antibody lineages that should ultimately be functional and recognize the epitope in the quaternary environment of the viral protein . To test this hypothesis , we designed a mouse immunization experiment with three cohorts , as outlined in Fig 4A . Following a priming immunization with RSVF , cohort 1 was boosted with adjuvant only ( “prime-only” ) , cohort 2 received two boosting immunizations with prefusion RSVF ( “homologous boost” ) , and cohort 3 received two boosts with NRM ( “heterologous boost” ) . A comparison between the prefusion RSV-immunized groups prime-only and homologous boost revealed that the two additional boosting immunizations with RSVF only slightly increased overall titers of prefusion RSVF-specific antibodies ( p = 0 . 02 ) , indicating that a single immunization with adjuvanted RSVF is sufficient to induce close to maximal serum titers against RSVF ( Fig 4B ) . Following the heterologous boost with NRM , overall RSVF-specific antibody titers remained statistically comparable to the prime-only group ( p = 0 . 22 ) . Next , we quantified the site II–specific endpoint serum titers in a peptide ELISA format ( Fig 4C ) . The homologous boost with prefusion RSVF failed to increase site II–specific antibody levels , reducing the responses directed to site II to the lower limit of detection by ELISA . This result is yet another example of the underlying complexity inherent to the fine specificity of antibody responses elicited by immunogens and how important specificities can be dampened throughout the development of an antibody response . In contrast to the homologous boost , the heterologous boost with NRM significantly increased site II peptide–specific serum titers ( p < 0 . 0001 ) . In order to understand whether this increase relied at least partially on an actual recall of antibodies primed by RSVF , or rather on an independent antibody response irrelevant for RSVF binding and RSV neutralization , we dissected the epitope specificity within the RSVF-specific serum response . In an SPR competition assay , a significantly higher fraction ( p = 0 . 02 ) of prefusion RSVF-reactive antibodies were competed by motavizumab in mouse sera primed with prefusion RSVF and boosted with NRM ( mean percent blocking = 37 . 5% ± 14 . 5% ) , as compared to mice immunized once or three times with prefusion RSVF ( 21 . 5% ± 12 . 1% or 19 . 5% ± 3 . 7% , respectively ) ( Fig 4D ) . Control mice immunized that were not primed with prefusion RSVF and instead were immunized three times with NRM did not yield detectable binding signals against prefusion RSVF in an SPR assay , despite detectable ELISA signals ( Fig 3A ) . Thus , while the heterologous boost with NRM will also prime antibodies that do not bind RSVF , the increased fraction of prefusion RSVF-binding , site II–specific antibodies is likely to arise from a recall of RSVF-primed , site II–specific antibodies . Similarly , a competition ELISA revealed that a significantly larger fraction of overall RSVF reactivity was attributed to site II–specific antibodies upon heterologous boost , as compared to both control groups ( mean percent competition = 36 . 1% ± 2 . 5% versus 22 . 6% ± 9 . 1% or 14 . 4% ± 5 . 9% , respectively , p = 0 . 002 and p < 0 . 0001 ) . In contrast , site II–specific antibodies were significantly higher in mice that received only one as opposed to three RSVF immunizations , indicating that RSVF boosting immunizations further dampened site II–specific antibody titers ( p = 0 . 03 ) ( Fig 4E ) . Together , we have shown that the serum antibody specificity can be steered toward a well-defined antigenic site by boosting preexisting , subdominant antibody levels with an epitope-focused immunogen . This is an important and distinctive feature of the epitope-focused immunogen compared to an immunogen based on a viral protein ( prefusion RSVF ) , which was shown to decrease already subdominant antibody responses under the same conditions . These results may have broad implications for strategies to control antibody fine specificities in vaccination schemes , both for RSV and other pathogens . The enhanced reactivity to site II observed in the heterologous prime–boost scheme led us to investigate if the antibodies boosted by a synthetic immunogen were functionally relevant for virus neutralization . In bulk sera , we observed 2 . 3-fold higher serum neutralization titers in mice receiving the heterologous boost ( mean IC50 = 7 , 654 ) compared to the prime-only control group ( mean IC50 = 3 , 275 ) ( Fig 5A ) . Although this increase in serum neutralization was not statistically significant , we next assessed if this increase in neutralization was driven by increased levels of epitope-specific antibodies . We observed that site II–directed antibody levels correlated with overall serum neutralization titers in the heterologous prime–boost group ( r2 = 0 . 76 , p = 0 . 0009 ) ( Fig 5B ) , whereas the prime-only ( r2 = 0 . 32 , p = 0 . 09 ) or the homologous boost cohorts showed no such correlation ( r2 = 0 . 18 , p = 0 . 22 ) ( S8 Fig ) . To characterize the RSV-neutralizing activity mediated by site II–specific antibodies , we pooled sera from each cohort , enriched site II–specific antibodies , and measured viral neutralization ( see Methods and S9 Fig ) . Briefly , we incubated pooled sera from each group with streptavidin beads , which were conjugated to biotin-labeled antigenic site II peptide , and eluted bound antibodies . To control for the quality of the enrichment protocol , we verified by ELISA that the column flow-through was depleted of site II–specific antibodies ( S9 Fig ) . In agreement with the different site II peptide–specific serum levels shown in Fig 4 , the overall quantity of site II–specific antibodies purified from equivalent amounts of sera differed between groups , with the heterologous boost and the 3x NRM groups showing the highest levels ( S9 Fig ) . Next , we tested neutralization of this polyclonal pool of site II–specific antibodies . We found that the heterologous boost cohort showed a 15-fold greater site II–specific neutralization titer ( IC50 = 99 . 6 ) as compared to the prime-only and homologous boost cohorts ( IC50 = 6 . 6 and IC50 = 4 . 9 , respectively ) . It is important to note that although the homologous boost with RSVF showed increased serum neutralizing activity compared to the prime-only control group , site II–mediated neutralization was similar . This finding is consistent with our observation that site II–specific antibodies do not increase with repeated immunizations of prefusion RSVF . Enriched antibodies from the 3x NRM group were nonneutralizing , despite the high concentration of site II–specific antibodies ( S9 Fig ) . Thus , NRM significantly enhances site II–mediated RSV neutralization but requires the priming of a relevant subset of RSVF-binding antibodies . Finally , we addressed whether this increase in site II–mediated neutralization was due to higher amounts of site II–specific antibodies or the intrinsic neutralization potency of the same antibodies . As shown in Fig 5D , antibodies from the prime-only , heterologous boost , and homologous boost cohorts exhibit similar neutralization potencies ( IC50 ranging from 1 . 7 μg/ml to 4 . 9 μg/ml ) of site II–specific antibodies . Consequently , the heterologous boosting scheme yielded higher amounts of site-specific , functional antibodies , rather than an increased potency of the same antibodies . Altogether , we dissected the mode of action of the synthetic immunogens when used as heterologous boosters , in which the observed enhanced neutralization resulted from the increase of sheer amounts of antibodies directed to site II .
Despite a rapid increase in our atomic-level understanding of antibody–antigen interactions for various pathogens , the translation of structural information into efficacious immunogens that elicit antibody responses specific to bona fide epitopes remains a key challenge for next-generation vaccine development . Multiple strategies have been investigated to focus nAb responses on defined neutralization epitopes [50] . Among them , epitope-scaffolds have been shown to elicit RSV site II–specific nAb responses in naïve NHPs . Although the overall serum neutralization was modest , a monoclonal antibody induced by vaccination showed superior neutralization potency to that of palivizumab [22] . However , a major limitation of epitope-scaffold immunogens [43 , 51 , 52] is that the quaternary environment of the epitope presented in the native viral protein is lost . Thus , the binding mode of a significant fraction of the elicited antibodies is likely incompatible with the epitope in its native environment . This observation is reinforced by our finding that although NRM elicited high serum levels of site II peptide–specific antibodies , only low levels were cross-reactive with RSVF , and neutralizing activity was residual . This finding is consistent with previous studies using epitope-scaffolds [43 , 53 , 54] . Together , these results highlight the limitations of synthetic scaffolds in an epitope-focused vaccine approach in naïve individuals . However , our finding—that an epitope that is subdominant ( site II ) in its native environment ( prefusion RSVF ) is readily targeted by the immune system when presented in a distinct molecular context ( NRM ) —supported the potential use of synthetic immunogens to reshape antibody responses toward such well-defined antigenic sites . Preexisting immunity against a viral protein ( RSVF , influenza HA , or others ) , in which certain antibody specificities are subdominant , is a common scenario in humans that have encountered repeated natural infections throughout their life [27 , 55–57] . Therefore , a major challenge for vaccine development is to boost preexisting , subdominant antibodies and enhance site-specific neutralization . To date , boosting nAbs that target specific epitopes under conditions of preexisting immunity has been challenging . For instance , strong antibody responses against immunodominant epitopes can sterically mask the neutralization epitope , preventing the induction of a potent antibody response targeting the subdominant site [24 , 26 , 27 , 58] . Overcoming these established immunodominance hierarchies is complex , as such hierarchies seem to be impacted by multiple factors , including serological antibody levels , their specificity , memory B cell counts , adjuvants , and the immunization or infection route [25] . Heterologous prime–boost schemes are a promising strategy to guide the fine specificity of antibody responses and to focus these responses on vulnerable antigenic sites . Several vaccine studies have been conducted for influenza [34 , 35] , RSV [32] , and HIV [29] , in which the heterologous immunogens were alternative strains or modified viral fusion proteins but yet not as heterologous as a computationally designed epitope-scaffold . It is possible that immunogens based on modified viral proteins retain immunodominant signatures that steer antibody responses away from the target epitopes . Although this scenario may not be fully absent in synthetic epitope-scaffolds , it is at least mitigated by the fact that the protein has not evolved under the pressure of escaping the immune system . Our study demonstrates that a heterologous boosting immunogen that optimally presents a single neutralization epitope can boost preexisting , subdominant antibody responses that target this epitope , yielding increased epitope-mediated neutralization . The ability to narrowly focus antibody responses to a single epitope that mediates clinical protection underlines the potential of rationally designed immunogens for vaccine development against elusive pathogens . In particular , our results demonstrate that although single-epitope immunogens may not be the most powerful to select functional antibodies from a naïve repertoire , they have a unique ability to boost neutralizing epitope-specific antibodies primed by a viral protein . Further studies in more relevant animal models will reveal if nAbs primed by natural infection with RSV can also be boosted , mimicking a more realistic vaccination scenario . Given that the approach presented here is generalizable and that epitope-scaffold nanoparticles can be proven successful in boosting nAbs specific for other sites , this strategy holds great potential to tune levels of antibody specificities through heterologous prime–boost vaccination schemes , which are now frequently used for challenging pathogens [29 , 34 , 59] . The original antigenic sin theory in the influenza field describes that the first viral exposure permanently shapes the antibody response , which causes individuals to respond to seasonal vaccines in a manner dependent on their immune history [24 , 60] . Seasonal vaccines generally fail to boost antibodies targeting broadly neutralization epitopes on the HA stem region [24] . Focusing antibody responses on these defined epitopes may remove the need for annual vaccine reformulation and may also protect against emerging pandemic strains [14 , 49 , 61 , 62] . The influenza vaccine challenge seems particularly well suited to our approach , considering that the human population has preexisting immunity to influenza , including some subdominant bnAbs that seasonal vaccines fail to stimulate [24] . Lastly , vaccine development against antigenically related viruses such as Zika and dengue could benefit from the approach presented here , as antibodies mounted against the envelope protein of a dengue subtype can facilitate infection with Zika [63] or other dengue subtypes [64] . A site conserved between all four dengue subtypes and Zika envelope protein has been structurally characterized and suggested for the development of an epitope-focused immunogen [7] . When seeking to apply an immunofocusing strategy to other antigenic sites and pathogens , one challenge is the development of epitope-scaffolds stably presenting the epitope in a synthetic immunogen that is compatible with antibody binding . Whereas the RSV antigenic site II is a structurally simple helix-turn-helix motif , many other identified neutralization epitopes comprise multiple , discontinuous segments . However , continuous advances in rational protein design techniques [65] will allow the design of more complex protein scaffolds to stabilize increasingly complex epitopes . Altogether , we have shown how an optimized presentation of a computationally designed immunogen in an RSVN-based nanoparticle can reshape bulk serum responses and boost subdominant nAb responses in vivo . This is a distinctive feature compared to using prefusion RSVF as a boosting immunogen and underscores how subdominant epitopes can be converted to immunodominant epitopes when presented in a different environment . We foresee the great promise of this strategy to overcome the challenge of boosting and focusing preexisting immunity toward defined neutralization epitopes , potentially applicable to multiple pathogens .
All animal experiments were approved by the Veterinary Authority of the Canton of Vaud ( Switzerland ) according to Swiss regulations of animal welfare ( animal protocol number 3074 ) . The previously published RSV site II epitope-scaffold ( “FFL_001” ) [22] was designed based on a crystal structure of a mutant of ribosome recycling factor from E . coli ( PDB entry 1ISE ) . Using BLAST , we identified sequence homologs of 1ISE from eukaryotic organisms and created a multiple-sequence alignment with clustal omega ( CLUSTALO [1 . 2 . 1] ) [66] of the mouse homolog sequence ( NCBI reference NP_080698 . 1 ) , 1ISE , and FFL_001 . Surface-exposed residues of FFL_001 were then mutated to the respective residue of the mouse homolog using the Rosetta fixed backbone design application [40] , resulting in 38 surface mutations . Amino acid changes were verified to not impact overall Rosetta energy score term . NRM was size excluded in PBS on a Superose 6 column ( GE Healthcare ) and diluted to a concentration of 0 . 015 mg/ml . The sample was adsorbed to a glow-discharged carbon-coated copper grid ( EMS , Hatfield , PA , United States ) washed with deionized water and stained with a solution of uranyl formate 0 . 75% . Observation was made using an F20 electron microscope ( Thermo Fisher , Hillsboro , OR , USA ) operated at 200 kV . Digital images were collected using a direct detector camera Falcon III ( Thermo Fisher , Hillsboro , OR , USA ) 4 , 098 × 4 , 098 pixels . Automatic data collection was performed using EPU software ( Thermo Fisher , Hillsboro , OR , USA ) at a nominal magnification of 50 , 000× , corresponding to a pixel size of 2 Å , and defocus range of −1 μm to −2 μm . Contrast transfer function for each image was estimated using CTFFIND4 [68] . One thousand particles of nanorings were picked using XMIPP manual-picking utility within SCIPION framework [69] . Manually picked particles were used as input into XMIPP auto-picking utility , resulting in 13 , 861 particles . Particles were extracted and binned to have the box size of 100 pixels , corresponding to the pixel size of 4 Å; phase-flipped; and subjected for three rounds of reference-free 2D classification without contrast transfer function correction in RELION-3 . 0 Beta [70] . SPR experiments were performed on a Biacore 8K at room temperature with HBS-EP+ running buffer ( 10 mM HEPES [pH 7 . 4] , 150 mM NaCl , 3 mM EDTA , 0 . 005% v/v Surfactant P20 ) ( GE Healthcare ) . Approximately 100 response units ( RU ) of FFLM were immobilized via amine coupling on a CM5 sensor chip ( GE Healthcare ) . Serial dilutions of site II–specific Fabs were injected as analyte at a flow rate of 30 μl/minute with 120 seconds of contact time . Following each injection cycle , ligand regeneration was performed using 0 . 1 M glycine ( pH 2 ) . If not stated otherwise , data analysis was performed using 1:1 Langmuir binding kinetic fits within the Biacore evaluation software ( GE Healthcare ) . Six-week-old , female Balb/c mice were ordered from Janvier labs and acclimatized for 1 week . Immunogens were thawed on ice and diluted in PBS ( pH 7 . 4 ) to a concentration of 0 . 2 mg/ml . The immunogens were then mixed with an equal volume of 2% Alhydrogel ( Invivogen ) , resulting in a final Alhydrogel concentration of 1% . Other adjuvants were formulated according to manufacturer’s instructions . After mixing immunogens and adjuvants for 1 hour at 4°C , each mouse was injected with 100 μl , corresponding to 10 μg immunogen adsorbed to Alhydrogel . All immunizations were done subcutaneously , with no visible irritation around the injection site . Immunizations were performed on days 0 , 21 , and 42 . Blood ( 100–200 μl ) was drawn on days 0 , 14 , and 35 , and the maximum amount of blood ( 200–1 , 000 μl ) was taken by cardiac puncture at day 56 , when mice were euthanized . Nunc MediSorp plates ( Thermo Scientific , #467320 ) were coated overnight at 4°C with 100 μl of antigen ( recombinant RSVF , FFLM , and NRM ) diluted in coating buffer ( 100 mM sodium bicarbonate [pH 9] ) at a final concentration of 0 . 5 μg/ml . For blocking , plates were incubated for 2 hours at room temperature with blocking buffer ( PBS + 0 . 05% Tween 20 [PBST] supplemented with 5% skim milk powder [Sigma , #70166] ) . Mouse sera were serially diluted in blocking buffer and incubated for 1 hour at room temperature . Plates were washed five times with PBST before adding 100 μl of anti-mouse HRP-conjugated secondary antibody diluted at 1:1 , 500 in blocking buffer ( Abcam , #ab99617 ) . An additional five washes were performed before adding Pierce TMB substrate ( Thermo Scientific , #34021 ) . The reaction was stopped by adding 100 μl of 2 M sulfuric acid , and absorbance at 450 nm was measured on a Tecan Safire 2 plate reader . Each plate contained a standard curve of motavizumab to normalize signals between different plates and experiments . Normalization was done in GraphPad Prism . The mean value was plotted for each cohort , and statistical analysis was performed using GraphPad Prism . Nunc MaxiSorp ELISA plates ( Thermo Scientific , #44-2404-21 ) were coated with heat-inactivated , frozen-thawed cell lysates from Hep2 cells that were infected for 48 hours with RSV [37] . A control lysate was prepared from uninfected Hep2 cells to subtract background signals . ELISA was performed as described for the antigen ELISA . Prior to incubation with a coated antigen plate , sera were serially diluted in the presence of 100 μg/ml competitor antigen and incubated overnight at 4°C . ELISA curves of a positive control , motavizumab , are shown in S10 Fig . Curves were plotted using GraphPad Prism , and the area under the curve ( AUC ) was calculated for the specific ( NRM ) and control ( RSVN ) competitor . Percent competition was calculated using the following formula [71]: %competition= ( 1− ( AUC ( specificcompetitor ( NRM ) ) AUC ( controlcompetitor ( NR ) ) ) ) *100 The antigenic site II was synthesized as peptide by JPT Peptide Technologies , Germany . The following sequence was synthesized and biotinylated at the N terminus: MLTNSELLSKINDMPITNDQKKLMSNNVQI For ELISA analysis of peptide-reactive serum antibodies , Nunc MediSorp plates were coated with 5 μg/ml streptavidin ( Thermo Scientific , #21122 ) for 1 hour at 37°C . Subsequently , ELISA plates were blocked as indicated above , followed by the addition of 2 . 4 μg/ml of the biotinylated site II peptide . Coupling was performed for 1 hour at room temperature . The subsequent steps were performed as described for the antigen ELISA . Approximately 300 RU of antigen was immobilized via amine coupling on a CM5 chip . Mouse sera were diluted 1:100 in HBS-EP+ running buffer and flowed as analyte with a contact time of 120 seconds to obtain an initial RU ( RUnon-blocked surface ) . The surface was regenerated using 50 mM NaOH . Sequentially , motavizumab was injected four times at a concentration of 2 μM , leading to complete blocking of motavizumab binding sites as confirmed by signal saturation . The same serum dilution was reinjected to determine the remaining response ( RUblocked surface ) . The delta serum response ( SR ) corresponds to the baseline-subtracted , maximum signal of the injected sera . Percent blocking was calculated as follows: %blocking= ( 1− ( ΔSRblocked surfaceΔSRnon−blocked surface ) ) *100 A schematic representation of the SPR experiment is shown in S4 Fig , and calculated blocking values are shown in S1 Data . B cell ELISpot assays were performed using the Mouse IgG ELISpot HRP kit ( Mabtech , #3825-2H ) according to the manufacturer’s instructions . Briefly , mouse spleens were isolated and pressed through a cell strainer ( Corning , #352350 ) to obtain a single-cell suspension . Splenocytes were resuspended in RPMI media ( Gibco , #11875093 ) supplemented with 10% FBS ( Gibco ) , Penicillin/Streptomycin ( Gibco ) , 0 . 01 μg/ml IL2 , 1 μg/ml R848 ( Mabtech , #3825-2H ) , and 50 μM β-mercaptoethanol ( Sigma ) for approximately 60 hours of stimulation at 37°C , 5% CO2 . ELISpot plates ( PVDF 96-well plates , Millipore , #MSIPS4510 ) were coated overnight with 15 μg/ml antigen diluted in PBS , followed by careful washing and blocking using RPMI + 10% FBS . Live splenocytes were counted , and the cell number was adjusted to 1 × 107 cells/ml . Serial dilutions of splenocytes were plated in duplicates and incubated overnight with coated plates . After several wash steps with PBS buffer , plates were incubated for 2 hours with biotinylated anti-mouse total IgG ( Mabtech , # 3825-6-250 ) in PBS , followed by incubation with streptavidin conjugated to HRP ( Mabtech , #3310–9 ) for 1 hour . Spots were revealed using tetramethylbenzidine ( TMB , Mabtech , #3651–10 ) and counted with an automatic reader ( Bioreader 2000; BioSys GmbH ) . Results were represented as number of spots per 106 splenocytes . The RSV A2 strain carrying a luciferase gene ( RSV-Luc ) was a kind gift of Marie-Anne Rameix-Welti , UFR des Sciences et de la Santé , Paris . Hep2 cells were seeded in Corning 96-well tissue culture plates ( Sigma , #CLS3595 ) at a density of 40 , 000 cells/well in 100 μl of Minimum Essential Medium ( MEM , Gibco , #11095–080 ) supplemented with 10% FBS ( Gibco , 10500–084 ) , L-glutamine 2 mM ( Gibco , #25030–081 ) , and penicillin-streptomycin ( Gibco , #15140–122 ) and grown overnight at 37°C with 5% CO2 . Sera were heat inactivated for 30 minutes at 56°C . Serial 2-fold dilutions were prepared in an untreated 96-well plate using MEM without phenol red ( M0 , Life Technologies , #51200–038 ) containing 2 mM L-glutamine , penicillin + streptomycin , and mixed with 800 pfu/well RSV-Luc ( corresponding to a final MOI of 0 . 01 ) . After incubating diluted sera and virus for 1 hour at 37°C , growth media were removed from the Hep2 cell layer , and 100 μl/well of the serum-virus mixture was added . After 48 hours , cells were lysed in 100 μl buffer containing 32 mM Tris ( pH 7 . 9 ) , 10 mM MgCl2 , 1 . 25% Triton X-100 , 18 . 75% glycerol , and 1 mM DTT . Lysate ( 50 μl ) was transferred to a 96-well plate with white background ( Sigma , #CLS3912 ) . Lysis buffer ( 50 μl ) supplemented with 1 μg/ml luciferin ( Sigma , #L-6882 ) and 2 mM ATP ( Sigma , #A3377 ) was added to each well immediately before reading luminescence signal on a Tecan Infinite 500 plate reader . On each plate , a palivizumab dilution series was included to ensure comparability of neutralization data . In our assay , we determined IC50 values for palivizumab of 0 . 32 μg/ml , which is similar to what other groups have reported [41] . The neutralization curve was plotted and fitted using the GraphPad variable slope fitting model , weighted by 1/Y2 . Streptavidin agarose beads ( 400 μl , Thermo Scientific , #20347 ) were pelleted at 13 , 000 rpm for 2 minutes in a tabletop centrifuge and washed with PBS . Biotinylated site II peptide ( 200 μg ) was incubated for 2 hours at room temperature to allow coupling of biotinylated peptide to streptavidin beads . Beads were washed three times with 1 ml PBS to remove excess of peptide and resuspended to a total volume of 500 μl bead slurry . Mouse sera from the same cohort ( n = 10 ) were pooled ( 4 μl each , 40 μl total ) in a total volume of 200 μl PBS , and 90 μl diluted sera were mixed with 150 μl of bead slurry , followed by an overnight incubation at 4°C . Beads were pelleted by centrifugation , and the supernatant was carefully removed by pipetting . Beads were then washed twice with 200 μl PBS , and the wash fractions were discarded . To elute site II–specific antibodies , beads were resuspended in 200 μl elution buffer ( 0 . 1 M glycine [pH 2 . 7] ) and incubated for 1 minute before centrifugation . Supernatant was removed , neutralized with 40 μl neutralization buffer ( 1 M Tris [pH 7 . 5] , 300 mM NaCl ) , and stored at −20°C for subsequent testing for RSV neutralization . As a control , unconjugated streptavidin was used for each sample to account for nonspecific binding . | Vaccines are one of the most valuable instruments to prevent and control infectious diseases . Their primary correlate of protection is the level of induction of neutralizing antibodies that target critical antigenic sites and thereby block infection . Natural infections with pathogens such as the respiratory syncytial virus ( RSV ) or influenza induce a broad repertoire of antibodies that target multiple epitopes . Among those , functional antibodies with key specificities are often subdominant ( present in low titers ) . Thus , a central goal for vaccine development is to focus antibody responses on such neutralization epitopes . Here , we show that a computationally designed , epitope-focused immunogen mimicking an important RSV neutralization epitope ( site II ) can focus antibodies onto this well-defined epitope . In a scenario of preexisting immunity , in which site II–specific antibodies were subdominant , the epitope-focused immunogen selectively boosted site II–specific antibodies , resulting in an increased viral neutralization through this epitope . We propose that rationally designed immunogens spotlighting defined epitopes have a unique potential to focus antibody responses on functionally conserved sites in cases of preexisting immunity . Our results have broad implications for vaccine design as a strategy to steer preexisting antibody responses away from immunodominant , variable epitopes and toward subdominant epitopes that confer broad and potent neutralization . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"enzyme-linked",
"immunoassays",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"microbiology",
"viruses",
"preventive",
"medicine",
"immunologic",
"adjuvants",
"rna",
"viruses",
"antibodies",
"immunologic",
"techniques",
"vaccination",
"and",
"immunization",
"antibody",
"response",
"research",
"and",
"analysis",
"methods",
"respiratory",
"syncytial",
"virus",
"immune",
"system",
"proteins",
"public",
"and",
"occupational",
"health",
"paramyxoviruses",
"proteins",
"medical",
"microbiology",
"antigens",
"immunoassays",
"microbial",
"pathogens",
"immune",
"response",
"biochemistry",
"physiology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"vaccine",
"development",
"organisms"
] | 2019 | Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen |
The basal ganglia are important for action selection . They are also implicated in perceptual and cognitive functions that seem far removed from motor control . Here , we tested whether the role of the basal ganglia in selection extends to nonmotor aspects of behavior by recording neuronal activity in the caudate nucleus while animals performed a covert spatial attention task . We found that caudate neurons strongly select the spatial location of the relevant stimulus throughout the task even in the absence of any overt action . This spatially selective activity was dependent on task and visual conditions and could be dissociated from goal-directed actions . Caudate activity was also sufficient to correctly identify every epoch in the covert attention task . These results provide a novel perspective on mechanisms of attention by demonstrating that the basal ganglia are involved in spatial selection and tracking of behavioral states even in the absence of overt orienting movements .
The basal ganglia are known to govern behavior by disinhibiting desired actions and inhibiting undesired actions [1] . The basal ganglia have also been implicated in perceptual and cognitive functions [2] , such as the encoding of object values [3] and action values [4] , and signals related to visual decision-making [5 , 6] . Neurons in caudate nucleus , one of the major input structures of the basal ganglia , display a number of decision-related signals as monkeys formulate their perceptual choice ( reported by a saccadic eye movement ) during a visual motion discrimination task [5] , consistent with a possible role for the basal ganglia in regulating the use of sensory evidence . Computational studies have provided a general framework that might account for these diverse functions , suggesting that the basal ganglia act as an integration center that plays a crucial role in representing the subject’s “belief state" about the current context that helps constrain the process of action selection [7] . If this view of basal ganglia function is correct , then during tasks involving spatial attention—for example , selectively basing a perceptual decision on a stimulus at one location while actively ignoring a distracter stimulus at a different location—one might expect to find neuronal signals in the caudate related to spatial selection and the internal encoding of belief states , even when no overt action or goal-directed movement is required . Spatial selection in the caudate nucleus has been studied principally during tasks requiring a goal-directed movement , either with the eyes [8] or arms [9] . In both cases , a subset of caudate neurons exhibits a degree of spatial selection as the monkey anticipates a movement instruction . Caudate neurons also show some spatial selection during the delay period preceding an action directed to the particular location [10] . In these paradigms , it is ambiguous whether the spatially selective activity is related to the visual location itself or to the spatial goal of the movement . In the antisaccade paradigm , which dissociates the visual target location from the movement endpoint , some caudate neurons have higher activity for antisaccades than for prosaccades [11 , 12] . However , even in this task , the instructional cue and movement endpoint are tightly linked , because antisaccades require a goal-directed eye movement to the location diametrically opposite the visual cue . Consequently , it is not known whether caudate activity can be spatially selective when animals attend covertly to a particular visual location that has no link to the end point of a goal-directed movement . Another well-documented contribution of the basal ganglia in the primate is the coordination of motor outputs by grouping individual movements into action “chunks” [13] during sequences of eye movements [4] or arm movements [14] . A recent study in rats found that striatal neurons were activated sequentially throughout the course of the delay period when animals had to wait before making a response , suggesting that sequence-related activity in the striatum might be a component of spatial working memory [15] . These studies raise the possibility that sequence-related activity in the primate striatum might also apply to the successive behavioral states that subjects pass through during the performance of covert spatial selection tasks in the absence of goal-directed or orienting movements , but this possibility has not yet been directly tested . To test whether the primate striatum plays a more general role in spatial selection in the absence of overt movements , we examined the activity of caudate neurons while monkeys performed a covert attention task . Animals were trained to covertly monitor a peripheral visual motion stimulus and report when the direction of motion changed by releasing a joystick; unlike previous studies , the task required spatial selection but did not include movements toward a spatial goal . Our results demonstrate that the primate striatum is involved in covert spatial selection by showing that ( 1 ) caudate neurons strongly discriminated the location of behaviorally relevant events , even though no goal-directed movement was involved during different crucial periods of the trial; ( 2 ) this spatially selective activity required the presence of a distracter and often disappeared when only a single visual stimulus was presented , indicating that the spatial selection was not only related to reward expectation; ( 3 ) caudate neurons often showed response-choice activity that also depended on the visual configuration; and ( 4 ) the pattern of activity across caudate neurons was sufficient to correctly identify epochs in the covert attention task . Our results illustrate a possible common thread between the motor and cognitive functions of the basal ganglia .
All procedures and animal care were approved by the National Eye Institute Animal Care and Use Committee and complied with the Public Health Service Policy on the humane care and use of laboratory animals . Data were collected from two adult monkeys ( Macaca mulatta; Monkey R , 11 kg; Monkey P , 14 kg ) . Under isoflurane and aseptic conditions , we surgically implanted plastic headposts and recording chambers . In both animals , recording chambers ( 28 × 20 mm ) were tilted laterally 35 degrees and aimed at the caudate head and body ( 20 mm anterior , 6 mm lateral ) . The animals were seated in a primate chair ( Crist Instrument , Hagerstown , MD , United States ) with their head fixed inside a darkened booth . Animals were positioned 48 cm in front of a 100 Hz VIEWPixx display ( VPixx Technologies , Saint-Bruno , QC , Canada ) , and experiments were controlled using a modified version of PLDAPS [16] . Eye position was monitored using an EyeLink 1000 infrared eye-tracking system ( SR Research , Ottawa , Ontario , Canada ) . Animals reported their choices using a joystick while maintaining central fixation . Joystick movements were measured as changes in voltage from a single-axis joystick ( CH Products , model HFX-10 ) mounted in the front wall of the primate chair and oriented so that the monkey deflected the joystick downward to initiate and continue each trial and released the joystick back to its central neutral position to indicate a response . Joystick release times ( reaction times ) were computed by detecting the onset of the step change in voltage from the joystick , similar to saccade detection . Neurons were recorded using tungsten in glass-coated electrodes with impedances of 1–2 MOhm ( Alpha Omega , Alpharetta , GA , US ) . Electrode position was controlled with a stepping motor microdrive ( NAN Instruments , Nazaret Illit , Israel ) . The electrical signal was amplified and filtered , and single-unit activity was recorded online using the Plexon MAP system spike sorting software ( Plexon , Dallas , TX , US ) . Spike waveforms were analyzed again off-line to confirm that recordings were of single well-isolated neurons . We recorded neurons in the head and body of the left caudate nucleus for both animals with a range from anterior commissure ( AC ) AC+7 to AC−5 for Monkey R and AC+5 to AC−2 for Monkey P ( with AC as anterior commissure at AP20 , Fig 1C ) . Neurons were considered to be in caudate nucleus according to their location ( based on MRI scans ) and their low background activity at >10 mm below the dural surface . In this study , we recorded only from phasically active neurons ( PANs ) , which we identified based on their low background activity compared to the tonic activity from the cholinergic interneurons ( TANs ) . Single units with low or unstable firing rates across the session or with no task-related activity were excluded from the analysis . Upon isolating spikes from a caudate neuron , we first tested neurons with an MGS task in most cases ( 179/227 , 78% ) . The monkey fixated a central spot for 0 . 5 s , after which a spot stimulus was flashed for 0 . 15 s at one of eight possible peripheral locations . The monkey maintained fixation until the fixation point turned off , at which point the monkey made a saccade to the memorized cued location within 0 . 5 s in order to receive a reward . We defined four different periods to test whether activity was modulated: visual ( 0–0 . 5 s ) and delay ( 0 . 5–1 s ) periods aligned on stimulus onset , saccade ( −0 . 2:0 . 1 s ) and postsaccadic ( 0 . 1:0 . 5 s ) periods aligned on saccade onset . A total of 60% ( 108/179 ) of caudate neurons tested with MGSs were significantly modulated for at least one of these four temporal periods ( Wilcoxon rank sum test , p < 0 . 05 comparisons with baseline activity [−0 . 4:0 s] ) , with the majority showing a preference for the contralateral side during the visual ( 55% ) and saccade ( 57% ) periods . Because not all caudate neurons were modulated during MGSs , we also tested most neurons with a joystick task ( 190/227 , 84% ) . The monkey held down the joystick and fixated a central spot in order for a peripheral white square stimulus to appear ( 2 or 4 locations were tested ) . On most trials ( 90% ) , after a random interval ( 1 . 5–3 . 5 s ) , the peripheral stimulus dimmed , and the monkey was rewarded for releasing the joystick within 1 s after the dimming . In 10% of the trials , the peripheral stimulus did not dim , and the monkey was rewarded for continuing to hold down the joystick . The joystick task allowed us to check whether caudate neurons activities were modulated by joystick release , and by varying the location of the dimming stimulus , we could also assess preferences for visual stimulus location . A total of 57% of caudate cells show significant activity during this joystick task during at least one of these three periods ( visual [0:0 . 5 s]; delay [0 . 5:1 s] or joystick release [−0 . 2:0 . 1 s] ) compared to a baseline period [−0 . 4:0 s from stimulus onset]; most did not show any side preference ( [visual: 29%/14%/57%] , [delay: 23%/12%/65%] , and [joystick release: 27%/21%/52%] for contralateral , ipsilateral , or neither , respectively ) . A small proportion of caudate neurons ( 8% , 18/227 ) were not tested with MGSs or the joystick task but only during the motion-direction change detection ( CD ) task . All caudate neurons were tested using a motion-direction change detection task performed covertly during maintained central fixation , essentially identical to that described previously [17] , except we used a joystick rather than a button press . Briefly , the monkey started a trial by fixating a central white square and holding down a joystick for 0 . 25 s . A peripheral cue ring was then presented for 0 . 2 s to indicate which location in the visual field the animal should monitor . The cue was placed either at the neuron’s preferred location ( as determined by MGSs or joystick task ) or at the diagonally opposite location . We placed the cue around the horizontal meridian ( <30 degrees ) when no preferred location was determined by MGSs or joystick task . The location of the ring was blocked for 68 successive trials . The cue ring was extinguished , and after 0 . 5 s , when only the fixation point was still present , two motion patches ( described below ) were presented , one at the same location as the spatial cue and the other one ( the foil ) in the diagonally opposite location ( Fig 1A ) . The direction of motion in the cued and foil patch was varied day to day but always differed by 90 degrees . We placed the motion stimuli at locations expected to evoke maximal activity for each neuron , based on the modulation observed during the MGS and joystick task . The average eccentricity was 12 degrees ( range: 10–13 degrees ) ; in most cases , stimuli were placed on or near the horizontal meridian ( <30 degrees ) . In cases for which no obvious spatial selectivity was observed during the MGS or joystick tasks , the motion stimuli were placed at an eccentricity of 12 degrees along the horizontal meridian , since these locations tended to be effective at modulating neurons in the attention task . The visual motion stimuli were circular patches of moving dots , with the direction of motion of each dot drawn from a normal distribution with a mean value ( defined as the patch motion direction ) and a 16-degree standard deviation . The lifetime ( 10 frames , 100 ms ) , density ( 25 dots/deg2/s ) , and speed of the dots ( 15 deg/s ) were held constant . The radius of the aperture varied between 3 and 3 . 75 degrees , depending on the eccentricity of the patch; the median value was 3 . 25 degrees . Luminance of the fixation dot and of each moving dot in the motion patches was 45 cd/m2 . The background luminance of the monitor was 9 . 9 cd/m2 . The monkey was trained to release the joystick if the motion-direction changed in the patch at the previously cued location; otherwise , he should keep holding the joystick down for trials that had no motion-direction change or a motion-direction change in the foil patch . On each trial , a single motion-direction change could occur anytime 1 . 0–4 . 3 s after the onset of the motion stimuli . The proportions of trials with a motion-direction change at the cued location or foil location or had no change were 57% , 29% , and 14% , respectively . The size of the motion-direction change was adjusted based on psychometric tests of each monkey to keep performance near threshold level ( 75% of performance ) , depending on visual field location and motion direction; the median direction changes were 28 and 26 degrees for Monkeys R and P , respectively . Clockwise and counterclockwise direction changes were equally likely and randomly chosen . After the motion-direction change , the stimuli remained on the screen for 1 s or until the animal released the joystick . Hits were defined as joystick releases that occurred within 1 s of a motion-direction change in the cued patch . False alarms were defined as incorrect joystick releases when the motion-direction change occurred at the foil location . Correct rejections were defined as successful nonreleases when no change occurred at the cued location . Monkeys were rewarded with a small drop of liquid ( apple juice mixed with water ) at the end of each correctly performed trial ( hits and correct rejections ) . The monkeys were required to maintain fixation of the central square for the entire duration of the trial ( until after their joystick release ) ; otherwise , the trial was aborted . At the beginning of each block of trials , the monkeys performed the motion-direction CD task with only one stimulus ( single-patch condition ) during the first 10–12 trials of each block . The single motion patch stimulus was located either at the cued location within the block ( 100% of the trials for Monkey R , 50% for Monkey P ) or at the foil location ( 50% of the trials for Monkey P ) . For Monkey P , the presentation of the single motion patch was preceded by the presentation of the spatial cue . Monkeys were rewarded for correct detection of MC at the cued location and successful nonreleases when no change occurred at the cued location ( Monkey R and P ) or when a change occurred at the foil location ( Monkey P only ) . For analysis of single-patch trials , we only used trials in which the single patch was preceded by a cue so that the sequence of stimulus events was directly comparable to the two-patch condition . Hit rates for single-patch conditions were 77 . 6% and 77 . 0% for Monkeys R and P , respectively , confirming that the size of the motion-direction change was set near the threshold level . Data from the motion-direction CD task were collected in 105 recording sessions in Monkey R and 60 recording sessions in Monkey P . We obtained neuronal recordings ( n = 227 neurons ) for an average of 200 trials per location of spatial cue; neurons recorded for fewer than 100 trials in the task were excluded from analysis . We defined contralateral trials as trials in which the spatial cue was presented on the side of the visual field contralateral to our recording sites in the caudate nucleus . Monkeys were first trained on the task using their right hand ( contralateral to the recording sites ) , which was used during all recordings except the 38 sessions ( 44 neurons ) when the right and left hands were used in separate interleaved blocks . For visualizing neuronal activity , we computed PSTHs using nonoverlapping bins of 0 . 02 s . To visualize neuronal activity across the population of caudate neurons , we computed normalized PSTHs by dividing the raw values from each time bin by the maximum firing rate ( peak of each neuron’s PSTH ) across all conditions ( i . e . , either contralateral or ipsilateral conditions , whichever was higher ) . We used the timing of the peak of each neuron’s PSTH to determine the categories of neurons for Fig 2 ( "pre-cue” [−0 . 95 to −0 . 7 s] , “post-cue” [−0 . 7 to 0 s] , “visual” [0 to 0 . 5 s] , and “delay” [0 . 5 to 1 s] from motion stimuli onset ) . For analysis of neuronal activity , the firing rates within different temporal windows were computed from the trial-by-trial spike counts . To compare firing rates , we performed nonparametric statistical tests such as Wilcoxon signed rank ( paired or not paired ) or Kruskal-Wallis test with a significant threshold of p < 0 . 05 . We corrected p-values using Holm’s variant of the Bonferroni method . To quantify spatial cue–related modulation , we computed a standard attention cue modulation index defined as [Rcontra − Ripsi]/[Rcontra + Ripsi] , where Rcontra and Ripsi are the mean activity on the contralateral trials and ipsilateral trials , respectively . Mean activity was computed in different temporal windows: “pre-cue” ( −0 . 25 to 0 . 02 s ) before the spatial cue onset , “post-cue” ( 0 . 2 to 0 . 6 s ) after the spatial cue onset , “visual” ( 0 . 1 to 0 . 5 s ) after the motion stimuli onset , and “delay” ( 0 . 5 to 1 s ) after the motion stimuli onset . We compared those mean activities with baseline activity ( −0 . 95:−0 . 75 s ) before the spatial cue onset ( Wilcoxon signed rank p < 0 . 05 ) . This modulation index was also computed separately for trials in the single-patch condition . To analyze response-choice activity , we first identified neurons that showed significant changes in activity after the change in the visual motion stimulus . We aligned the data on the time of the motion-direction change and compared spike counts after the MC ( 0 . 1 to 0 . 6 s ) to those before the MC ( −0 . 5 to 0 ) and identified a subset of caudate neurons that had significantly higher activity after the MC ( 80/227 , 35% , Wilcoxon signed rank test , p < 0 . 05 ) . We restricted our analysis to this subset of caudate neurons . We aligned activity on the joystick release for hit responses ( separately for contralateral- and ipsilateral-change trials ) , identified the time of peak activity by fitting a Gaussian function to the data from −0 . 5 to 0 . 5 s with respect to joystick release , and then measured the activity within a 0 . 3 s window centered on the peak . The spike counts from this 0 . 3 peak-centered window were then used for further analysis of response-choice activity . We used a standard receiver operating characteristic ( ROC ) analysis [18] to determine the sensory and motor-related preferences of neurons during the response-choice epoch . For each neuron , we did three ROC-style analyses . The first analysis assessed how well response-choice activity discriminated the location of the visual MC event . We divided correctly performed trials based on where the motion-direction change occurred ( hits contralateral versus hits ipsilateral ) ; for these two different types of trials , the action was the same ( releasing the joystick ) , but the location of the visual event was different . The area under the ROC curve ( AROC ) quantifies how well the location of the visual event could be discriminated based on the activity of each neuron , following a convention with values greater ( less ) than 0 . 5 indicating a preference for the contralateral ( ipsilateral ) side . The second analysis assessed how well response-choice activity discriminated the two behavioral outcomes ( detect probability; hits versus misses ) ; the sensory conditions were the same , but the response choice was different ( release versus hold ) . For this analysis , outcome values greater ( less ) than 0 . 5 indicated a preference for hits ( misses ) . The third analysis ( neuronal sensitivity ) assessed how well response-choice activity discriminated the presence or absence of the MC event in either motion patch ( cued or foil ) . For this analysis , outcome values greater ( less ) than 0 . 5 indicated a preference for presence of the MC event ( absence ) . To analyze data for trials without joystick releases , we aligned activity on the median reaction time computed for hit trials separately for contralateral and ipsilateral conditions . Significance of ROC values was evaluated using bootstrapped ( 1 , 000 iterations ) 95% CIs . We also analyzed neuronal activity for three other cases in which the joystick was released: ( 1 ) false alarms , when the monkey incorrectly released the joystick for stimulus changes at the foil location; ( 2 ) joystick breaks , when the monkey incorrectly released the joystick when neither cued nor foil stimulus changed; and ( 3 ) joystick trial end , when the monkey appropriately released the joystick at the end of correct rejection trials to initiate the next trial . Only neurons with at least five occurrences for each type of these three cases were used for analysis . Spike counts for each neuron were measured from a 0 . 3-s window identical to that used to analyze response choice activity as described above . For each trial from every caudate neuron ( n = 227 ) , we obtained spike counts in the motion-direction change task from 14 unique nonoverlapping epochs , defined by different time periods during the trial ( n = 7 ) and whether the cue was ipsilateral or contralateral ( n = 2 ) . The seven time periods were ( 1 ) “pre-cue , ” a 0 . 23-s epoch starting 0 . 25 s before cue onset; ( 2 ) “post-cue , ” a 0 . 4-s epoch starting 0 . 1 s after cue onset; ( 3 ) “visual , ” a 0 . 4-s epoch starting 0 . 1 s after motion patch onset; ( 4 ) “delay , ” a 0 . 5-s epoch starting 0 . 5 s after motion patch onset; ( 5 ) “change contra , ” a 0 . 5-s epoch starting 0 . 1 s after a motion-direction change in the contralateral patch; ( 6 ) “change ipsi , ” a 0 . 5-s epoch starting 0 . 1 s after a motion-direction change in the ipsilateral patch; and ( 7 ) “change neither , ” a 0 . 5-s epoch matched in time to the two preceding change epochs . We first used the “svmtrain” and “svmclassify” functions in Matlab ( version R2015b , The Mathworks , Natick , MA , US ) to train and test a linear binary classifier for each of the 14 epochs defined above . For each classifier , we randomly drew ( with replacement ) a single-trial spike count from each neuron from the corresponding epoch to generate a single-trial feature set ( n = 227 neurons ) and then repeated this procedure multiple times ( n = 150 ) to make the full data set for that classifier . Using 120/150 of the trials , each classifier was trained to distinguish data from its particular epoch from data pooled together from all of the other epochs . The remaining 30/150 trials were held in reserve to test and cross-validate the performance of the classifier with data from its own epoch . In addition , each of the 14 classifiers was also tested with the reserve data from each of the other 13 classifiers individually to generate a confusion matrix ( i . e . , epochs that might be identified by more than one classifier ) . Thus , even though each classifier was trained in a binary fashion ( i . e . , one epoch versus the remaining 13 lumped together ) , it was tested in a multiclass manner ( i . e . , each epoch tested individually ) . For each classifier , this procedure was then repeated 1 , 000 times , and the fifth percentile from the distribution of outcomes was compared to chance performance to identify significant results ( reported as medians ) . In a second analysis , we followed the same procedure using data from the epochs related to response choice ( epochs 5–7 defined above , for contralateral and ipsilateral cue conditions ) , subdivided based on trial outcome ( hit , correct reject , miss , false alarm: uncued change , and joystick breaks: no change ) . We trained classifiers for the four possible correct trial outcomes and then tested these four classifiers on all 10 possible trial outcomes; the training was restricted to four outcomes to avoid overfitting the data and to test whether error responses involved the same patterns of activity as correct responses . A bootstrap procedure ( 1 , 000 repeats ) was used again to assess significance .
Caudate neurons showed several distinctive patterns of activity during the early epochs of the attention task , when the spatial cue and the motion patches were presented . To illustrate the range of activity patterns , we show the time course of spike counts ( PSTHs ) from four example caudate neurons , sorted by whether the spatial cue was presented contralateral ( orange ) or ipsilateral ( blue ) to the recording site ( Fig 2A ) . The activity of many caudate neurons was related to the location and timing of the spatial cue . Some of these neurons exhibited phasic activity that preceded the appearance of the spatial cue ( Neuron a , Fig 2A ) , suggesting the presence of an anticipatory signal made possible by the 68-trial blocking of spatial cue conditions and the fixed temporal period ( 0 . 25 s ) before the appearance of the spatial cue . The pre-cue activity of this neuron was slightly but significantly larger when the cue was contralateral than when it was ipsilateral ( p = 0 . 017 , Wilcoxon rank sum test , period [−0 . 250 to −0 . 020 before cue onset] , 18 . 5 spikes per second ( sp/s ) on average for contralateral trials versus 14 . 4 sp/s for ipsilateral trials ) . Other neurons showed phasic activity after , and presumably evoked by , the spatial cue ( Neuron b ) . The post-cue activity for this neuron was much larger when the cue was presented on the ipsilateral side ( Wilcoxon rank sum test , period [0 . 1 to 0 . 5 s after cue onset] , p < 0 . 001 , 0 . 5 sp/s for contralateral trials versus 4 . 1 sp/s for ipsilateral trials ) . For other caudate neurons , activity was mostly related to the presentation of the motion stimuli and the delay period of the attention task . Some neurons exhibited large phasic responses to the onset of the motion stimuli , followed by activity that extended into the delay period of the task ( Neuron c , Fig 2A ) , with a strong preference based on the location of the spatial cue that emerged shortly after cue onset; this side preference nearly eclipsed the visual phasic response in the nonpreferred condition , even though the visual stimuli were identical across the two cue conditions . The delay period activity also varied across caudate neurons . Some showed a distinctive ramp-like pattern toward the end of the delay period without any side preference ( Neuron d ) . To visualize the activity patterns across our population of caudate neurons ( n = 227 ) during the early phases of the task , we normalized each neuron’s spike counts and rank-ordered all of the neurons based on the times of their peak activity ( see Materials and methods ) . The representation of these results ( Fig 2B ) , aligned on motion stimulus onset separately for contralateral and ipsilateral cue conditions , illustrates that the sample neurons in Fig 2A were exemplars of features present across the population . Specifically , based on the timing of peak activity , we classified neurons into four different groups ( indicated by labels in pink gradient ) . The first group of neurons ( Pre-cue , light pink , n = 48 ) showed peaks of activity preceding the appearance of the spatial cue ( like Neuron a ) . Neurons in this group tended to exhibit phasic activity for both contralateral and ipsilateral cue conditions , with slightly higher activity for contralateral ( as indicated by both the higher normalized activity for the contralateral plot and the larger proportion of orange horizontal tics in the side bar of Fig 2B ) . The second group of neurons ( Post-cue , n = 76 ) increased their activity after the presentation of the spatial cue ( like Neuron b ) . These post-cue neurons tended to show higher activity in the ipsilateral cue condition ( as indicated by the higher normalized activity and the larger proportion of blue tics in the side bar of Fig 2B ) . The timing of this activity was distributed across the post-cue epoch , including just after cue onset ( neurons #50–60 ) , after cue offset ( neurons #80–100 ) , and just before motion stimuli onset ( neurons #115–120 ) . The third group of neurons ( Visual , n = 70 ) had responses that appeared to be evoked by the onset of the motion stimuli ( like example Neuron c ) . These visual neurons sometimes also exhibited cue-related activity before motion stimuli onset and lower sustained activity into the delay period . The phasic visual response showed a preference for the ipsilateral cue condition ( neurons #140–170 ) , whereas the preferences during the delay period were more equally split . For the last group of neurons ( Delay , n = 33 ) , the peak of activity occurred well after the presentation of the motion stimuli and into the delay period ( 0 . 5 s and longer after motion stimuli onset ) . These “delay” neurons tended to show a ramping pattern of activity ( like example Neuron d ) , similar to that described previously [5] , and had a slight preference for the contralateral cue condition . The categorization of caudate neurons did not depend on the particular procedure used to rank the neurons based on the timing of their peak activity ( S2 Fig ) . To quantify the cue-related modulation , we computed a modulation index for spike counts within different temporal periods ( Fig 2C , see Materials and methods ) and found clear distinctions between the groups of caudate neurons . The neurons with prominent pre-cue activity ( n = 48 ) were weakly modulated by the spatial cue condition ( Fig 2C ) . Only 4% ( 2/48 ) of this group showed significantly different activity based on cue condition , one preferring contralateral and the other one preferring ipsilateral ) . In contrast , the post-cue and visual neurons were strongly modulated by spatial cues . Most of the post-cue neurons ( 34/76 , 45% ) displayed a significant effect of cue condition , with almost 2/3 showing a preference for ipsilateral ( 22/34 , 64% ) . Similarly , some visual neurons ( 29/70 , 41% ) showed significant cueing effects , again mostly in favor of the ipsilateral cue condition ( 21/29 , 72% ) . The amplitude of the cueing effects for post-cue and visual neurons was large—the median cue modulation index was −0 . 55 and −0 . 46 ( post-cue and visual , respectively ) for the ipsilateral condition and 0 . 35 and 0 . 34 for the contralateral condition . We observed the ipsilateral bias for post-cue and visual periods also through the full population of caudate neurons ( t test , p < 0 . 05 ) while the distributions of Attention Modulation Indices ( AMIs ) for pre-cue and delay periods were centered on 0 ( t test , p > 0 . 05 ) . Finally , neurons defined by their delay period activity were also modulated by spatial cues; a quarter of the neurons ( 8/33 ) showed a significant effect , with a slight preference for the contralateral side ( 6/8 ) . We unexpectedly found that the spatial cue modulation was dependent on the presence of a visual distracter during the covert attention task . For visual neurons ( n = 70 ) , we compared activity during the standard two-patch version of the attention task with activity during a simpler single-patch version that omitted the distracter . During this single-patch task , a single motion patch was presented at the contralateral or ipsilateral location , and the animal was again rewarded for releasing the joystick if the motion direction in the single patch at the cued location changed . This comparison revealed that the visual activity of many caudate responses was selective for visual conditions that included the second distracter patch . For example , Neuron #1 in Fig 3A showed a strong preference during the two-patch attention task for ipsilateral placement of the cued stimulus ( blue versus orange , top left quadrant ) . This preference completely disappeared during the single-patch condition ( lower left quadrant ) , demonstrating that the spatial selectivity of this neuron was specific to visual conditions in which an ipsilateral cued stimulus was accompanied by a contralateral distracter . Some caudate neurons were like Neuron #1 and displayed spatial selectivity during the two-patch version of the covert attention task but lost their side preference during the single-patch condition ( green dots in Fig 3B , n = 27/70 , significant AMI for two-patch but not single-patch condition ) ; a majority of these neurons ( 20/27 ) had a preference for the ipsilateral side ( i . e . , AMI < 0 for the two-patch condition ) . Less common were neurons like Neuron #2 ( Fig 3A ) , which exhibited a side preference during both single-patch and two-patch conditions ( violet dots , n = 13 ) . Most of these neurons ( 9/13 ) retained a consistent side preference across visual conditions like Neuron #2 , whereas 4 neurons preferred the ipsilateral side in the two-patch condition but preferred the contralateral side in the single-patch condition . An additional eight neurons had a significant side preference for the single-patch but not the two-patch condition ( blue dots , n = 8 ) . Finally , some neurons ( gray dots , n = 22/70 ) did not show a side preference during either the single-patch or two-patch condition . The visual activity of some caudate neurons was selective to the visual configuration during the covert CD task and could not be predicted by either the MGS or joystick mapping tasks . In a population of 160 neurons tested in all three tasks , we found that most caudate neurons did not show the same spatial selectivity ( Fig 3C ) across the different tasks . For the neurons that preferred the contralateral side during the post-cue period of the CD task ( left column , orange rows , n = 32/160 ) , only 10/32 showed the same spatial selectivity for MGS and joystick task during the visual periods , and only 2/43 neurons showed congruent selectivity for the ipsilateral side . In summary , we observed cue-related modulation across all of the early epochs of the attention task , indicating that these cueing effects were not related to the delivery of the reward or to behavioral outcomes at the end of the trial . The effects were strongest in the post-cue and visual epochs and larger for ipsilateral than contralateral spatial cues . The visual cue–related modulation for many caudate neurons depended on the visual configuration—it was specific to the CD paradigm , required the presence of a visual distracter , and disappeared when only a single visual stimulus was presented at the preferred location . As might be expected from previous results implicating the caudate nucleus in movement sequencing and procedural learning , a subset of our caudate neurons was modulated during the joystick response choice . However , even among these neurons , neuronal activity was not simply movement related but also exhibited unexpected selectivity for the visual and task conditions . Among the subset of neurons with activity modulated during the response choice ( n = 80 , defined in Materials and methods ) , we found that response-choice activity could be driven by sensory signals ( i . e . , where the MC happened ) , motor-related signals ( i . e . , whether or not the animal released the joystick ) , or combinations of the two . For example , the response-choice activity of Neuron #1 in Fig 4A combined a preference for contralateral over ipsilateral motion-direction changes ( top ) with a preference for hits over misses ( bottom ) . To quantify these sensory and choice-related signals , we used ROC analyses to measure ( 1 ) the neuronal MC selectivity by comparing spike counts on contralateral hits to ipsilateral hits ( AROC sensory ) and ( 2 ) the detect probability by comparing spike counts on trials with hits versus misses ( AROC motor ) . For Neuron #1 , both ROC analyses were significantly greater than chance ( AROC of 0 . 88 and 0 . 6 , sensory and motor , respectively ) , confirming that the response-choice activity of this neuron combined a preference for hits with a preference for stimulus changes in the contralateral visual field . In contrast , Neuron #2 preferred hits over misses ( AROC: 0 . 71 ) but had no preference between contralateral and ipsilateral change-event locations ( AROC: 0 . 5 ) , suggesting that the response-choice activity of this neuron was predominantly related to the joystick release . Similar mixed dependencies were observed across the caudate neurons with response-choice activity . Some caudate neurons were like Neuron #1 and combined a preference for change-event location with a preference for hits over misses ( green dots in Fig 4B , n = 32/80 , both AROCs were significantly different from chance level 0 . 5 ) ; most ( 23/32 ) of these neurons preferred contralateral change events ( AROC > 0 . 5 ) , with an almost exclusive preference for hits ( 31/32 ) . Almost as common were neurons like Neuron #2 , which signaled the motor choice without a preference for change-event location ( red dots , n = 30 ) . Finally , a smaller number of neurons ( blue dots , n = 13 ) exhibited response-choice activity that discriminated the location of the MC ( and generally preferred contralateral ) but did not show any difference between hits and misses . The remaining six neurons showed selectivity for neither ( black dots ) . Despite these distinctions , caudate neurons did not form exclusive categories based on their response-choice activity but showed a continuum of mixed preferences for change-event location and motor choice , as illustrated by the broad scatter of data points in Fig 4B . We also confirmed that aligning the response-choice activity on the time of the motion-direction change rather than on the time of joystick release ( S3 Fig ) did not change the proportions of caudate neurons in these different categories ( sensorimotor , motor , sensory , and neither ) . We also tested the time course of AROC sensory and the detect probabilities for the entire duration of the trial ( see S4 Fig ) . Consistent with Fig 2 , spatial selectivity was evident after cue onset and motion stimulus onset but also after the MC ( S4 Fig ) . Significant detect probabilities were not present early in the trial ( i . e . , after motion stimulus onset ) but increased markedly after the motion-direction change ( S4C Fig ) , suggesting that the commitment to release the joystick was triggered by the motion-direction change event itself rather than formed endogenously earlier in the trial . We next tested the modulation of caudate responses to the presence or absence of the change event . Fig 4C shows an example of a typical caudate neuron whose activity was significantly modulated by the presence or absence of the MC and also for hits versus misses , independently of the cue location ( contralateral/ipsilateral ) . We measured the correlation between neuronal sensitivity and detect probability across our population of neurons , separately for contralateral and ipsilateral cue conditions ( see Materials and methods ) . Neuronal MC sensitivity was significantly and positively correlated with the detect probability for both contralateral and ipsilateral cueing conditions ( Fig 4D , contralateral trials , R = 0 . 49 , p < 10−6; ipsilateral trials , R = 0 . 49 , p < 10−6 ) , indicating that caudate neurons with greater sensitivity to the visual event were also more strongly predictive of the response choice . Among these response-choice neurons , there was no evident preference for a particular epoch earlier in the trial ( S1 Table ) . Together , these results show that during the response choice itself , the activity of many caudate neurons was selective not only for the motor choice but also for the spatial location of the relevant stimulus event . Moreover , caudate neurons that better detected the presence or absence of the MC were also better at predicting whether or not the monkey would release the joystick . These findings are consistent with caudate neurons establishing a link between the occurrence of a specific sensory event and the decision to commit to a particular motor response . The phasic response-choice activity was also dependent on the task condition in which the joystick was released . We considered three other situations during the covert attention task when the animal released the joystick , in addition to the case of “hits” analyzed in Fig 4 . First , we analyzed activity during “false alarms , ” when the animals incorrectly released the joystick for a MC that occurred at the foil location . Second , we analyzed “joystick breaks , ” when the animals released the joystick but there was no MC event at either stimulus location . Third , we analyzed activity during “joystick releases” at the end of correct reject trials , when the animal was obliged to release the joystick in order to end the trial . We verified that the dynamics of the joystick release was the same across different trial outcomes by analyzing the voltage changes associated with the joystick movement ( S1 Fig ) . Mean activity for false alarms was not different from that for hits for most of the neurons ( Fig 5A; 12/14 , 86% contralateral; 12/14 , 86% for ipsilateral , Wilcoxon rank sum test , p > 0 . 05 ) . Similarly , mean activity for joystick breaks was equivalent to activity for hits for most of the neurons ( Fig 5B; 35/46 , 76% contralateral; 25/31 , 81% for ipsilateral , Wilcoxon rank sum test , p > 0 . 05 ) . In contrast , mean activity for hits was significantly larger than mean activity for joystick release at the end of correct-reject trials ( Fig 5C , Wilcoxon signed rank , p < 0 . 001 for contralateral , p < 0 . 001 for ipsilateral ) . These results indicate that the response-choice activity was specific to joystick releases associated with the choice about the visual motion stimulus . As an additional test of the specificity of this response-choice activity , for a subset of caudate neurons , we tested whether the hand used to release the joystick made a difference . We defined the contralateral and ipsilateral hand relative to the recording site of the neurons , as we did for spatial cue location . We recorded a total of 44 neurons in 38 behavioral sessions ( n = 25 for Monkey R and n = 13 for Monkey P ) . Behavioral performance was not different when animals used their contralateral hand or the ipsilateral hand with only minor differences in hit rates between the two hands ( R: 65 . 2% ipsilateral hand , 66 . 3% contralateral hand , p = 0 . 777 , Wilcoxon signed rank test; P: 57 . 7% ipsilateral hand , 65 . 4% contralateral hand , p = 0 . 002 ) . Among the population of neurons that showed choice-related phasic activity ( n = 20 ) , there was no preference for one particular hand . Indeed , mean responses for correct responses to MCs on the contralateral side ( orange dots ) or ipsilateral side ( blue dots ) did not depend on which hand was used to release the joystick ( Wilcoxon rank , p = 0 . 681 contralateral change , p = 0 . 575 ipsilateral change , Fig 5D ) . Thus , the features of the phasic activity related to joystick release support the use of the term “response-choice”—this activity was specific to joystick releases associated with choices about the visual motion stimulus but was largely unaffected by which hand was used . However , the response-choice activity was strongly affected by the visual and task conditions ( Fig 6 ) . We compared caudate neuronal activity for joystick releases during three different conditions: ( 1 ) the standard two-patch attention task , ( 2 ) the single-patch version of the attention task introduced earlier , and ( 3 ) a joystick task in which the monkey released the joystick when a single peripheral square stimulus reduced its luminance . Many caudate neurons exhibited response-choice activity only during the two-patch condition ( green dots in Fig 6A , n = 25/70 , AROC different from chance level 0 . 5 for two patches only ) and did not show any side preference during the joystick task ( green dots in Fig 6B , n = 21/70 ) . Another group of caudate neurons showed a dependence on the location of the visual stimulus evoking the joystick release , and this side preference was retained across the three different task conditions ( Fig 6A , violet dots , n = 14/80 ) and also during the dimming joystick task ( Fig 6B , violet dots , n = 21/70 ) . For all of these neurons , the side preference remained the same across visual conditions ( Fig 6A , n = 14/14 ) and tasks ( Fig 6B , n = 19/21 ) . To facilitate comparison of neuronal activity across these three task conditions ( two patches , single patch , or joystick task ) , we illustrated the spatial selectivity of each neuron ( n = 70 ) using a color-coded format similar to that used in Fig 3C . Some caudate neurons ( 15/39 , 38% ) lost the spatial selectivity exhibited during the attention task when we tested them with the other tasks ( Fig 6C , rows colored blue or orange for “CD” but gray for “single patch” and “joystick task” ) , indicating that the spatial selectivity was dependent on the presence of the distracter stimulus even during the response-choice period . However , in contrast to the side preference exhibited during earlier trial epochs ( e . g . , Fig 3c ) , most neurons with a side preference during the response choice preferred the contralateral side during the two-patches version ( left column , orange rows , n = 30/70 ) . Overall , like the visual cue–related modulation of caudate neurons described earlier , the response-choice activity was also often specific to the visual and task conditions—in particular , the presence of a visual distracter—even though the act of releasing the joystick was the same and was largely unaffected by which hand was used . Our previous analyses illustrated that caudate neurons exhibited diverse patterns of activity that appeared to cover the different trial conditions and the full time course of the attention task , including the visual periods and also the response choice . We next wanted to confirm this result by testing whether there was sufficient information contained in the spike counts from our population of caudate neurons to correctly identify the epochs and cue conditions in the attention task using linear classifiers ( support vector machine [SVM] ) . As illustrated in Fig 7A and described in more detail in the Materials and methods section , we identified 14 unique epochs based on the cueing condition and trial events in the attention task , trained a separate classifier for each of these epochs , and then cross-validated and quantified the performance of each classifier by testing it with spike counts from its own epoch as well as each of the other 13 epochs individually . To visualize the results , in Fig 7B , we display the performance of each classifier in matrix format , using a color scale to indicate the percentage of test trials identified by each classifier for each of the 14 trial epochs . The set of classifiers performed significantly above chance for 12 of the 14 epochs , with no significant misclassifications except for the pre-cue epochs ( Fig 7B ) . The post-cue epochs and MC epochs were classified with higher accuracy ( 90% and 93% correct ) , regardless of whether the cue was contralateral or ipsilateral . The pre-cue epochs were also classified better than chance ( 67% and 53% correct ) , presumably reflecting the fact that cue conditions were blocked , although the two pre-cue epochs were also the only cases with significant , mutual misclassifications ( 30% and 27% errors ) , maybe because these epochs are less valuable for representing the task sequence compared to the other ones . The two no-change epochs ( “change: none” in Fig 7B ) were the only epochs that were not correctly identified above chance level . Because caudate neuronal activity was related to response choice and also contained sufficient information to identify epochs of the attention task ( Fig 7B ) , we tested whether the spike counts from our population of caudate neurons could identify the trial outcomes during the attention task . Specifically , we sought to determine if caudate activity during the MC epoch was related to the stimulus condition or to the perceptual choice ( Fig 7C ) . To this end , we subdivided the data from the change epoch used previously for the linear classifier analysis ( epoch 5–7 and 12–14 , Fig 7B ) based on the trial outcome ( hit , correct reject , miss , false alarm , and joystick break ) . Consistent with the previous analysis , we subdivided erroneous releases of joystick into two types—joystick releases for MC on the uncued side ( false alarms ) and joystick releases for no MC ( joystick breaks ) . We adopted the strategy of training classifiers on data from the four types of correct trials ( hits and correct rejects for contralateral and ipsilateral cue conditions ) and then testing these four classifiers with data from all 10 trial outcomes . The results demonstrate that caudate neuronal activity represents the response choice , correct or incorrect , especially when the cue is on the contralateral side ( Fig 7C ) . Classifiers identified correctly performed trials ( hits and correct rejects ) with high probability ( 90%–100% ) ; these same classifiers also identified particular classes of errors . For example , the classifier for contralateral hits also identified 97% of joystick breaks committed during contralateral cues with no MC ( upper-left row of matrix in Fig 7C ) , consistent with erroneous detection of an MC that did not happen . On the other hand , the contralateral hits classifier only rarely identified false alarms committed during contralateral cues with ipsilateral MC ( 10% ) ; these were more frequently identified by the ipsilateral hits classifier ( 30% ) , consistent with erroneous coding of the cue condition . The classifier for contralateral correct rejects also identified 53% of misses committed during contralateral cues , suggesting that the motion-direction change was simply not detected . For the ipsilateral classifiers , the outcomes were slightly different . The classifier for ipsilateral correct rejects also identified misses ( 37% ) , but the classifier for ipsilateral hits identified both types of erroneous releases ( false alarms and joystick breaks ) committed during ipsilateral cues ( 53% and 63% ) , albeit with lower probabilities . These results quantitatively demonstrate that the pattern of spike counts across our caudate neurons represented the perceptual choices made during the covert attention task , on both correct and error trials . Choices during contralateral and ipsilateral cue conditions are both represented , but the patterns associated with erroneous releases ( false alarms and joystick breaks ) are different between the two cue conditions .
We found that the activity of caudate neurons was strongly modulated by spatial cues during the covert attention task , consistent with previous findings that neurons in the caudate head are modulated by task instructions [10 , 19] . Indeed , the size of the cue-related modulation we found for caudate neurons was much larger than what has been typically reported for visual cortical areas during visual attention tasks similar to ours . The firing rates of our caudate neurons were modulated by spatial cues by more than 100% ( median modulation indices were 0 . 35–0 . 55 , Fig 2C ) , compared with the more modest changes of approximately 8% found for visual area V1 [20] , 10%–20% for middle temporal area ( MT ) [21] , approximately 26% for visual area V4 [20] , approximately 40% for medial superior temporal area ( MST ) [22 , 23] , and 25%–50% found for lateral intraparietal area ( LIP ) [24 , 25] . The attention cue–related changes we observed for caudate neurons are more consistent with the larger cueing effects found for neurons in frontal cortical areas such as the frontal eye fields [26] and dorsolateral prefrontal cortex [27] . Our caudate recordings also included a substantial number of neurons that showed higher activity for ipsilateral spatial cues , similar to what has been found for dorsolateral prefrontal cortex [27] and unlike the preference for contralateral spatial cues found in visual cortex . These similarities in spatial cueing effects between frontal cortex and our caudate neurons are consistent with the known anatomy . The frontal cortex provides direct projections to regions in the head of the caudate nucleus that overlap with the locations of our recording sites [28]; visual cortical areas implicated in our CD task would be expected to project to more posterior regions around the genu of the caudate nucleus [29] . The large and bilateral spatial cueing effects we found for caudate neurons are presumably related to aspects of selective attention that lie outside of the changes in sensory processing . One possibility is that these cueing effects are related to spatial working memory . Selective attention and working memory are often studied as separate behavioral phenomena , but performing an attention task requires remembering the location of the cue , and conversely , there is substantial evidence that working memory may rely on rehearsal using visual selection mechanisms [30 , 31] . Neurons in dorsolateral prefrontal cortex are well known for the sustained delay-period activity that could support working memory [32 , 33] , but this sustained activity is also consistent with representing the currently attended location [34] . Our results demonstrate that similar signals are present on caudate neurons , and the design of our selective attention task allows us to attribute this activity to visual working memory rather than to movement planning , because our task involved a nonspatial joystick response and no targeting saccades . This interpretation is consistent with recent caudal evidence that the output of the basal ganglia biases sensorimotor processing that takes place in other structures but is not necessarily the route responsible for evaluating the sensory evidence itself [6 , 35] . Another possibility is that the spatial selectivity was related to reward expectation , which is often closely linked to spatial attention [36] . For example , previous studies showed that visual or memory-related responses of caudate neurons may be modulated by the expectation of reward [37] . Indeed , caudate neurons can exhibit selective activity for visual cues that predict the direction of rewarded saccadic eye movements when those visual cues are defined by spatial location [38 , 39] or by nonspatial features such as color [37] . In our task , the delivery of reward was not related to a goal-directed movement , because animals responded by releasing or holding down the same joystick across all task conditions . We also showed that spatial selectivity cannot be explained by the expectation of reward in general , because it was present for the covert attention task but not for other task conditions ( single patch , joystick task , and MGS task ) . Thus , although it is difficult to completely rule out some relationship with reward expectation , our results would point to a novel form that is specifically associated with the conditions of our covert spatial attention task . We also showed that the cue-related modulation of caudate neurons was strongly dependent on the visual and task conditions . One possible explanation is that this selectivity is a byproduct of movements that are planned but then not executed: subjects in our covert attention task might have preferred to look directly at the cued motion patch if they had been allowed to break central fixation . However , the properties of our caudate neurons are not consistent with this explanation . We found that the spatial selectivity exhibited during the attention task was not well predicted by the spatial preferences during MGSs or the other joystick mapping task , indicating that it is unlikely that the cue-related modulation during the attention task was simply due to movement planning . However , it remains possible that some of the cue-related modulation was related to subtle movements of the body that might also depend on the visual and task conditions . In addition , the visual-evoked activity of caudate neurons was specifically modulated during the covert attention task when the second distracter patch was present but not when only a single patch was presented . Similarly , the response-choice activity of many caudate neurons was specific to motion-direction changes in one visual hemifield only when a distracter was present , even though the physical act of releasing the joystick was the same across all tasks and did not involve a goal-directed movement . These results demonstrate that choice-related activity in the caudate includes the covert spatial selection of behaviorally relevant events in addition to the overt preparation of orienting movements [5 , 40] . We also found that in caudate nucleus , sensitivity and detect probability were correlated , suggesting that the perceptual decision might be based on the activity of the most sensitive neurons; similar results have been shown in cortical areas MT [41] and LIP [42] . These observations are consistent with the caudate being part of the circuit for implementing the perceptual decision during the covert attention task . We found that different subsets of caudate neurons showed phasic cue-related modulation during particular time epochs so that the population of neurons tiled the full duration of the task , even during the response choice . Previous studies have demonstrated that caudate neurons can represent a task sequence when animals perform a sequence of oriented movements [4 , 14] and that caudate activity tiles the full duration of the trials [43] . Because our covert attention task did not include overt movements to the attended location , our results are substantively different from these previous findings and thus demonstrate activity related to task sequence rather than action sequence . This type of sequential transient activation of caudate neurons is strikingly similar to a pattern recently reported in the dorsomedial striatum of rats during a non-match to position task [15]; as in our results , this sequential activity could not be explained by changes in motor activity and was present over the entire task , not just the delay period . One might argue that the sequential activation of caudate neurons was simply related to time and not the task sequence itself . However , the cue-related modulation provides compelling evidence that activity depended on the sequence of events—for example , many caudate neurons showed visual- and choice-related activity that was specific to the visual field location of the preceding spatial cue even though the timing of events was the same across these conditions . Our results are also consistent with the idea that the striatum plays a central role in representing the “belief states” needed to guide action selection [7] . Using a set of linear classifiers , we found that the pattern of activity across our population of caudate neurons correctly identified trial epochs throughout the visual attention task . Because most of these trial epochs involved maintained fixation and the absence of any overt action , these results demonstrate that caudate activity is not only important for tracking sequences of actions , as previously shown [4 , 14] , but also for tracking the sequence of seemingly quiescent behavioral states that are necessary to perform a covert attention task [44] . This perspective nicely dovetails with recent evidence showing that caudate neuronal activity encodes several aspects of perceptual decision-making during a visual motion direction–discrimination task [5] . As in our data set , the caudate neurons recorded by Ding and Gold [5] showed a strong dependence on sensory and task conditions , a diversity of activity patterns and timing , and that only a subset of neurons had activity linked to the action—in their case , a saccade to one of two choice targets . They interpret their results within the framework of accumulation of sensory evidence toward a decision boundary [45] , which has been an enormously fruitful approach; it may be useful now to consider how this sensory-based decision fits into the longer sequence of behavioral steps needed to perform the task . If it is true that the pattern of caudate activity represents a sequence of belief states , then each transition from one distinct pattern to another may correspond to a “decision” , even if the design of the experiment is mostly concerned with the particular state transition that is linked to the animal’s overt choice . This interpretation provides an answer to a long-standing question about the accumulator model—namely , what determines when the accumulation process should start . From the viewpoint of the sequence of behavioral states , the accumulation process would start with the “pre-choice” state that immediately precedes the “choice” state . The transition to this “pre-choice” state , presumably guided by other sensory events and the learned temporal structure of the task , is itself a type of decision , albeit a covert one . For example , in our case , we observed ramp-like activity similar that observed by to Ding and Gold [5] , but it began during the delay period prior to the abrupt motion-direction change ( Fig 2 ) , suggesting that this was not an accumulation of sensory evidence but a gradual change in belief based on elapsed time [46] , urgency [47] , or other information . Our results provide new insights into the functions of the basal ganglia and how they may contribute to selective visual attention . We found that neurons in the primate caudate nucleus strongly select the spatial location of behaviorally relevant stimulus during specific visual contexts , even in the absence of any overt motor response , and that caudate neuron activity is sufficient to identify the epochs of a covert attention task . These results demonstrate that the role of the basal ganglia in selection extends to nonmotor aspects of behavior and illustrate how the tracking of behavioral states by striatum and related circuits could support nonmotor , cognitive functions such as attention and decision-making , as well as motor functions such as action selection . | The ability to respond selectively to sensory inputs is a crucial brain function , one that is implicated in variety of high-level brain disorders . The basal ganglia are a set of evolutionarily ancient structures best known for their role in controlling motor actions and more recently implicated in nonmotor cognitive functions . Here we show for the first time , to our knowledge , that neuronal activity in the caudate nucleus , one of the major input structures of the basal ganglia , is modulated during a covert selective attention task . Two monkeys were trained to detect a subtle change in a visual motion stimulus at a cued location while ignoring a similar stimulus at a second , uncued location . Monkeys were not allowed to look directly at the stimuli , but they monitored them covertly and released a joystick to report their choices . We found that caudate neurons strongly selected the spatial location of the relevant stimulus throughout the task even in the absence of any overt movement . This spatially selective activity was dependent on task and visual conditions and could be dissociated from goal-directed actions . These results advance our understanding of the basal ganglia and provide a novel perspective on the brain circuits for selective attention . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"decision",
"making",
"brain",
"social",
"sciences",
"vertebrates",
"neuroscience",
"animals",
"mammals",
"motor",
"neurons",
"primates",
"cognitive",
"psychology",
"cognition",
"amniotes",
"vision",
"monkeys",
"animal",
"cells",
"basal",
"ganglia",
"cellular",
"neuroscience",
"psychology",
"sensory",
"cues",
"eukaryota",
"cell",
"biology",
"anatomy",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"sensory",
"perception",
"cognitive",
"science",
"attention",
"organisms"
] | 2018 | Covert spatial selection in primate basal ganglia |
Many neglected tropical infectious diseases affecting humans are transmitted by arthropods such as mosquitoes and ticks . New mode-of-action chemistries are urgently sought to enhance vector management practices in countries where arthropod-borne diseases are endemic , especially where vector populations have acquired widespread resistance to insecticides . We describe a “genome-to-lead” approach for insecticide discovery that incorporates the first reported chemical screen of a G protein-coupled receptor ( GPCR ) mined from a mosquito genome . A combination of molecular and pharmacological studies was used to functionally characterize two dopamine receptors ( AaDOP1 and AaDOP2 ) from the yellow fever mosquito , Aedes aegypti . Sequence analyses indicated that these receptors are orthologous to arthropod D1-like ( Gαs-coupled ) receptors , but share less than 55% amino acid identity in conserved domains with mammalian dopamine receptors . Heterologous expression of AaDOP1 and AaDOP2 in HEK293 cells revealed dose-dependent responses to dopamine ( EC50: AaDOP1 = 3 . 1±1 . 1 nM; AaDOP2 = 240±16 nM ) . Interestingly , only AaDOP1 exhibited sensitivity to epinephrine ( EC50 = 5 . 8±1 . 5 nM ) and norepinephrine ( EC50 = 760±180 nM ) , while neither receptor was activated by other biogenic amines tested . Differential responses were observed between these receptors regarding their sensitivity to dopamine agonists and antagonists , level of maximal stimulation , and constitutive activity . Subsequently , a chemical library screen was implemented to discover lead chemistries active at AaDOP2 . Fifty-one compounds were identified as “hits , ” and follow-up validation assays confirmed the antagonistic effect of selected compounds at AaDOP2 . In vitro comparison studies between AaDOP2 and the human D1 dopamine receptor ( hD1 ) revealed markedly different pharmacological profiles and identified amitriptyline and doxepin as AaDOP2-selective compounds . In subsequent Ae . aegypti larval bioassays , significant mortality was observed for amitriptyline ( 93% ) and doxepin ( 72% ) , confirming these chemistries as “leads” for insecticide discovery . This research provides a “proof-of-concept” for a novel approach toward insecticide discovery , in which genome sequence data are utilized for functional characterization and chemical compound screening of GPCRs . We provide a pipeline useful for future prioritization , pharmacological characterization , and expanded chemical screening of additional GPCRs in disease-vector arthropods . The differential molecular and pharmacological properties of the mosquito dopamine receptors highlight the potential for the identification of target-specific chemistries for vector-borne disease management , and we report the first study to identify dopamine receptor antagonists with in vivo toxicity toward mosquitoes .
Mosquitoes ( Class Insecta; Order Diptera; Family Culicidae ) vector multiple neglected tropical diseases ( NTDs ) affecting human health , including malaria , yellow-fever , dengue and filariasis . Historically , insecticides employed against arthropod disease vectors have reduced the impact of NTDs , but unfortunately , continued disease control is threatened by the widespread development of vector populations that are resistant to insecticidal chemistries [1] . This issue is further complicated by the fact that there has not been a new public health insecticide produced for vector-borne disease control for over 30 years [2] . Recently , philanthropic investment has focused attention toward the development of new drugs to control NTDs in the human population [3] . It is widely recognized that an arsenal of new vector control solutions are required in order to meet this and other public health goals regarding NTDs . Thus , the research community should aggressively pursue the discovery of new mode-of-action chemistries for mosquito control through both traditional phenotypic screening and target-based approaches . Novel insecticide targets potentially exist among the arthropod G protein-coupled receptors ( GPCRs ) . These proteins comprise a large family of membrane-bound molecules that mediate critical biological processes such as neurotransmission , vision , and hormonal regulation , among others [4] , [5] . GPCRs are extensively targeted for drug development in humans - approximately 40% of prescription pharmaceuticals interact with these receptors [6] - and more recently , Gamo et al . [7] reported multiple GPCR-interacting chemistries as promising anti-malarial leads . Also , the mode-of-action of amitraz , a chemistry registered for tick and insect control , is presumed to have partial agonistic activity at an octopamine sensitive GPCR [8] . More than 100 different GPCRs have been identified in the genomes of multiple insect species , including malaria- and yellow fever-transmitting mosquitoes [9] , [10] . These studies have provided a basis for the functional characterization of GPCRs and their prioritization as potential subjects for insecticide development . The biogenic amine-binding GPCRs ( rhodopsin-like ) are integral components of the central and peripheral nervous systems of eukaryotes and include receptors that bind the neurotransmitters dopamine , histamine , octopamine , serotonin , tyramine , and acetylcholine [11] . The dopamine receptors are classified as either D1- or D2-like [12] based on their differential functional roles . Ligand binding to the D1-like dopamine receptors causes Gαs-mediated stimulation of adenylyl cyclase ( AC ) production of cAMP . A reciprocal effect is observed following agonist activation of D2-like dopamine receptors , whereby cAMP production by AC is inhibited via Gαi/o proteins . Dopamine and its receptors are essential for complex behavioral mechanisms in arthropods such as locomotion [13] , [14] , [15] , arousal [16] , and olfactory learning [17] , [18] . The importance of dopaminergic-related functions has stimulated research to understand these processes in mosquitoes . Dopamine and serotonin have been tied to salivary gland functioning of vectors [19] , [20] and may have an impact on pathogen acquisition and transmission during blood feeding . Andersen et al . [21] reported that increased levels of dopamine were detected in Aedes aegypti following a blood meal that were implicated in ovarian or egg development , and in newly-emerged adults , presumably as part of the sclerotization process . Much attention has been given to the role of dopamine in the melanization pathway of mosquitoes and other insects , as well as the effect of dopamine on development , pigmentation , reproduction , immune responses to parasites , wound healing , and Wolbachia infection [22] , [23] , [24] , [25] , [26] , [27] . In the mosquito Culex pipiens , dose-dependent increases in cAMP were detected following treatment with dopamine and octopamine in homogenized head tissues , suggesting the presence of Gαs-coupled receptors that are responsive to these biogenic amines [28] . Putative D1-like and D2-like dopamine receptors have been identified in the genomes of the mosquitoes Ae . aegypti [9] and Anopheles gambiae [10] , but research investigating their pharmacological properties is lacking . These genomic sequences provide a logical starting point to functionally characterize the receptors , which is needed to improve our comprehension of dopaminergic processes in mosquitoes . Moreover , due to their presumed significance in mosquito neurobiology , these dopamine receptors are attractive candidates to explore as new targets for chemical control . We present the results of a “proof-of-concept” study involving a “genome-to-lead” approach for developing new mode-of-action insecticides for arthropod disease vectors ( Figure 1A ) . Our research strategy involves ( i ) exploitation of an arthropod genome sequence for novel target identification , ( ii ) molecular , biochemical and pharmacological target validation , ( iii ) chemical library screening , and ( iv ) confirmation of hits and identification of candidate “leads” using secondary in vitro assays and mosquito in vivo assays . Toward these objectives , two dopamine receptors ( AaDOP1 and AaDOP2 ) were identified in the genome of the yellow-fever mosquito , Ae . aegypti , and characterized using molecular and pharmacological methods . Subsequently , we conducted a chemical library screen in which multiple lead antagonistic chemistries of the AaDOP2 receptor were identified . Finally , we employed a “hit-to-lead” approach ( Figure 1B ) , wherein screen “hits” were confirmed in secondary in vitro assays and two “lead” chemistries were identified using in vivo assays that confirmed their toxicity to mosquito larvae . These results serve as an entry point for expanded chemical library screening of mosquito dopamine receptors and subsequent structure-activity relationship- and further “hit-to-lead”-studies to discover candidate compounds that will enter the registration phase of product development ( Figure 1A ) . Our pipeline will expedite the exploration of GPCRs as potential targets for chemical control in mosquitoes and other important arthropod disease vectors for which sufficient genome sequence data is available .
The gene sequences for the putative dopamine receptors AaegGPRdop1 ( AAEL003920 ) and AaegGPRdop2 ( AAEL005834 ) ( referred to hereafter as Aadop1 and Aadop2 , respectively ) in Ae . aegypti [10] were downloaded from VectorBase ( http://www . vectorbase . org/index . php ) [29] . Sequences of the D1-like dopamine receptors in Drosophila melanogaster were used to identify and compare conserved structural features [30] , [31] . Gene expression analyses for each receptor were conducted using RNA extracted from the eggs , larvae , pupae , and adult male and female mosquitoes from the Liverpool strain of Ae . aegypti [10] . Total RNA was isolated using TRIzol Reagent ( Invitrogen , Carlsbad , CA ) and then treated with RNase-Free DNase ( QIAGEN , Valencia , CA ) . The SuperScript One-Step RT-PCR kit ( Invitrogen , Carlsbad , CA ) was used to amplify receptor mRNA from approximately 150 ng total RNA per reaction using the primers and experimental conditions provided in Table S1 . RT-PCR amplification products were electrophoresed and compared by size to the DNA HyperLadder I ( Bioline USA Inc . , Randolph , MA ) . Products were cut from the gel and isolated with the Qiagen Gel Extraction Kit ( Qiagen Valencia , CA ) . The cloning procedure was performed using the TOPO TA cloning kit ( Invitrogen , Carlsbad , CA ) , according to the manufacturer's instructions . DNA sequencing was conducted at the Purdue University Genomics Core Facility . The resultant DNA sequences were used to predict full-length coding regions that were manually annotated using Artemis software ( version 9 ) [32] . A neighbor-joining sequence analysis was performed using the deduced amino acid sequences representing the mosquito dopamine receptor proteins ( referred to hereafter as AaDOP1 and AaDOP2 ) , additional representative biogenic amine receptors from the insects D . melanogaster and A . mellifera , and the human D1- and D2-like dopamine receptors . ClustalW 1 . 83 [33] was used for sequence alignments prior to tree construction in PAUP 4 . 0b4a [34] . The bootstrap method ( 100 replicates ) was used to provide branch support . Alignments of amino acid sequences for determination of conserved motifs were conducted using Multalin software [35] . Conserved amino acid residues and additional protein features were predicted as described by Meyer et al . [36] . Functional characterization of AaDOP1 and AaDOP2 was conducted by heterologous expression in HEK293 cells ( ATCC , Manassas , VA ) [36] . Expression constructs were produced by synthesis ( GenScript , Piscataway , NJ ) and included the partial Kozak transcriptional recognition sequence “CACC” added directly upstream of the transcription initiation codon for each gene . Constructs were cloned into pUC57 and then subcloned into the expression vector pcDNA3 . 1+ ( Invitrogen , Carlsbad , CA ) by GenScript ( Piscataway , NJ ) . Stable cell lines co-expressing either AaDOP1 or AaDOP2 with a CRELuc reporter construct were developed to permit pharmacological studies in a 384-well format [36] , [37] . Briefly , cells already stably expressing the CRELuc reporter construct were transfected in a 10 cm dish with 15 µl Lipofectamine2000 and 3 µg of pcDNA3 . 1+/Aadop1 or pcDNA3 . 1+/Aadop2 . Clones were maintained as described for the wild-type HEK293 cells [36] with the addition of 2 µg/ml puromycin and 300 µg/ml Geneticin ( Sigma-Aldrich , St . Louis , MO ) . For initial functional analysis , the receptors were transiently expressed in HEK293 cells [36] and analyzed using a competitive binding assay to measure levels of cAMP accumulation [37] . Dose-response curves were generated using cells stably expressing the receptors [36] , [37] . The compounds used for pharmacological characterization included dopamine hydrochloride , histamine dihydrochloride , 5-hydroxytryptamine hydrochloride ( serotonin ) , ( ± ) -octopamine hydrochloride , tyramine hydrochloride ( Sigma-Aldrich , St . Louis , MO ) , ( − ) -epinephrine bitartrate , and L ( − ) -norepinephrine bitartrate ( Research Biochemical International , Natick , MA ) . The synthetic dopamine receptor ligands tested included SKF38393 and SKF81297 ( Tocris , Ellisville , MO ) , SCH23390 ( Tocris , Ellisville , MO ) , and dihydrexidine ( DHX ) ( a gift from D . Nichols , Purdue University ) . Data was collected from a minimum of three independent replicate experiments with each sample measured in triplicate . Statistical analysis of data was conducted with GraphPad Prism 5 software ( GraphPad Software Inc . , San Diego , CA ) . To identify novel AaDOP2 receptor antagonists , the Library of Pharmacologically Active Compounds ( LOPAC1280 ) was screened at the Integrated Screening Technologies Laboratory , Discovery Park , Purdue University , using HEK-CRELuc-Aadop2 cells . These cells were cultured as described above , expanded , and cryo-preserved , to produce a uniform cell population . Briefly , cells ( ∼2 . 5×107 ) were harvested by non-enzymatic dissociation [0 . 5 mM EDTA in Ca2+Mg2+free-phosphate buffered saline ( CMF-PBS ) ] resuspended in cell culture media , and pelleted by centrifugation for 5 min at 100× G . The pellet was resuspended in freezing media ( Opti-MEM supplemented with 10% DMSO and 20% FBS ) to a concentration of 5×106/ml , frozen step-wise , and held in liquid N2 until use . Cells were rapidly thawed , diluted in Opti-MEM , and 20 µl containing 25 , 000 cells were plated per well in 384-well plates ( Nunc , Fisher Scientific , Pittsburgh , PA ) using a BiomekFX liquid handling station ( Beckman-Coulter , Brea , CA ) . The plates were incubated overnight in a humidified incubator at 37°C and 5% CO2 . Prior to screen initiation , a “checkerboard” analysis was conducted that included a minimum ( 300 nM dopamine in combination with 10 µM SCH23390 ) and maximum ( 300 nM dopamine ) stimulatory condition . The data obtained were analyzed to calculate the Z-factor [38] using a modified equation that accounts for the number of replicates ( NIH website: http://assay . nih . gov/assay/index . php/Section2:Plate_Uniformity_and_Signal_Variability_Assessment ) . All compounds were diluted to appropriate concentrations and suspended in assay buffer ( Opti-MEM supplemented with 0 . 02% ascorbic acid ) using a BiomekFX 96-tip head . All LOPAC1280 compounds were screened in quadruplicate at a concentration of 10 µM , including duplicate samples on two separate assay plates in different quadrants to control for plate and automation effects . Each plate contained a dopamine response curve ( 14 nM–30 µM ) and antagonist control wells ( 10 µM SCH23390 in combination with 300 nM dopamine ) . Following compound addition , dopamine was added to each test well at a final concentration of 300 nM , and cells were incubated for 2 hr at 37°C in a humidified incubator . The plates were then equilibrated at 25°C prior to the addition of Steadylite plus luminescence reagent ( PerkinElmer , Shelton , CT ) . Plates were incubated on a shaker at 300 rpm for 5 min , and the luminescence signal was measured using a DTX880 multimode reader ( Beckman Coulter , Brea , CA ) with a 1 sec integration time . Raw screen data were processed as follows: the average background luminescence ( cells in the absence of dopamine or LOPAC1280 compound ) was subtracted from the raw data . Values for the positive receptor activation control ( 300 nM dopamine ) were averaged within each assay plate and used to establish a 100% dopamine receptor stimulation level . Similarly , the average response to SCH23390 was calculated within each assay plate to establish a baseline inhibition for antagonist chemistries . The average percent compound effect was calculated for each LOPAC chemistry in comparison to the SCH23390 antagonist control . The minimum criterion for selection of an antagonist “hit” was established as the percent inhibition equivalent to that determined for SCH23390+3 standard deviations . Single dose-point and dose response in vivo mosquito bioassays were used to assess the toxicity of selected AaDOP2 receptor antagonists identified in the chemical screen . Larvae of Ae . aegypti ( Liverpool strain ) were reared under standard laboratory conditions on a 12 hr day/night cycle at 75% RH and 28°C , and bioassays were conducted at room temperature ( 22–24°C ) . Larvae were transferred from standard rearing trays into six-well tissue culture plates ( Corning , Inc . Corning , NY ) using a small plastic pipette . Ten L4-stage larvae were included per well , each containing five ml of de-ionized water and the assigned drug concentration . Controls were conducted similarly but lacked a drug treatment . Bioassays employed a double-blind experimental design , and percent mortality was scored 24 hr following administration of drugs . Single dose-point assays were conducted using 400 µM drug and included three biological replicates each consisting of 50–100 larvae . Dose-response assays were conducted using five doses ( 400 , 200 , 100 , 50 , and 25 µM ) of the compounds suspended in water , with water alone as a control . Five technical replicates , each including 10 larvae , were performed per dose , and the assay was repeated three times . Statistical analyses included one sample t-tests ( single-point assays ) and determination of the LC50 and LC90 values ( dose-response assays ) conducted with GraphPad Prism 5 software ( GraphPad Software Inc . , San Diego , CA ) .
mRNA transcripts for Aadop1 and Aadop2 were detected by RT-PCR in eggs , larvae , pupae , and adult male and female Ae . aegypti ( Figure S1 ) . DNA sequencing of RT-PCR products confirmed the splice junctions at each intron/exon boundary for both receptor genes . Using a combination of evidence from our RT-PCR data , the genome sequence , and related sequences in D . melanogaster , we predicted the gene structure and complete coding regions of Aadop1 ( Genbank accession: JN043502 ) and Aadop2 ( Genbank accession: JN043503 ) ( Figure S2 ) . A neighbor-joining sequence analysis was conducted to assess the relationships of AaDOP1 and AaDOP2 with other representative biogenic amine receptors ( Figure 2 ) . AaDOP1 was included in a clade ( bootstrap = 100 ) containing the presumably orthologous D1-like dopamine receptors D-Dop1 of D . melanogaster [30] , [40] , DOP1 of A . mellifera [41] , and Isdop1 of I . scapularis [36] , [42] . AaDOP2 clustered with two presumably orthologous insect D1-like dopamine receptors ( INDRs ) [43] , DopR99B ( DAMB ) of D . melanogaster [31] , [44] and DOP2 of A . mellifera [41] , as well as Isdop2 of I . scapularis [36] . The INDR-like and Isdop2 sequences were also joined together in a larger clade ( bootstrap = 76 ) containing the octopamine receptors OAMB of D . melanogaster [45] and OCT1 [46] of A . mellifera , consistent with Mustard et al . [41] . The human D1-like dopamine receptors formed a separate clade ( bootstrap = 100 ) distinct from the arthropod dopamine receptors . The deduced amino acid sequences of AaDOP1 and AaDOP2 were analyzed to identify conserved structural features typically associated with biogenic amine-binding GPCRs ( Table S2 ) , as well as unique regions that could be potentially exploited for development of mosquito-specific chemistries . Conserved features included sites predicted for ligand binding , protein stability , and G protein-coupling , and residues with potential for post-translational modification were identified . Alignments of the full-length AaDOP1 and AaDOP2 amino acid sequences ( Figure 3 ) indicated that these sequences were divergent in the presumed N- and C-termini and the intracellular and extracellular loops , and the TM domains were moderately conserved ( 47% amino acid identity ) . A substantial difference was observed in the composition and relative size of the third intracellular loop that was much larger in AaDOP2 ( 115 amino acids ) than in AaDOP1 ( 62 amino acids ) . Only a modest level of similarity was observed between the mosquito and human D1-like dopamine receptors , which shared between 47–54% amino acid identities among the TM domains , which typically represent the most conserved regions of GPCRs ( Table S3 ) . Moreover , comparison of the predicted TM domains from multiple invertebrate and vertebrate D1-like dopamine receptors showed that only 34% ( 58/172 ) of the amino acids were shared among all species included in the alignment ( Figure S3 ) . The highest level of sequence similarity to the TM domains of AaDOP1 and AaDOP2 was found in their predicted D . melanogaster orthologs , D-Dop1 ( 88% identity ) ( Table S3 ) and DopR99B ( 97% identity ) , respectively . To study the function of the putative dopamine receptors AaDOP1 and AaDOP2 , each receptor was expressed in HEK293 cells . Production of the mosquito receptor transcripts in transiently-transfected cells was first verified using RT-PCR ( Figure S4 ) . Increases of intracellular cAMP were detected in cells transiently expressing either AaDOP1 [2 . 7±0 . 6 fold ( n = 3 ) ] or AaDOP2 [48±14 fold ( n = 3 ) ] in response to a single dose of dopamine ( 10 µM ) ( Figure S5 ) . No significant increase in cAMP was observed in the mock transfected cells ( empty pcDNA3 . 1+ vector ) . For cells transiently expressing AaDOP1 , relatively high levels of constitutive activity were observed ( 17 . 6±2 . 4 fold greater than in mock transfected cells ) as compared to AaDOP2 ( 1 . 83±0 . 93 fold greater than in mock transfected cells ) . Subsequently , dose-response curves for seven different biogenic amines were generated using HEK-CRELuc cells stably expressing either AaDOP1 or AaDOP2 ( Figure 4; Table 1 ) . Again , dopamine stimulated both receptors , with EC50 values determined at 3 . 1±1 . 1 nM and 240±16 . 0 nM for AaDOP1 and AaDOP2 , respectively ( Figure 4A–B; Table 1 ) . In addition , we observed activation of the AaDOP1 receptor by epinephrine ( EC50 = 5 . 8±1 . 5 nM ) and norepinephrine ( EC50 = 760±180 nM ) ( Table 1 ) . Conversely , no significant stimulation was observed for the AaDOP2 receptor by epinephrine or norepinephrine ( Table 1 ) . Neither receptor was stimulated by histamine , octopamine , serotonin , or tyramine ( EC50≥10 uM ) . The effects of known synthetic dopamine receptor agonists were also investigated ( Figure 4C–D; Table 1 ) . Considerable stimulation was observed for AaDOP1 with the agonists listed in their rank order of potency: DHX>SKF81297>SKF38393 . In contrast , of the synthetic agonists tested here , only treatment with DHX resulted in significant dose-dependent activation of AaDOP2 . The addition of the D1 dopamine receptor antagonist SCH23390 ( 10 µM ) robustly inhibited the dopamine-mediated stimulation of both AaDOP1 and AaDOP2 ( Figure 4E ) . We selected the AaDOP2 receptor for an antagonist screen of the LOPAC1280 library because of its low constitutive activity and strong dopamine response compared to background ( approximately 10-fold ) ( Figure 4B , D ) . Using dose-response studies , it was determined that 300 nM dopamine alone and in combination with 10 µM SCH23390 created a suitable “signal window” for identification of AaDOP2 antagonists ( Figure 4F ) . A “checkerboard analysis” using these conditions and assuming four replicates in the screen generated a Z-factor of 0 . 5±0 . 1 ( n = 3 ) , indicating that the assay was suitable for antagonist screening . The criterion for “hit” detection was established relative to the control antagonist ( SCH23390 response +3 standard deviations ) , such that only those compounds that inhibited the dopamine response by at least 81% were considered ( Table 2 ) . Based on this , our screen identified 51 potential antagonists of the AaDOP2 receptor ( complete screen results provided in Table S4 ) . These compounds were partitioned into seven different classes based on their known biochemical interactions with mammalian molecular targets that included dopamine receptor antagonists ( 20 ) , serotonin ( 6 ) , histamine ( 2 ) , and acetylcholine receptor ligands ( 1 ) , biogenic amine uptake inhibitors ( 9 ) , protein kinase modulators ( 6 ) , and miscellaneous chemistries such as cell cycle regulators and apoptosis inhibitors ( 7 ) . Ten “hit” compounds ( amitriptyline hydrochloride , ( ± ) -butaclamol hydrochloride , clozapine , doxepin hydrochloride , cis- ( Z ) -flupenthixol dihydrochloride , methiothepin maleate , mianserin hydrochloride , niclosamide , piceatannol , and resveratrol ) , in addition to SCH23390 were selected for screen validation assays . These compounds were tested for their activity in cAMP accumulation assays to control for potential “off-target” effects ( i . e . chemistries that affect the CRELuc reporter system ) . Seven of these compounds were potent antagonists of the AaDOP2 receptor , as shown by the dose-dependent reduction of cAMP accumulation relative to the dopamine-stimulated control ( Table 3 , Figure 5 ) . Three of the compounds ( i . e . niclosamide , piceatannol , and resveratrol ) showed no significant antagonistic effects against AaDOP2 in the cAMP accumulation experiments , having IC50 values ≥10 µM . The toxicity of the AaDOP2 antagonist screen hits amitriptyline and doxepin was assessed in Ae . aegypti larval bioassays . These chemistries were selected due to their relatively higher potency at AaDOP2 compared to hD1 ( Table 3 ) . Single dose-point assays at 400 µM effective concentration of drug revealed that amitriptyline ( 93% average mortality ) and doxepin ( 72% average mortality ) each caused significant mortality ( p<0 . 05 ) 24 hours post-treatment relative to the water control ( 0% mortality ) ( Figure 6A ) , whereas no mortality was observed for SCH23390 during this timeframe ( data not shown ) . In addition , dose-response experiments were conducted for amitriptyline , which caused a rapid and high mortality effect in the single-point assays . The toxicity of amitriptyline was dose-dependent , and the LC50 and LC90 values were determined at 78 µM and 185 µM , respectively ( Figure 6B ) .
This work provides the first detailed investigation into the molecular and pharmacological properties of D1-like dopamine receptors , AaDOP1 and AaDOP2 , from the mosquito vector of dengue and yellow fever , Ae . aegypti , and the development of a cell-based screen assay to discover antagonists of AaDOP2 . Our study employed a novel pipeline utilizing a “genome-to-lead” approach for the discovery of new chemistries for vector control . This research establishes a basis for improving understanding of mosquito dopaminergic processes in vivo and for chemical screening of these and other receptors characterized in arthropod vectors of human disease , such as in the Lyme disease tick , I . scapularis [36] , [42] . To our knowledge , Lee and Pietrantonio [47] have published the only other study involving the functional characterization of a biogenic amine-binding GPCR in mosquitoes that was focused on a Gαs-coupled serotonin receptor in Ae . aegypti . Furthermore , ligands of only four other cloned GPCRs have been pharmacologically verified in mosquitoes , including those that target an adipokinetic hormone receptor , a corazonin receptor , a crustacean cardioactive peptide receptor [48] , and an adipokinetic/corazonin-related peptide receptor in the malaria mosquito , A . gambiae [49] . Typically , insects possess three different dopamine receptors including two D1-like receptors and a single D2-like receptor [43] . Here , RT-PCR data were used to validate the two mosquito D1-like dopamine receptor gene models [10]; this enabled confirmation of intron/exon boundaries and prediction of the complete protein coding regions needed prior to heterologous expression studies . A putative D2-like dopamine receptor gene ( AaDOP3 ) was also identified in Ae . aegypti [10] although this receptor has not yet been functionally characterized . The RT-PCR studies also demonstrated that transcripts for both D1-like dopamine receptor genes were detectable in each developmental stage of Ae . aegypti , suggesting the importance of these receptors throughout the mosquito life cycle . Much progress has been made in determining the life-stage and tissue-specific expression dynamics of the orthologous dopamine receptors in D . melanogaster [14] , [30] , [31] , [40] , [44] , [50] , A . mellifera [41] , [43] , , and most recently in the Lyme disease tick , I . scapularis [42] . Our research will support future complementary studies needed to localize expression of these dopamine receptors in mosquito tissues to gain further insight toward their neurophysiological roles . The AaDOP1 and AaDOP2 amino acid sequences were compared and analyzed to identify conserved as well as unique features of the receptors . Several characteristics typically associated with biogenic amine-binding GPCRs were evident , including aspartate residues in TM II and TM III that are thought to interact with the amine moieties of catecholamines [55] . The conserved serine residues in TM V and aromatic residues in TM V and VI are also potentially important for ligand interaction [56] , [57] . In both receptors , the conceptual cytoplasmic region of TM III contained the conserved “DRY” motif associated with G protein-coupling [58] , [59] , and a pair of cysteine residues were located in the extracellular loops I and II that may form a disulfide bond for protein stabilization [58] , [60] , [61] . Interestingly , the divergent intracellular loop III was predicted to be almost twice as long in AaDOP2 ( 115 amino acids ) than in AaDOP1 ( 62 amino acids ) , but the sizes of the carboxyl tail region were similar between these receptors . This corresponded well with the relative sizes of these features in the fruit fly and honeybee orthologs [43]; however , the significance of these characteristics is yet to be determined in the mosquito . Importantly , the AaDOP1 and AaDOP2 sequences were markedly different from the human D1-like dopamine receptor sequences . Although a modest level of amino acid identity ( ∼50% ) was observed between the TM domains , the N- and C-termini and extracellular and intracellular loop regions were highly divergent ( data not shown ) . These differences suggest that there exists potential for identifying chemistries that are mosquito-specific and , importantly , do not interfere with dopaminergic functioning in humans . Heterologous expression experiments conducted in HEK293 cells provided experimental evidence that the Ae . aegypti receptors are functional D1-like dopamine receptors . We measured significant increases in cAMP accumulation following dopamine treatment of cells transiently expressing either AaDOP1 or AaDOP2 , suggesting that both receptors couple to Gαs proteins . This effect was further substantiated in cell lines stably co-expressing either of these receptors and the CRELuc reporter system , as measured by an increase in luciferase activity following dopamine treatment . Future research is needed to determine if these receptors operate through multiple cellular signaling mechanisms , such as was shown for the D . melanogaster dopamine receptor involved with both cAMP and calcium signaling [62] . The stably transformed cell lines were used to compare the pharmacological properties of AaDOP1 and AaDOP2 in response to seven different biogenic amines . For dopamine , we measured EC50 values in the nanomolar range for both AaDOP1 ( 3 . 1±1 . 1 nM ) and AaDOP2 ( 240±16 nM ) . However , there were differences in the responses of these receptors to the other biogenic amines . AaDOP2 was activated only with dopamine , whereas AaDOP1 was stimulated by dopamine , epinephrine , and to a lesser extent , norepinephrine . These results were similar to those reported for the orthologous dopamine receptors in the tick I . scapularis [36] , [42] . Another difference between AaDOP1 and AaDOP2 was observed regarding constitutive activity . In both transient and stable expression experiments , the AaDOP1 receptor exhibited significant constitutive activity , as determined by the elevated levels of cAMP detected in the absence of a receptor agonist , whereas AaDOP2 did not . Such constitutive activity was also reported for the D1-like dopamine receptors AmDOP1 of A . mellifera [41] , CeDOP1 from the nematode Caenorhabditis elegans [63] , Isdop1 of I . scapularis [36] , and the human D5 receptor [64] . Seifert and Wenzel-Seifert [65] proposed that constitutive activity of a GPCR may enable the maintenance of basal neuronal activity , although evidence is needed to support such activity for AaDOP1 in vivo . The pharmacological properties of AaDOP1 and AaDOP2 were further explored by testing their responses to synthetic dopamine receptor agonists and antagonists . Both receptors were strongly stimulated by the agonist DHX; however , only AaDOP1 significantly responded to the well characterized D1 agonists SKF81297 and SKF38393 . This differential response to the SKF compounds was also observed for the orthologous D1-like dopamine receptors in the tick I . scapularis [36] . Interestingly , neither of the D . melanogaster D1-like dopamine receptors was strongly stimulated by SKF38393 [31] , [40] . Both AaDOP1 and AaDOP2 were inhibited by the antagonist SCH23390 , as were the tick D1-like receptors [36] . This contrasted with the lack of significant inhibition reported by SCH23390 for D-dop1 in the fruit fly [40] and DOP1 of the honeybee [51] . Given the limited number of drugs that have been tested against these receptors , to date , these differential pharmacological responses provide further evidence that it may be possible to discover chemistries that operate specifically at the mosquito dopamine receptors . Our over-arching goal was to develop a pipeline to identify lead chemistries active at biogenic-amine binding GPCRs in vector arthropods . Broadly speaking , we define a lead chemistry as any molecule , or its analog or derivative , with potential for insecticide development . In our study , this refers to any molecule identified by screening and subsequently confirmed in a variety of “hit-to-lead” assays . The LOPAC1280 library was chosen for our pilot screen because it is enriched with chemistries that influence dopaminergic processes and includes other GPCR-binding ligands . We hypothesized that chemistries that antagonize these dopamine receptors may possess insecticidal properties . Precedent for this concept stems from pest management successes associated with the use of phenylpyrazoles ( e . g . Fipronil ) and cyclodienes , which block GABA-gated chloride channels and have highly insecticidal properties [66] , [67] . This notion was pursued using HEK293 cells stably expressing AaDOP2 because this receptor has a robust response to dopamine and a low constitutive activity , which are properties that aid interpretation of screen data . Our initial screen was directed at the identification of AaDOP2 antagonists; the success of this experiment justifies expanded screening to explore the antagonist chemical “space” , and with assay modification , screens to detect agonists active at this receptor . Moreover , development of the AaDOP1 assay would enable comparative screens against LOPAC1280 chemistries . Of the 51 hit AaDOP2 antagonists identified in the LOPAC1280 library , 20 ( 39% ) are known antagonists of mammalian dopamine receptors . A majority of these chemistries fall into the benzodiazepine , phenothiazine , or thioxanthene classes that in other systems are known to bind other biogenic amine receptors . Included were ligands selective for D1- and D2-like dopamine receptors in mammalian systems , as well as several non-dopamine receptor selective compounds such as ( ± ) -butaclamol , cis- ( Z ) -flupenthixol , and the atypical antipsychotic , clozapine . These three compounds were tested in a dose-response format for their ability to inhibit dopamine-stimulated cAMP accumulation . The IC50 values demonstrated the following rank order of potency clozapine>cis-flupenthixol>butaclamol . The next largest grouping of identified compounds includes inhibitors of the biogenic amine transporters ( 9 compounds , 18% ) . Several serotonin receptor antagonists ( 6 compounds , 12% ) were identified as well . Follow-up dose response studies with selected chemistries from the identified transport inhibitors and serotonin antagonists ( i . e . methiothepin , mianserin , amitriptyiline , and doxepin ) revealed that these compounds were potent antagonists at the AaDOP2 receptor and were much more potent than the prototypical D1 antagonist , SCH23390 ( Table 3 ) . The antagonistic activity of these ligands is not completely surprising; the National Institute of Mental Health's Psychoactive Drug Screening Program ( NIMH-PDSP ) database reports Ki values for the human D1-like dopamine receptors at 80–900 nM ( http://pdsp . med . unc . edu/ ) . However , these observations , combined with the dopamine antagonist screen results , indicate that well studied and clinically used compounds could be used to target invertebrate GPCRs . In fact , a number of the chemistries identified in our screen have been used in humans for decades , suggesting the possibility of “drug repurposing” as insecticides . Further precedent for the concept of insect-specific chemistries can be drawn from the fact that a number of insecticides ( e . g . , pyrethroids and fipronil ) are considerably more selective at invertebrate as opposed to mammalian targets [68] . The screen also identified multiple protein kinase modulators and several agents that regulate germane cellular functions that presumably inhibit the CRE response via non-AaDOP2 mechanisms . Support for this hypothesis was demonstrated in the direct measurement of cAMP accumulation experiments , where resveratrol , pieacetannol , and niclosamide each lacked activity . The remaining three “hit” compound classes included antagonists of either histamine or muscarinic acetylcholine receptors , and this likely reflects the lack of receptor selectivity for these ligands . The LOPAC1280 library includes several known antagonists of mammalian dopamine receptors that did not qualify as hits in our screen . In part , this can be explained by the fact that we used a highly stringent cut-off to signify antagonistic activity at AaDOP2 . Had we reduced the stringency to select for hits with an antagonistic effect equivalent to that of SCH23390+6 standard deviations ( 69% inhibition ) , our screen would have returned an additional 13 hit chemistries , including compounds predicted to have a modest antagonistic effect at AaDOP2 and those that are more selective for D2-like dopamine receptors . Considering the substantial divergence between the mosquito and human D1-like dopamine receptor sequences , there is a strong possibility that a subset of the “non-hit” dopamine receptor antagonists are not active at the mosquito receptor . In support of this , the prototypical mammalian D1 antagonist , SCH23390 , was greater than 3000-fold more selective for hD1 than AaDOP2 . Although our comparison data set is limited to only eight compounds , these experiments suggest a very divergent pharmacology between these human and mosquito dopamine receptors . Thus , our study provides a foundation for subsequent comparative pharmacological analyses of the mosquito and human dopamine receptors . Analyses involving a small subset of compounds revealed a correlation between our in vitro and in vivo data . The AaDOP2 antagonist screen hits , amitriptyline and doxepin , caused significant lethality in the mosquito bioassay . Our finding that these drugs each have a relatively higher potency at the mosquito dopamine receptor than at the human dopamine receptor ( hD1 ) has implications for the identification of arthropod-selective chemistries . Drugs with minimal or no impact on the neurological functioning of humans or other vertebrate species are particularly desirable as prospects for insecticide development . Conversely , SCH23390 , which is active at AaDOP2 only in the micromolar range and was several fold more selective for hD1 in cAMP assays , did not cause significant mortality at 24 hr . The success of this initial chemical library screen in identifying new mosquitocidal chemical leads justifies the pursuit of an expanded high-throughput screening effort involving thousands or hundreds of thousands of chemistries against mosquito dopamine receptors . Our platform is also amenable for the screening of agonist chemistries active at these mosquito dopamine receptors , as well as for Gαs-coupled biogenic amine targets of other vector arthropods , and also could be modified to screen Gαi/o-coupled receptors [69] . Importantly , the identification of lead AaDOP2 receptor antagonistic chemistries provides a basis for investigating the effect of these or related compounds on mosquito dopaminergic processes in vivo [70] . Follow-up research is needed to determine the precise mechanism ( s ) of amitriptyline- and doxepin-induced mortality in Ae . aegypti . Further work is also needed to determine if these chemistries and associated derivatives or analogs identified by chemical screens possess the properties desired of an insecticide ( e . g . bioavailability , in vivo potency/toxicity , suitable half-life , lack of effects on non-target organisms , suitability for synthesis and formulation ) . Molecular modeling of three dimensional GPCR structures and their binding capabilities , as reported for an adipokinetic hormone receptor in A . gambiae [71] and a tyramine receptor in the moth Plodia interpunctella [72] , may facilitate in silico chemical screening [73] and ligand-receptor studies that permit the design or refinement of lead molecules active at mosquito GPCRs . Historically , multiple neuroactive processes in arthropods have been exploited for pest control using insecticides such as chlorinated hydrocarbons , organophosphates , methylcarbamates , pyrethroids , amidines , and phenylpyrazoles [67] . Resistance involving each of these classes ( the vast majority of which operate by affecting ion channels and neurotransmitters ) has been documented . The development of new mode-of-action insecticides could improve our arsenal against mosquito populations that have developed resistance to existing chemical formulations [1] . We suggest that the two dopamine receptors characterized here , as well as other biogenic amine-binding GPCRs [74] , [75] , represent promising targets for new insecticide research , due to their presumably central roles in insect neurobiology . This “proof-of-concept” study sets the stage for target-specific approaches for vector control . Such efforts , in parallel with activities of organizations such as the Innovative Vector Control Consortium , may help to realize the goal of delivering new insecticides for reduction of vector-borne diseases [2] . | Mosquitoes and other arthropods transmit important disease-causing agents affecting human health worldwide . There is an urgent need to discover new chemistries to control these pests in order to reduce or eliminate arthropod-borne diseases . We describe an approach to identify and evaluate potential insecticide targets using publicly available genome ( DNA ) sequence information for arthropod disease vectors . We demonstrate the utility of this approach by first determining the molecular and pharmacological properties of two different dopamine ( neurotransmitter ) receptors of the yellow fever- and dengue-transmitting mosquito , Aedes aegypti . Next , we tested 1 , 280 different chemistries for their ability to interact with one of these dopamine receptors in a chemical screen , and 51 “hit” compounds were identified . Finally , we show that two of these chemistries , amitriptyline and doxepin , are selective for the mosquito over the human dopamine receptor and that both chemistries caused significant mortality in mosquito larvae 24 hours after exposure , identifying them as possible “leads” for insecticide development . Our methodology is adaptable for chemical screening of related targets in mosquitoes and other arthropod vectors of disease . This research demonstrates the potential of target-specific approaches that could complement traditional phenotypic screening , and ultimately may accelerate discovery of new mode-of-action insecticides for vector control . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"pest",
"control",
"drugs",
"and",
"devices",
"global",
"health",
"chemistry",
"biology",
"genomics",
"neuroscience",
"genetics",
"and",
"genomics",
"agriculture"
] | 2012 | A “Genome-to-Lead” Approach for Insecticide Discovery: Pharmacological Characterization and Screening of Aedes aegypti D1-like Dopamine Receptors |
The mechanisms of liver injury associated with chronic HCV infection , as well as the individual roles of both viral and host factors , are not clearly defined . However , it is becoming increasingly clear that direct cytopathic effects , in addition to immune-mediated processes , play an important role in liver injury . Gene expression profiling during multiple time-points of acute HCV infection of cultured Huh-7 . 5 cells was performed to gain insight into the cellular mechanism of HCV-associated cytopathic effect . Maximal induction of cell-death–related genes and appearance of activated caspase-3 in HCV-infected cells coincided with peak viral replication , suggesting a link between viral load and apoptosis . Gene ontology analysis revealed that many of the cell-death genes function to induce apoptosis in response to cell cycle arrest . Labeling of dividing cells in culture followed by flow cytometry also demonstrated the presence of significantly fewer cells in S-phase in HCV-infected relative to mock cultures , suggesting HCV infection is associated with delayed cell cycle progression . Regulation of numerous genes involved in anti-oxidative stress response and TGF-β1 signaling suggest these as possible causes of delayed cell cycle progression . Significantly , a subset of cell-death genes regulated during in vitro HCV infection was similarly regulated specifically in liver tissue from a cohort of HCV-infected liver transplant patients with rapidly progressive fibrosis . Collectively , these data suggest that HCV mediates direct cytopathic effects through deregulation of the cell cycle and that this process may contribute to liver disease progression . This in vitro system could be utilized to further define the cellular mechanism of this perturbation .
Hepatitis C virus ( HCV ) , a member of the Flaviviridae family , is a blood-borne pathogen which currently infects approximately 170 million people worldwide . Exposure to HCV typically results in a persistent infection and approximately 30% of chronically infected patients will develop progressive liver disease including fibrosis , cirrhosis and hepatocellular carcinoma ( HCC ) [1] . The majority of pathology associated with chronic infection is believed to occur via a HCV-specific cell-mediated immune response [2] . However , in light of this , it is somewhat perplexing that liver disease progression is accelerated in immuno-compromised individuals . Specifically , HCV/HIV-coinfected patients and liver transplant patients receiving immuno-suppressive drugs tend to develop fibrosis/cirrhosis at a much faster rate than immuno-competent individuals [3] . A recent study found that HCV-specific CD8 T cells were actually associated with areas of low hepatocellular apoptosis and weak fibrosis . It is thought that these cells are protective of liver damage through production of IL-10 [4] . Furthermore , characterization of the host response to HCV infection in the SCID-Alb/uPA mouse model demonstrated histological evidence of hepatocyte apoptosis in a manner similar to that observed during acute HCV infection in patients [5] . HCV infection in this model was associated with perturbations in cellular pathways , including lipid metabolism and oxidative stress , which have the potential to be cytopathic . The inability of these animals to generate a virus-specific immune response raises the intriguing possibility that HCV replication is capable of directly mediating hepatocyte apoptosis . It is now thought that both direct cytopathic effects and immune-mediated processes likely play a role in HCV-associated liver injury [3] . The cellular mechanisms by which HCV replication , and subsequent virus-host interactions , may mediate liver injury are unclear . Progress in this area has been hindered by the lack of appropriate model systems in which to investigate the role of viral factors in liver disease progression . Currently , studies focused on defining the mechanisms of HCV-associated liver injury are primarily restricted to limited analysis of patient samples , including liver biopsy tissue . While such studies have provided significant insight into the role of steatosis , oxidative stress and death-receptor signaling in liver disease , there are obvious limitations with respect to conducting more mechanistic studies , in particular the role of viral proteins . There is a wealth of literature describing experiments in which one more HCV proteins are over expressed in cultured hepatocytes . The results indicate a wide range of , and often conflicting , effects of viral protein expression on cellular functions , including apoptosis ( Reviewed in Fischer et al , 2007 ) . Core protein , in particular , has been reported to have both pro- and anti-apoptotic effects on death-ligand mediated hepatocyte apoptosis , including TNF-α , CD95Ligand and TRAIL-induced apoptosis , via a variety of mechanisms . The HCV envelope protein E2 has been found to both inhibit TRAIL-induced apoptosis and also to induce mitochondria-related/caspase-dependent apoptosis in the same hepatoma cell line . Perturbations of apoptotic pathways have also been demonstrated with the non-structural proteins . The NS3 protease inhibits pro-apoptotic RIG-I signaling via cleavage of the adaptor protein Cardif and also induces apoptosis of hepatocytes via caspase-8 . NS5A has been found to inhibit apoptosis through multiple mechanisms , including sequestering of p53 , activation of NFkB , increased expression of bcl-XL and p21 as well as activation of the P13-kinase-AKt/PKB survival pathway . While intriguing , many of these studies involve the expression of a single HCV protein , often at very high levels which do not accurately represent those seen in naturally infected livers . These experiments also fail to study the impact of potentially crucial interactions between the different HCV proteins . A significant breakthrough in HCV research was achieved by the discovery of a specific HCV strain that efficiently infects and replicates in the cultured hepatoma cell line Huh-7 . 5 [6] , [7] , [8] , [9] . This strain , termed JFH-1 , was isolated from a Japanese patient who suffered fulminant hepatitis following exposure to the virus [6] , [7] . Subsequent inoculation of clonal JFH-1 into chimpanzees and SCID-Alb/uPA mice resulted in productive infections in the absence of fulminant hepatitis , suggesting that the host response to infection played a key role in the severe form of hepatitis observed in this patient [8] . This model system provides the opportunity to study the impact of viral protein expression and replication on host cell function during a productive HCV infection and to potentially investigate the role of viral factors in liver injury . In the current study , microarray experiments were performed to characterize the host transcriptional response to HCV infection in an attempt to gain insight into the mechanism of HCV-associated cell death . Both the presence of activated caspase-3 and induction of cell death-related genes indicated that HCV infection was associated with a direct cytopathic effect . Gene ontology analysis suggests a role of cell cycle perturbation , possibly in response to oxidative stress and/or TGF-β1 signaling , in HCV-mediated apoptosis .
To study the host response to infection during the early phase of acute infection , Huh-7 . 5 cells were infected at a relatively high MOI ( 1–2 virions/cell ) with HCV genotype 2a chimeric virus , J6/JFH ( HCVcc ) [7] . The virus used to infect cells was a pool of cell culture adapted virions generated by multiple passages over naïve Huh 7 . 5 cells ( see Materials and Methods ) . For infection controls , cells were inoculated with either UV-inactivated HCVcc ( to distinguish effects due to virus binding and virus replication ) or conditioned media ( mock ) . Conditioned media was used for the mock as it has been shown that factors present in the media of cultured cells can induce transcriptional changes ( Walters , unpublished data ) . Cells were incubated with HCVcc for approximately 8 hrs , after which the cells were washed and fresh media added . Following infection , HCV ( + ) cells were visualized using an anti-NS5A antibody . As shown in Figure 1A , the majority of the cells expressed viral antigen by 48 hrs post-infection and continue to do so for the remainder of the study . No HCV RNA or viral protein expression was detected in cells exposed to UV-inactivated HCVcc ( data not shown ) . Samples were harvested at 24 , 48 , 72 , 96 , and 120 hours post-infection and cellular RNA isolated for measuring intracellular HCV RNA levels and for global gene expression profiling . Similar to what has been reported previously , a cytopathic effect was observed in the cultures of cells infected with HCVcc starting around 72 hrs post-infection ( Figure 1B ) . This effect was not observed in the cells exposed to either UV-inactivated HCVcc or conditioned media , indicating that it is induced by active viral replication . The cytopathic effect became more prominent at 96 hrs and appeared to include the majority of the cells by 120 hrs post-infection ( Figure 1B ) . Immuno-histochemistry specific for cleaved caspase-3 demonstrated activation of a terminal pathway involved in apoptosis that appears to cause the cytopathic effect in culture . As shown in Figure 1C , cleaved caspase-3 was present in cells infected with HCVcc beginning around 72 hrs , suggesting that the mechanism of cell death was apoptosis . It was not observed in cells exposed to either conditioned media ( data not shown ) or UV-inactivated virus . The initial presence of activated caspase-3 also coincided with peak levels of intracellular viral RNA ( Figure 2B ) , suggesting a causative link between the level of HCV replication and cell death . Microarray experiments were performed to characterize the host transcriptional response to HCV infection in an attempt to gain insight into the mechanism of HCV-associated cell death . For these experiments , mRNA samples isolated from cells exposed to either HCVcc or UV-inactivated HCVcc were compared to mRNA isolated from time-matched mock-treated cells . Figure 2A shows the global gene expression profiles of cells infected with UV-inactivated HCVcc and HCVcc at 24 , 48 , 72 , 96 , and 120 hrs post-infection . Similar to what was observed in previous genomic studies using the chimpanzee and SCID-Alb-uPA mouse models , the overall effect of HCV infection on cellular gene expression was subtle in the early phases of infection , with less than 50 differentially regulated genes at 24 hrs post-infection . Overall , 860 genes showed a 2-fold or higher change in expression ( P value≤0 . 05 ) in at least one experiment ( Figure 2A ) . The primary sequence name and fold-change of these genes are shown in Table S1 . In contrast , cells exposed to the UV-inactivated virus showed very little , if any , regulation of genes ( 2-fold change , P value≤0 . 05 ) throughout the time-course , indicating that the process of virus attachment and entry into cells does not significantly impact host cell gene expression . A similar lack of differential regulation was observed in global transcriptional profiling of cells containing the HCV full-length replicon ( data not shown ) . As shown in Figure 2B , there was a clear association between intracellular HCV RNA levels and number of differentially expressed genes , with the maximum regulation of cellular genes coinciding with peak intracellular HCV RNA levels ( 72 hrs post-infection ) . The reason for the decreasing HCV RNA levels following 72 hrs is unclear but may be related to a decrease in the number of cells capable of producing high levels of virus . It is interesting to note that the majority of transcriptional changes involve increased expression of host genes . Few differentially expressed genes showed decreased expression in the HCV-infected relative to the mock-infected cultures ( Figure 2A ) , although the significance of this finding is unclear . Gene ontology analysis was used to identify the cellular processes represented by the changes in steady-state abundance of transcripts associated with HCV infection . Notably , many of the differentially expressed genes belong to functional categories of cell death , cell cycle and cell growth/proliferation . Indeed , cell death genes comprised approximately half of all annotated differentially regulated genes at each time-point , with the exception of 24 hrs post-infection which showed negligible regulation of cellular genes . Figure S1 demonstrates the expression profiles of 118 genes associated with cell death in HCV-infected cells . Differential expression occurred beginning at 48 hrs post-infection at which time there was no significant visual evidence of cytopathic effect or apoptosis . Maximum differential expression of cell death genes occurred at 72 hrs post-infection whereas the level of apoptosis , as measured by caspase-3 cleavage , continued to increase until 120 hrs post-infection . Both the differential expression of cell death-related genes and detection of cleaved caspase-3 in HCV-infected cells indicated that the observed cytopathic effect is apoptosis . Ingenuity Pathway Analysis identified a large number of cell death-related genes that function in cell cycle checkpoint/arrest , suggesting a potential role of cell cycle perturbation in apoptosis of infected cells ( Figure 3 ) . Consistent with this , a significant number of genes were identified that are associated with the DNA damage/oxidative stress response , many of which belong to the NRF2-mediated oxidative stress response pathway . This suggests that HCV replication is associated with generation of reactive oxygen species ( ROS ) . Interestingly , the expression of two members of this pathway ( CAT and EPHX1 ) was decreased , suggesting the ability of the cells to deal with excess ROS may be impaired . Apoptosis associated with cell cycle arrest is thought to be mediated through the mitochondria and p53 pathway [10] . In support of this , there was regulation of genes that have been linked to cytochrome c release from mitochondria ( BBC3 , BIK , BMF , PMAIP1 , GSN , HRK ) . Many of these genes , along with others ( DAPK3 , CASP4 , RIPK2 ) , are also known to specifically regulate caspase activation . Interestingly , Ingenuity Pathway Analysis revealed that p53 signaling was significantly effected during HCV infection and this could provide the important link between oxidative stress/DNA damage , cell cycle arrest and apoptosis . Indeed , many of the differentially expressed genes associated with HCV infection that are involved in cell cycle arrest ( TP53INP1 , GADD45A , GADD45B , KLF6 , UHRF1 ) and DNA damage/oxidative stress response ( PMAIP1 , ATF3 , BBC3 , FOXO3A , NOXA , CAT , UHRF1 ) regulate apoptosis via interaction with p53 . The functional categories cell cycle and cell growth/proliferation were also significantly enriched among genes showing differential expression during HCV infection . However , the majority of the differentially expressed genes associated with cell cycle regulation were involved with cell cycle checkpoint/arrest and subsequent induction of apoptosis , rather than actual progression through the cell cycle ( Figure 4A ) . This likely explains the significant overlap between cell cycle genes and those associated with cell death as described above . Many of the genes associated with checkpoint/arrest involved the G1/S phase transition , suggesting that this checkpoint is the main area of cell cycle regulation by HCV replication . Similar to what was observed with apoptosis-associated genes , many cell cycle genes function to induce cell arrest in response to DNA damage and cellular stress . Genes previously identified as transcriptional targets of p53-induced growth arrest and apoptosis ( BBC3 , PMAIP1 , AURKB , MKi67 , RRM2 , MCM4 , and MCM6 ) were also differentially regulated in HCV-infected cells . Again this suggests that perturbations in the p53 signaling pathway play a key role in perturbation of cell cycle and induction of apoptosis during HCV infection [11] , [12] . Increased expression of genes encoding proteins which function to either decrease p53 levels or serve as a protective effect against p53-dependent apoptosis ( TYMS , JUND , and UBD ) suggests the cell is attempting to counteract the activation of the p53 signaling pathway . Alternatively , the cell may be trying to undergo apoptosis and the virus is trying to counteract the process . A much smaller set of genes associated with mitosis and cell cycle progression ( positive regulators of cell proliferation ) were also differentially regulated . Interestingly , the expression of MK167 , Mcm4 and Mcm6 , common markers of cell proliferation , are decreased during HCV infection . This is consistent with the observation that proliferation of HCV-infected Huh-7 . 5 cells was slower than naïve cells and provides further support that HCV-infection delays cell cycle progression ( data not shown ) . Quantitative PCR analysis of a number of the genes shown in Figure 4A demonstrated a good correlation with the gene expression data from the microarrays ( Figure 4B ) , although the ratios calculated from RT-PCR generally exceeded those obtained using microarrays . Flow cytometry analysis was performed to determine if HCV infection was associated with alterations in the cell cycle . Specifically , the number of cells progressing through S-phase of the cell cycle was determined by pulse-labeling the cells with the nucleoside EdU ( 5-ethynyl-2′-deoxyuridine ) , followed by a copper-catalyzed covalent reaction to fluorescently detect the DNA-incorporated nucleoside analog ( see Materials and Methods ) . At 72-hours post-infection , approximately 21% of the HCV-infected population showed evidence of EdU incorporation/DNA synthesis , compared to 61% of the UV-inactivated control cells ( Figure 4C ) . This significant reduction of labeled cells in the HCV-infected population suggests reduced cellular proliferation , or a block in cell cycle progression prior to S-phase , due to the presence of the virus . Comparable results were obtained when performing the analysis by immuno-fluorescence on fixed/attached cells ( Figure 4D ) . Reduced cell proliferation due to HCV could also be seen at earlier time-points ( 24 and 48 hours post-infection ) , but the difference was not as dramatic , although it was progressive ( data not shown ) . HCV infection was also associated with differential expression of genes associated with cytokine/growth factor signaling , with the highest induction again at 72 hours post-infection ( Figure 5 ) . These genes included pro-inflammatory cytokines which are chemotactic for specific immune cells ( e . g . CCL4-macrophages , CXCL1-neutrophils , IL8/CXCL2/CXCL3-PMNs , CX3CL1-macrophages , NK , lymphocytes , and CCL20-dendritic/lymphocytes ) . Of particular interest , some of the cytokines induced by HCV infection in vitro ( including CCL4 , CXCL1 , IL32 , TGFβI , TNFRSF12A , SOCS3 and TNFSF14 ) were identified by ANOVA analysis as being significantly ( P value<0 . 01 ) associated with fibrosis progression in these transplant patients ( data not shown ) . The fact that they are induced in HCV-infected Huh-7 . 5 cells suggests that hepatocytes themselves are an important source of these cytokines in an HCV-infected liver . The induction of SOCS2 and SOCS3 , negative regulators of cytokine signaling , may indicate that the hepatocytes are actually trying to attenuate the expression of cytokines , possibly because they are negatively impacting cell viability . Interestingly , despite the lack of both TLR3 and a functional RIG-I in Huh-7 . 5 cells , induction of known interferon stimulated genes ( ISGs ) , including ISG15 and ISG20 , was also observed . Comparison of the gene expression profiles of HCV-infected and IFN-treated Huh-7 . 5 cells revealed significant overlap in differentially regulated genes , suggesting that HCV infection is associated with activation of Type 1 IFN signaling ( data not shown ) . As the cultures were nearly 100% infected , the expression of these genes is likely coming from HCV-infected hepatocytes . TGF-β1 is the most likely candidate for exerting effects on hepatocytes that are consistent with the gene expression data indicating cell arrest and apoptosis . It is a potent inhibitor of cell growth of many cell types , including hepatocytes , and growth arrest occurs by blocking the cell cycle at middle and late G1 phase of cell cycle [13] . Although TGF-β1 itself was not induced , there was increased abundance of a significant number of genes associated with TGF-β1 signaling , particularly at 72 hrs post-infection . Many of these genes are either associated with the TGF-β1 signaling pathway ( BMP2 , TGIF1 , SMAD7 and 9 , MRAS , FOS , ROR1 , PDGRFA , JUN , INHBA ) and/or are known to be regulated by TGF-β1 . The increased expression of Smad7 , which provides a TGF-β1-induced negative feedback loop by inhibiting nuclear translocation of SMAD proteins , suggests that TGF-β1 is produced and interacting with receptors present on hepatocytes [14] . Interestingly , KLF10 ( also known as TIEG ) is a gene induced by TGF-β1 that induces the generation of ROS and the loss of mitochondrial membrane potential prior to death . This , together with the fact that p53 plays a key role in TGF-β1-induced growth arrest [15] , may provide an important link between the three most significant pathways affected by HCV replication: TGF-β1 signaling , p53 signaling and the NFR2-mediated oxidative stress response ( Figure 6 ) . As indicated by the network analysis , there is extensive interaction between genes associated with these pathways . As part of a separate study examining the progression of fibrosis in liver transplant patients with re-current HCV , microarray experiments were performed comparing individual patient liver biopsy tissue ( n = 25 ) to a pool of normal , uninfected liver tissue . To determine the clinical relevance of transcriptional changes observed in HCV-infected Huh-7 . 5 cells , the expression of the cell death-related genes regulated at 72 hrs post-infection was assessed in liver tissue from these HCV-infected patients . As shown in Figure 7A , many of the genes that were induced during HCV infection in cell culture were also regulated during re-current HCV infection in liver transplant patients . Interestingly , the increased expression of a subset of these genes appeared to be associated with liver disease progression as they were , in general , more highly induced in patients who developed rapidly progressive fibrosis post-transplant ( indicated in red text ) than in patients who did not ( black text ) . The fact that only a subset of the cell death genes regulated in vitro were regulated in liver tissue can likely be attributed to the much lower incidence of hepatocyte apoptosis and multiple cell types in the livers of chronically infected patients . A similar scenario was observed when the expression of cytokine signaling genes differentially regulated during HCV infection of Huh-7 . 5 cells was assessed in the patient cohort . A subset of these genes was more highly expressed in patients who develop recurrent liver disease ( indicated in red text in Figure 7B ) . Significantly , some were identified by ANOVA ( comparing patients with and without re-current disease post-transplant ) as being statistically ( P value<0 . 05 ) associated with fibrosis development . Collectively , these data demonstrate that , despite JFH-1 being somewhat of an atypical HCV , transcriptional changes which occur in HCV-infected Huh-7 . 5 cells parallel those which occur specifically during fibrosis development in HCV-infected patients .
This study represents the first report of global transcriptional profiling of HCV-J6/JFH-infected cultured human hepatoma cells . It is unique in that it examines the host response to in vitro infection during the early acute phase of infection . The host transcriptional response corresponds closely to the levels of HCV replication , with the most gene expression changes coinciding with peak intracellular viral load ( 72 hours ) . No changes in gene expression were observed in cells treated with UV-inactivated HCVcc , indicating that viral attachment/entry does not significantly impact host gene expression . It also indicates that the changes observed in the HCV-infected cells are dependent on HCV replication and not the interaction of secreted cellular factors present in the inoculum . In contrast , both replication-dependent and -independent transcriptional changes are observed during acute influenza virus infection [16] . This discrepancy may be a reflection of inherent differences in host response to acute versus chronic viruses . Chronic viruses such as HCV may have evolved to cause minimal impact upon entry into cells in an effort to delay cellular changes that may trigger an immune response , as evidenced by the minimal effect on host gene expression even at 24 hrs post-HCV infection . Similar results were obtained in transcriptional profiling of acute HBV infection in chimpanzees , where viral entry and expansion occurred in the absence of host gene regulation [17] . Significantly , the results of this study indicate that HCV has the potential to mediate direct cytopathic effects , suggesting that not all liver injury during chronic HCV infection is immune-mediated . The initial appearance of cytopathic effect , activated caspase-3 and the highest induction of cell death-related genes all coincided with peak viral loads , suggesting that intrahepatic HCV RNA levels play a role in hepatocyte cell death . While the role of viral load in HCV-liver disease remains controversial , there is evidence to suggest that higher replication rates are associated with more severe liver disease , particularly in the liver transplant setting . Significantly higher pre- and post-transplant serum HCV levels has been associated with cholestatic fibrosis , a severe form of hepatitis [18] . Similarly , elevated serum HCV pre-transplant is associated with accelerated HCV-induced allograft injury [19] . High levels of intrahepatic HCV in biopsies taken at early times post-transplant was found to be an independent predictor of progression to chronic active hepatitis [20] . In the non-transplant setting , in situ hybridization demonstrated an association between the number of hepatocytes harboring replicating HCV and severity of fibrosis [21] . Collectively , these studies suggest that elevated viral replication may cause increased liver injury . Differences in viral load may actually provide an explanation for the discrepancy in the level of hepatocyte cell death that occurred in vitro , which involved the majority of cells , and during chronic HCV infection . The lack of important dsRNA signaling molecules in the hepatoma cell line Huh-7 . 5 used for this study allows higher levels of HCV replication than what is typically observed in the liver of chronic HCV patients ( Walters , unpublished data ) , presumably due to decreased activation of an intracellular innate antiviral response . The balance of IFN-mediated suppression of HCV replication and HCV-mediated regulation of host innate antiviral pathways likely varies from cell to cell in infected livers . If the balance shifts more toward HCV control over innate antiviral signaling , then HCV levels within that cell may increase to a level that is incompatible with cell survival . HCV infection in Huh-7 . 5 cells likely reflects the extreme of this situation . Indeed , levels of HCV replication in Huh-7 cells are much lower than in Huh-7 . 5 cells and is associated with a delay in cell death . It is possible that apoptotic hepatocytes in infected livers also have higher levels of HCV replication than non-apoptotic cells , although this would be technically challenging to demonstrate . A significant proportion of differentially expressed genes during in vitro HCV infection were associated with cell cycle checkpoint/arrest and subsequent induction of apoptosis , which may have been in response to oxidative stress/DNA damage . Also , the observed delayed growth kinetics of HCV-infected cells and flow cytometry analysis demonstrating fewer cells in S-phase , suggests that delayed cell cycle progression may be involved in HCV-mediated cytotoxicity . This is particularly interesting in light of recent studies where immuno-histochemistry of patient liver biopsies demonstrated that few hepatocytes which have entered the cell cycle go beyond G1 phase during chronic HCV infection [22] , [23] . The G1 arrest observed in patient livers was associated with increased expression of p21 , a cdk-cyclin inhibitor which causes G1 arrest after DNA damage . A correlation was observed between p21 expression and fibrosis severity , suggesting a link between delayed cell cycle progression and liver injury . Such results suggest the delay in cell cycle progression observed in HCV-infected Huh-7 . 5 cells is physiologically relevant . Consistent with these in vivo studies , many of the genes associated with arrest during in vitro HCV infection are involved in G1 arrest or transition from G1 to S phase . Interestingly , a link between perturbations in cell cycle control and pathogenesis has been observed in SIV infection in non-human primates , another chronic viral infection that can have different disease outcomes . Perturbation of the cell cycle within CD4 ( + ) T lymphocytes is characteristic of pathogenic HIV/SIV infection [24] , [25] , [26] and , similar to what is proposed in the current study , increased T lymphocyte susceptibility to apoptosis correlates with cell cycle perturbation [26] . Unlike the AIDS field , there is no animal model of HCV-associated liver disease in which to validate the biological significance of events which occur in cell culture models . To circumvent this challenge , gene expression data from in vitro HCV infection was integrated with an extensive database of patient liver microarray experiments . The intriguing finding that a higher induction of a subset of these genes was observed in HCV-infected patients with rapidly progressive fibrosis post-transplant , but not those HCV-infected patients lacking histological evidence of fibrosis , also suggests a potential role of cell cycle perturbations in HCV pathogenesis . However , it is important to note that this patient cohort is immuno-compromised and so it is uncertain if HCV-associated CPE causes significant liver injury in immuno-competent individuals . Both gene expression profiling and flow cytometry analysis suggest that HCV-mediated apoptosis of Huh-7 . 5 cells is linked to perturbations in cell cycle progression . However , it is difficult to determine from the gene expression data if the cell cycle arrest is directly linked to apoptosis or if there other factors that are driving the arrested cells to undergo apoptosis . It is also not clear what factors are responsible for inducing the delay in cell cycle progression . Perturbation of the cell cycle may be mediated directly by HCV proteins , as has been observed in other viral infections including HIV ( vpr ) and HBV ( x protein ) [27] , [28] . Due to the association between chronic HCV infection and development of hepatocellular carcinoma , there has been keen interest in the impact of HCV on cell cycle regulation . Core protein in particular has been implicated in impairment of G1 to S phase transition through multiple mechanisms , including induction of p21 expression and concomitant decrease in cdk2 activity [29] , direct interaction and suppression of CAK activity [30] , and stabilization of cell cycle inhibitor p27 [31] . Delayed progression through S-phase has been shown to be mediated both by NS2-mediated down-regulation of cyclin A [32] and NS5B induction of IFN-B [33] . NS5A-induced chromosome instability has been linked to aberrant mitotic regulation , including impaired mitotic exit [34] . It is unclear whether delaying cell cycle progression would be beneficial or detrimental to HCV replication . The liver is normally a quiescent organ and so hepatotropic viruses which establish chronic infections , such as HBV and HCV , may have evolved to replicate efficiently under such non-proliferating conditions . In support of this , production of infectious HCV in cultured hepatoma cells does not seem to be significantly impacted during growth arrest induced by either serum starvation or DMSO [35] , [36] . The differential regulation of numerous genes associated with DNA damage/oxidative stress response , including many associated with the NRF2-oxidative stress response , suggests this as a possible mechanism of arrest . Oxidative stress has long been thought to play a key role in HCV pathogenesis and a potential link between HCV-associated perturbation of lipid metabolism genes and oxidative stress was observed in the SCID-Alb/uPA mouse model [5] . Specifically , HCV-infected animals showing induction of genes functioning in cholesterol biosynthesis , peroxisome proliferation , and β-oxidation also showed induction of genes which function in antioxidant cell defense , presumably due to generation of reactive oxygen species ( ROS ) during β-oxidation of fatty acids . In the current study , oxidative stress appears to be independent of lipid metabolism . No significant regulation of genes associated with cholesterol synthesis or enzymes involved in β-oxidation was observed at any time-point of infection . It is possible that the presence of viral gene products or replication directly results in the generation of ROS . Core protein has been implicated in perturbations in mitochondrial function , including release of cytochrome c and loss of membrane potential , as well as production of ROS in a variety of systems [37] , [38] , [39] . Consistent with the current study , expression of full-length HCV open reading frame was found to cause marked growth inhibition and increased intracellular ROS [40] . Preliminary global quantitative proteomic data showed multiple perturbations in the host proteome indicative of HCV-associated metabolic stress and ROS generation as early as 24 h post infection ( Diamond et al , manuscript in preparation ) . Consistent with this idea , these perturbations were accompanied by a concomitant increase in proteins functioning in antioxidant cell defense . Another possible mechanism of cell arrest could be through activation of TGF-β1 signaling . TGF-β1 is a potent inhibitor of cell growth and apoptosis of many cell types , including hepatocytes , and growth arrest occurs by blocking cell cycle at mid and late G1 phase [13] , [14] . This is consistent with the gene expression data showing regulation of numerous genes involved in G1 arrest . Significant correlations between TGF-β1 polymorphisms , intensity of hepatocyte-specific TGF-β1 staining , serum TGF-β1 levels and degree of fibrosis have consistently been demonstrated in HCV-infected patients [41] , [42] , [43] . However , its role in hepatocyte apoptosis during HCV infection remains unclear . The possibility that TFG-β1 may be directly involved in HCV-associated cell cycle arrest is intriguing . While increased expression of TGF-β1 was not observed in the current study , the expression of numerous genes associated with TGF-β1 signaling pathway , and those known to be induced by TGF-β1 , was elevated during infection . It is possible that very low levels of the cytokine are needed to activate the pathway , a scenario similar to what is observed with Type 1 IFN and ISGs . Furthermore , in an infected liver other cell types , including hepatic stellate cells , are an important source of TGF-β1 . Chronic HCV infection is universally associated with the induction of ISGs in the liver but usually in the absence of detectable increased expression of IFN-α/β [5] , [44] . TGF-β1 has been proposed as a potential therapeutic target for treatment of viral hepatitis and so a clear understanding of the role it plays in HCV pathogenesis is crucial [45] . Further study is warranted to determine if TGF-β1 is inducing cell arrest and also whether this is directly linked to apoptosis or simply sensitizing cells to apoptosis through alternative mechanisms , including the effects of oxidative stress . Alternate mechanisms of HCV-induced cytopathic effects , such as through induction of ER stress , have been proposed [46] . However , there was little evidence for regulation of genes associated with either ER stress or the unfolded protein response in the current study . This is consistent with a recent study demonstrating that HCV-JFH-1 mediates apoptosis through a mitochondrion-mediated , caspase-3 dependent pathway in the absence of ER stress [47] . Although ER stress is associated with transcriptional regulation of a subset of genes , it is unclear if gene expression profiling would accurately detect ER stress and so it should not be ruled out as a possible contributor of apoptosis in the current study . Apoptosis has also been found to be mediated through the induction of the death ligand , TRAIL , and its receptors [48] . No increase in the expression of TRAIL , or its receptors , was observed in the current study , making it unlikely to be involved in apoptosis of Huh-7 . 5 cells . This is possibly related to the absence of functional RIG-I and TLR3 in these cells , which are key components of dsRNA signaling pathways . It is important to note that these alternative mechanisms of HCV-mediated cell death are not necessarily mutually exclusive , and multiple mechanisms of cytotoxicity may be involved in liver injury during chronic HCV infection . Collectively , the gene expression data and flow cytometry analysis suggests that HCV infection is associated with perturbation of the cell cycle which may sensitize cells to apoptosis . Significantly , the integration of the in vitro and patient liver gene expression data also suggests that this process contributes to liver disease progression . These results suggest that despite the fact that HCV typically establishes persistent infections , events which occur during the very acute phase of infection of individual hepatocytes can determine the ultimate fate of the cell . During the time this manuscript was in preparation , a report was published describing altered expression of cell cycle and apoptotic proteins , as demonstrated using immunohistochemistry , in liver biopsies from chronic HCV patients [49] . However , the delay in cell cycle progression and apoptosis were observed in separate cell types ( hepatocytes versus sinusoidal cells , respectively ) . The results of the current study demonstrate that cell cycle perturbation and apoptosis occur in the same cell , suggesting a direct link , and also provide additional insight into potential mechanisms of cell cycle perturbation , including oxidative stress and TGF-β1 signaling . Further study is warranted to more clearly define the mechanism of HCV-associated cell cycle perturbation . This may provide significant insight into the pathogenesis of HCV infection with the possibility of identifying novel therapeutic targets .
Huh 7 . 5 cells ( human hepatoma ) were electroporated with 1 µg of in vitro transcribed RNA from the chimeric HCV genome J6/JFH ( see reference 7 ) ; five identical electroporations were performed . Cells were expanded three times following electroporation and supernatants were pooled and used to infect naïve Huh 7 . 5 cells . Following a single expansion of infected cells , virus containing supernatants were collected , pooled , and used to again infect naïve Huh 7 . 5 cells . This process was repeated a total of five times and resulted in the generation of a relatively high titer ( 2×105 TCID50/ml ) and large volume ( ∼500 mls ) of stock virus ( HCVcc ) . Approximately one-half of the HCVcc stock was exposed to UV light for 60 seconds , using a Stratalinker UV light box , and served as a non-infectious control ( UV-HCV ) . In addition , during the multiple passages of HCVcc on naïve cells , a mock control sample was generated by passing conditioned media along in parallel . For infections , Huh 7 . 5 cells were seeded at a density of 3×106 cells/plate on p150 plates and treated for ∼8 hours with 20 mls of supernatant containing virus ( HCVcc ) , UV-inactivated virus ( UV-HCV ) , or conditioned media ( mock ) . This amounted to a moi of ∼1 . 3 . Following initial infection , the supernatant was replaced with fresh media and incubated until harvest at 24 , 48 , 72 , 96 , and 120 hours post-infection . Following removal of supernatant , cells were washed once with PBS and then scraped from the plate in ice cold PBS . RNA was isolated from approximately 106 cells using RNeasy mini prep kit with an on-column DNase treatment , following the manufacturer protocol ( Qiagen ) . Cells were fixed in −20°C methanol or 1% paraformaldehyde ( in PBS at room temperature ) for 15–20 minutes . Following a series of PBS rinses , the cells were blocked in 1% BSA/0 . 2% skim milk in PBS for 30–60 minutes at room temperature . Cells were incubated in primary antibody ( diluted in 0 . 5% Tween-20 in PBS ) overnight at 4°C; mouse anti-NS5A ( 1∶2000 , Clone 9E10 , ref . 21 ) and rabbit anti-activated caspase-3 ( 1∶500 , Cell Signaling ) . Click-iT EdU chemistry was performed following manufacturer protocol ( Invitrogen ) ; 10 µM EdU labeling for 3 hours and 30 minute reaction with AlexaFluor 488 azide ( 1∶400 dilution ) . Secondary antibodies are AlexaFluor conjugated and used at 1∶1000 dilution; goat anti-mouse AlexaFluor488 and goat anti-rabbit AlexaFluor594 . Cells were trypsinized , washed twice with ice cold PBS and fixed 1% paraformaldehyde for 20 minutes . Following a series of PBS rinses , the cells were blocked and permeabilized in 0 . 1% FBS/0 . 1% saponin in PBS . HCV infected cells were detected using an anti-NS5A antibody ( clone 9E10 ) directly conjugated with the AlexaFluor647 fluorophore ( according to manufacturer protocol; Invitrogen , A20173 ) . Proliferating cells were detected using Click-iT EdU chemistry , as described above , following manufacturer guidelines for flow cytometry analysis ( Invitrogen ) ; AlexaFluor 488 azide ( 1∶400 dilution ) . Flow cytometry was performed on a BD FACSCalibur machine with analysis done using FlowJo software ( version 8 . 7 . 1 ) . Core needle liver biopsies were collected from patients at the University of Washington All patients gave informed consent to protocols approved by the Human Subjects Review Committee at the University of Washington . Normal , uninfected liver tissue ( n = 10 ) was obtained from donor livers that were considered unacceptable for liver transplantation . These uninfected samples were pooled to create a standard normal liver reference that was used for all microarray experiments using patient tissue . Fibrosis was graded by a single liver pathologist using the Batts-Ludwig grading system [50] . Microarray format , protocols for probe labeling , and array hybridization are described at http://expression . microslu . washington . edu . Briefly , a single experiment comparing two mRNA samples was done with four replicate Human 1A ( V2 ) 22K oligonucleotide expression arrays ( Agilent Technologies ) using the dye label reverse technique . This allows for the calculation of mean ratios between expression levels of each gene in the analyzed sample pair , standard deviation and P values for each experiment . Spot quantitation , normalization and application of a platform-specific error model was performed using Agilent's Feature Extractor software and all data was then entered into a custom-designed database , Expression Array Manager , and then uploaded into Rosetta Resolver System 7 . 0 ( Rosetta Biosoftware , Kirkland , WA ) and Spotfire Decision Suite 8 . 1 ( Spotfire , Somerville , MA ) . Data normalization and the Resolver Error Model are described on the website http://expression . viromics . washington . edu . This website is also used to publish all primary data in accordance with the proposed MIAME standards [51] . Selection of genes for data analysis was based on a greater than 95% probability of being differentially expressed ( P≤0 . 05 ) and a fold change of 2 or greater . The resultant false positive discovery rate was estimated to be less than 0 . 1% ( Walters , unpublished data ) . Ingenuity Pathway Analysis ( IPA ) software and Entrez Gene ( www . ncbi . nlm . nih . gov/sites ) were used for gene ontology analysis . Quantitative real-time PCR ( RT-PCR ) was used to validate the gene expression changes and measure intrahepatic HCV RNA . Total RNA samples were treated with DNA-free DNase Treatment and Removal Reagents ( Ambion , Austin , TX ) . Reverse transcription was performed using random hexamer primers and Taqman RT reagents ( Applied Biosystems , Foster City , CA ) . Real-time PCR was performed using an ABI 7500 Real Time PCR system and Taqman chemistry . Each target was run in quadruplicate with Taqman 2× PCR Universal Master Mix and a 20 µL total reaction volume . Primer and probe sets for relative quantification were selected from the Assays-on-Demand product list ( Applied Biosystems ) including two endogenous controls , GAPDH and 18 S ribosomal RNA . Quantification of each gene , relative to the calibrator , was calculated by the instrument , using the equation 2−ΔΔCT within the Applied Biosystems Sequence Detections Software version 1 . 3 . Probes used for analysis ( Applied Biosystems ) : Human genes: eukaryotic 18S rRNA ( Catalogue No . Hs99999901_s1 ) ; ATF3 ( catalogue No . Hs00910173_ml ) , MKi67 ( catalogue No . Hs01032443_ml ) , MCM4 ( catalogue No . Hs00381539_ml ) , MCM6 ( catalogue No . Hs00195504_ml ) , TGIF1 ( catalogue No . Hs00545014_ml ) , CAT ( catalogue No . Hs00156308_ml ) , SMAD7 ( catalogue No . Hs00998193_ml ) , GADD45A ( catalogue No . Hs00169255_ml ) , GADD45B ( catalogue No . Hs00169587_ml ) Primer and probe sets for absolute quantification of intrahepatic viral load were designed based on sequences of HCV 1a armored RNA ( Ambion Diagnostics , Austin , TX ) using Primer Express ( version 3 ) . A standard curve was made from six serial dilutions of HCV 1a armored RNA ( Ambion Diagnostics ) with a known viral copy number . The PCR efficiency was determined by the slope of the standard curve Standard curve analysis and viral load was determined using the Applied Biosystems SDS Software 1 . 3 ( Applied Biosystems , CA ) . Total RNA was DNase treated prior to cDNA synthesis via reverse transcription and all samples were processed with equal mass amounts of total RNA [52] . All measurements were taken in quadruplicate with negative and non-template controls . Primer and probe sets consisted of F: CAC TCC CCT GTG AGG AAC TAC TG , R: GCT GCA CGA CAC TCA TAC TAA CG , and P: 6FAM-TTC ACG CAG AAA GC-MGBNFQ and were designed from the 5′UTR using Primer Express 3 . 0 ( Applied Biosystems , CA ) . Quantification of HCV RNA levels was performed on the same total RNA sample that was used for the microarray experiments . | Chronic HCV infection is associated with progressive liver injury and subsequent development of fibrosis/cirrhosis . The cellular mechanisms by which HCV replication , and subsequent virus–host interactions , may mediate liver injury are unclear . Microarray experiments were performed to characterize the host transcriptional response to HCV infection of cultured hepatocytes in an attempt to gain insight into the mechanism of HCV-associated cell death . Analysis of the gene expression data revealed that many differentially regulated genes function to induce apoptosis in response to cell cycle arrest , possibly in response to DNA damage and oxidative stress . Labeling of dividing cells in culture followed by flow cytometry also demonstrated the presence of significantly fewer cells in S-phase in HCV-infected cultures relative to mock cultures , suggesting HCV infection is associated with delayed cell cycle progression . Finally , many of the cell-death–related genes whose expression changes in response to HCV infection of cultured hepatocytes were also differentially regulated in liver tissue from HCV-infected patients with histological evidence of fibrosis . In summary , HCV may mediate direct cytopathic effects through perturbation of the cell cycle which potentially contributes to liver disease progression . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"virology/virulence",
"factors",
"and",
"mechanisms",
"virology/animal",
"models",
"of",
"infection"
] | 2009 | Genomic Analysis Reveals a Potential Role for Cell Cycle Perturbation in HCV-Mediated Apoptosis of Cultured Hepatocytes |
For most HIV-infected patients , antiretroviral therapy controls viral replication . However , in some patients drug resistance can cause therapy to fail . Nonetheless , continued therapy with a failing regimen can preserve or even lead to increases in CD4+ T cell counts . To understand the biological basis of these observations , we used mathematical models to explain observations made in patients with drug-resistant HIV treated with enfuvirtide ( ENF/T-20 ) , an HIV-1 fusion inhibitor . Due to resistance emergence , ENF was removed from the drug regimen , drug-sensitive virus regrown , and ENF was re-administered . We used our model to study the dynamics of plasma-viral RNA and CD4+ T cell levels , and the competition between drug-sensitive and resistant viruses during therapy interruption and re-administration . Focusing on resistant viruses carrying the V38A mutation in gp41 , we found ENF-resistant virus to be 17±3% less fit than ENF-sensitive virus in the absence of the drug , and that the loss of resistant virus during therapy interruption was primarily due to this fitness cost . Using viral dynamic parameters estimated from these patients , we show that although re-administration of ENF cannot suppress viral load , it can , in the presence of resistant virus , increase CD4+ T cell counts , which should yield clinical benefits . This study provides a framework to investigate HIV and T cell dynamics in patients who develop drug resistance to other antiretroviral agents and may help to develop more effective strategies for treatment .
Antiretroviral therapy has been used to successfully treat HIV-1 infection . However , a subset of patients develops drug resistance followed by an observable increase in plasma HIV viral load . This “virological failure” usually triggers a change in the drug regimen . Here we examine a situation in which patients had developed resistance to most common drugs and a novel agent , enfuvirtide , was added to their failing drug regimen . When resistance to enfuvirtide developed the use of this agent was discontinued in the hope that drug-sensitive virus would outcompete the resistant virus and enfuvirtide could be given again . Despite the fact that resistance developed when enfuvirtide was re-administered and viral loads were unable to be suppressed , CD4+ T cell counts were preserved or increased . Observing increasing CD4+ T cell counts without viral suppression is intriguing and suggests that issues of viral fitness may play a role . Fitness costs have been associated with drug resistance not only to enfuvirtide but also to other drug classes [1]–[6] . Further , despite virologic failure due to the emergence of drug resistance , continued treatment that imposes selective pressure on drug sensitive virus and causes outgrowth of resistant HIV is often associated with benefits such as higher sustained CD4+ T cell counts and reduction in the risk of morbidity and mortality [2]–[5] . To uncover the nature of the CD4+ T cell increase and to determine a general principle that may be useful in developing treatment strategies in the face of drug resistance , we performed a detailed viral kinetic analysis of a set of patients treated with enfuvirtide in which longitudinal measurements of drug sensitive and drug resistant viral levels , as well as CD4 counts , were available . Enfuvirtide ( ENF ) , formerly called T-20 , is a 36 amino acid synthetic peptide that binds to the HR-1 region of the HIV-1 gp41 molecule , thereby preventing fusion of the viral membrane with the target cell membrane [7] . It is the first FDA-approved HIV-1 fusion inhibitor [8] . As ENF is expensive and must be administered parenterally , it is often reserved for heavily pretreated patients with limited therapeutic options [9]–[13] . ENF acts extracellularly prior to viral entry . This feature provides a number of benefits , such as less susceptibility to cellular efflux transporters that lower the effective intracellular concentrations of other classes of antiretroviral drugs and little or no drug-drug interactions with drugs metabolized by the CYP 450 or N-acetyltransferase route [14] . As with other antiviral drugs , in patients treated with ENF , the high replication rate of HIV and the low fidelity of HIV reverse transcriptase can lead to the development of drug resistance [14] . Resistance to ENF occurs due to amino acid substitutions within the HR-1 region of gp41 at amino acids 36–45 of HIV-1 gp41 with G36D , G36S , G36V , G36E , V38A , V38M , V38E , Q40H , N42T , and N43D being the most common ENF resistant mutations [12] , [13] , [15] . These mutations result in significantly reduced binding of ENF to HR-1 [16] . Since ENF is expensive and poorly tolerated , many individuals interrupt this drug once virologic failure is confirmed . In a single arm prospective study of individuals exhibiting virologic failure on ENF , selective interruption of ENF was not associated with any appreciable increase in HIV RNA levels , suggesting that the drug had only limited residual activity and hence its use during failure may not be warranted [11] , [17] . Observational data from other groups , however , have suggested that there may be a CD4+ T cell benefit associated with certain ENF-associated mutations [18] . These data suggest that despite virological failure the drug may have continued benefit due to alterations in the virus's pathogenic effects . Interruption of ENF in individuals with ENF-resistance is associated with a rapid decay in the resistant variant [11] , [13] , [17] . The reason resistant virus decays in the absence of drug is not fully understood . Although the rebound of archived more “fit” wild-type virus is often cited as the major mechanism whereby HIV resistance decays in the absence of therapy [13] , [17] , ongoing evolution within the envelop gene and the eventual selection of the wild-type virus may also account for the loss of ENF resistance when this drug is interrupted [9] . Despite marked differences in fitness of drug-sensitive and drug-resistant viruses and evidence of ongoing viral evolution , plasma HIV-1 RNA levels remain almost constant during ENF interruption [13] . This apparent paradox suggests that viral fitness may not be a major determinant of the steady-state level of viremia . To more fully understand the role of viral fitness as well as other parameters determining the dynamics of HIV-1 during ENF interruption , we use mathematical models to study the competition between ENF sensitive and ENF resistant viruses after the interruption of ENF and during subsequent re-administration . We consider only the V38A mutant because this single substitution in HIV-1 gp41 is the most frequently observed in drug resistant virus [19] and data on the population size of mutants with V38A are available [13] . We estimate the rate of forward and backward mutations , the replication capacity of both drug-sensitive and drug-resistant viruses , and the efficacy of ENF against viral fusion when it is re-administered after interruption . We also examine the effect of target cell level on the dynamics and steady states of drug sensitive and resistant viruses during ENF interruption and subsequent re-administration . Lastly , we discuss virus population turnover and plasma viral RNA levels during the presence and absence of the drug .
We obtained wild-type and V38A mutant viral load and CD4+ T cell data from Department of Medicine , University of California-San Francisco , CA , USA , San Francisco General Hospital , San Francisco , CA , USA and Section of Retroviral Therapeutics , Brigham and Women's Hospital and Division of AIDS , Harvard Medical School , Boston , MA , USA . Viral load and CD4+ data were obtained for three HIV-1 infected subjects ( P1 , P2 , and P3 ) during ENF interruption who continued to receive the other drugs in their antiretroviral regimen . Before ENF interruption , subjects P1 , P2 and P3 were treated with ENF for 27 , 33 and 39 weeks , respectively , and each of them had the V38A mutation as the predominant virus population ( more than 85% frequency ) . Viral load and CD4+ data were also obtained during subsequent 4-week re-administration of ENF after interruption for 76 , 68 and 38 weeks , respectively . For subject P3 , the data were also collected during a second interruption of ENF . Therefore , there were two data sets during ENF interruption for subject P3 . A schematic diagram of the model is shown in Fig . 1 . The model contains five variables: uninfected target cells , T , cells infected by ENF-sensitive virus , Is , cells infected by ENF-resistant virus , Ir , ENF-sensitive virus , Vs , and ENF-resistant virus , Vr . The model assumes that target cells are produced at a constant rate , λ , and die at rate dT . ENF-sensitive virus infects target cells to produce infected cells , Is , at rate βsTVs , among which a fraction μsβsTVs , become ENF-resistant during the process of reverse transcription of viral RNA to DNA due to mutation at rate μs . Similarly , the infection by ENF-resistant virus produces infected cells , Ir , at rate βrTVr , with a fraction μrβrTVr undergoing backward mutation to the drug sensitive strain at rate μr . Cells infected by ENF-sensitive and ENF-resistant virus produce new virions at rates psIs and prIr , and die at rates δIs and δIr , respectively . Both viruses are cleared at the same rate c per virion . Whether the V38A mutation in gp41 affects viral production remains unclear . For simplicity , we assume ps = pr , and describe the resistance-associated fitness loss only by a reduced infectivity rate , i . e . , βr = ( 1−α ) βs , where the fitness cost of the mutant virus , α , satisfies 0≤α≤1 . ENF is a fusion inhibitor and reduces infection of target cells by free virus . We assume εs and εr are the efficacies of ENF against ENF-sensitive and ENF-resistant virus , respectively , with 0≤εs , εr≤1 . In the patient data we analyze the populations of both drug-resistant and drug-sensitive virus always remain high ( above 2 . 8 log10 HIV RNA copies/ml ) . Thus , stochastic effects would not be significant and we formulate the model as a deterministic model – a standard two-strain viral dynamic model – similar to the ones in [20] , [21] . The model is described by the following differential equations: ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) As measured by Ki-67 antigen expression , only a small percentage of CD4+ T cells in peripheral blood appear to be activated into proliferation and hence are preferred targets for HIV-1 infection [22] . Therefore , we take only a fraction of the total CD4 count , i . e . the activated cells , as targets for HIV-1 infection and estimate this fraction . The total CD4+ T cell count is assumed to be given by ( T+Is+Ir ) /a , where a denotes the fraction of CD4+ T cells that are activated . In principle , a could be time-varying or in particular depend on the CD4+ T cell count [22] . However , the CD4 count of the patients in this study always remains below 200/µl , and according to the relationship between CD4 count and activated cell percentage given in [22] , a 10-fold change in CD4 count ( from 20 to 200/µl ) causes only a minor change in activation percentage ( from 8 . 6% to 10 . 4% ) . Therefore , for our study we felt it reasonable to assume a is constant . We note that in the study we analyze [13] , ENF is given in combination with other drugs , the infection rates βs , βr and the virus production rates ps , pr that we estimate include the effects of the other drugs in the background regimen . However , since the background regimen was failing to suppress HIV replication , these effects may be minimal . Moreover , the data have taken only V38A mutants into account with other mutants being included in the “wild-type” . Therefore ENF efficacy against wild-type , εs , in our model also incorporates the possible reduction in efficacy due to other mutants included in the wild-type . Further , loss of V38A mutation at rate μr , can lead to any of a variety of viral variants that we include in the drug sensitive population . Lastly , virus variants carrying the V38A mutation may also carry other mutations , such as compensatory mutations or other drug resistance mutations , which may affect the fitness of the drug-resistant population as well as its level of drug resistance . We note that there is loss of some free virus due to the infection of target cells as virus must enter a cell in order to infect . To incorporate this effect , one can add the terms − ( 1−εs ) βsTVs and − ( 1−εr ) βrTVr to Eqs . ( 4 ) and ( 5 ) , respectively . For the measured range of T in the subjects considered here , and the estimates of βs and βr determined below , βsT and βrT are <0 . 05 d−1 which is ∼500 times lower than the viral clearance rate c ( 23 d−1 ) , indicating that virion loss due to infection will have negligible effect on the viral dynamics compared to the term −cV . We confirmed this by fitting the model with the terms − ( 1−εs ) βsTVs and − ( 1−εr ) βrTVr in Eqs . ( 4 ) and ( 5 ) , respectively , in which we found almost no change in parameter estimates . Therefore , we neglected virion loss due to infection and left only the viral clearance term ( −cV ) in the V equations . The dynamics of free virus is typically fast in comparison with that of infected cells [23]–[25] . Therefore , we assume a quasi-steady state , which from Eqs . ( 4 ) and ( 5 ) provides Vs = ( ps/c ) Is and Vr = ( pr/c ) Ir . This simplifies the model leaving only equations for T , Is and Ir . Further , we set Is ( 0 ) = ( c/ps ) Vs ( 0 ) and Ir ( 0 ) = ( c/pr ) Vr ( 0 ) for data fitting as well as all simulations , where Vs ( 0 ) and Vr ( 0 ) are determined by direct measurement at the start of interruption or the start of ENF re-administration . As measured by Mohri et al . [26] , we take the uninfected CD4+ T cell death rate d = 0 . 01 day−1 . Recent estimates show that the virion clearance rate constant , c , varies between 9 . 1 day−1 and 36 day−1 , with an average of 23 day−1 [25] , [27] . Therefore , we take c = 23 day−1 . During ENF interruption , we estimate the parameters λ ( target cell recruitment rate ) , βs ( drug sensitive virus infection rate ) , μs ( forward mutation rate ) , μr ( backward mutation rate ) , α ( fitness cost of ENF-resistance ) , ps ( production rate of drug sensitive virus ) , δ ( infected cell death rate ) , T0 ( initial uninfected target cell concentration ) and a ( fraction of CD4+ T cells that are activated ) by fitting the model to the ENF-sensitive viral load , the ENF-resistant viral load and the CD4 count data simultaneously for each patient . Since fewer data points are available during re-administration of ENF , we fix some parameters at the values obtained by estimation during ENF interruption; and only estimate εs ( ENF efficacy against the sensitive strain ) , εr ( ENF efficacy against the resistant strain ) , λ , T0 and a . We also fitted the data during ENF interruption and ENF re-administration allowing the initial concentrations of drug sensitive and drug resistant viruses to be free parameters , but the fit could not be improved . We solved Eqs . ( 1 ) – ( 5 ) numerically using the Runge-Kutta 4 algorithm in Berkeley Madonna [28] . We also used it to obtain the best-fit parameters via a nonlinear least squares regression method . The predicted log10 values of the ENF-sensitive and ENF-resistant viral loads and the CD4 count for each patient were fit to the corresponding log-transformed data . Of note , to avoid the difficulty of assigning different weights to the viral loads and CD4 counts in the objective function being minimized , and give equal importance to all the widely varied values in the data set , we fitted the data in the log-scale rather than linear-scale for both viral loads and CD4 count . Finally , for each best fit parameter estimate , we provide a 95% confidence interval ( CI ) using 200 bootstrap replicates [29] , which we performed in MATLAB .
The estimated viral dynamic parameters during ENF interruption along with their mean and sample standard deviation , and their 95% confidence interval are summarized in Table 1 and Table 2 , respectively . Using the estimated parameters , we found the predictions of the model agree well with the data for each of the study participants ( Fig . 2 ) . All the parameters are approximately the same for two ENF-interruptions in P3 , suggesting that the viral dynamic parameters remain stable over time . We estimated the rates of forward and backward mutation as 2 . 24±0 . 32×10−5 and 1 . 73±0 . 30×10−5 , respectively . Even though the backward mutation rate is slightly lower than the forward mutation rate , early after ENF interruption the ENF-resistant virus population is significantly larger than the ENF-sensitive virus population , and consequently the amount of backward mutation during the early post ENF interruption is usually higher than the amount of forward mutation . Although there is a continued evolution in gp41 after ENF interruption , our results show that the rate of on-going evolution including backward mutation accumulation is not sufficient to explain the rapid waning of ENF-resistant virus . For example , during the first week ( month ) post interruption , the contribution of ongoing evolution and backward mutation to the loss of cells carrying a drug-resistant proviral genome is only about 0 . 2 ( 0 . 4 ) cells per ml , which corresponds to the loss of 26 ( 70 ) drug resistant virions per ml per week ( month ) . Since the contribution of these de-novo mutations is small , we also fitted the data using the model without de-novo mutation , i . e . , μs = μr = 0 , and found that the changes in estimated parameter values lie within a range of 0–6% . As the loss of resistant virus due to backward mutation is negligible , we find the fitness cost , i . e . , the reduction of the infectivity of the resistant virus compared to the wild-type virus , plays a more important role in the decay of ENF-resistant virus and the increase of ENF-sensitive virus . Fitting our model to the data suggests that ENF-resistant virus is 17±3% less fit ( i . e . , α = 0 . 17±0 . 03 , Table 1 ) than ENF-sensitive virus in the absence of ENF . This fitness loss is consistent with the results in Marconi et al . [13] , although they obtained a higher estimate of the relative fitness cost ( 25–65% ) using a different fitness estimation method . Our estimates of the virion production rate , ps = pr = 3628±857 virions day−1 , the infected cell death rate , δ = 0 . 29±0 . 02 day−1 , and the sensitive virus infection rate , βs = 7 . 1±3 . 1×10−7 ml−1 day−1 ( Table 1 ) , are approximately consistent with the estimates 1427±2000 virions day−1 , 0 . 37±0 . 19 day−1 and 11 . 8±14×10−7 ml−1 day−1 , respectively , in Stafford et al . [30] . However , the estimate of δ is much smaller than that in some other studies [31] . We also estimated the uninfected cell recruitment rate λ = 790±311 cells ml−1 day−1 . Our estimate of a suggests that 11% of CD4+ T cells are activated , consistent with the finding that ∼10% of CD4+ T cells in peripheral blood are Ki-67+ in the patients with CD4 count less than 200 cells/µl [22] . After ENF interruption , ENF was re-administered to the study subjects for 4 weeks while keeping the same “background” regimen . During this re-administration of ENF , we estimated the ENF efficacies against sensitive and resistant viruses , εs and εr . Estimated values and their 95% confidence intervals are summarized in Table 3 and Table 4 , respectively . Comparisons of model predictions with the patient data are shown in Fig . 3 . Our estimates indicate that ENF re-administered following interruption is 66±6% effective in reducing infection by ENF-sensitive virus , while the effectiveness is reduced to 29±6% in reducing infection by ENF-resistant virus . This indicates that ENF-resistant variants still remain partially sensitive to ENF even though they have reduced susceptibility . We note that the efficacy of ENF against drug sensitive virus obtained here is a minimal estimate as it might have included the reduction of efficacy due to inclusion of other mutant virus in the drug sensitive virus data . Other estimated parameters during ENF re-administration ( Table 3 ) are more or less the same as those estimated during ENF interruption ( Table 1 ) . The continued activity of ENF against the drug-resistant virus is supported by the apparent immediate albeit transient and small increase in plasma HIV RNA levels observed when ENF was interrupted in a larger cohort of individuals ( see Figure 1 in [11] ) . Despite the difference in replication capacity and changes in the proportion of ENF-sensitive and ENF-resistant viruses ( Fig . 2a ) , the total plasma viral load remains approximately the same during ENF interruption ( Fig . 2b ) . The plasma viral load also remains unchanged during ENF re-administration ( Fig . 3 ) except for a nominal transient post-readministration suppression followed by a rebound . This raises a question: what determines the plasma viral load ? We first studied the effect of ENF-resistant virus fitness cost on plasma viral load . In Figs . 4a and 5a , we show the plasma viral load obtained from our model for different fitness costs during ENF interruption and ENF re-administration , respectively , with other parameter values held to their estimated values . When we varied the fitness cost from 5 to 50% we did not find any observable change in plasma viral load . This suggests that the fitness cost has a minor role in determining the total viral load . We next studied the effect of different initial proportions of the mutant virus at the time of ENF interruption ( Fig . 4b ) and ENF re-administration ( Fig . 5b ) . The initial proportion of ENF-resistant virus does not seem to have any effect on plasma viral load either . From the model we can calculate the steady state level of infected cells , , which given our assumption that ps = pr = p , is proportional to the total viral load , , i . e . , where an over-bar denotes a steady state value . As the resistant virus population decays to a low level during ENF interruption , the net effect of backward mutation on the steady state is negligible . Therefore , we neglect backward mutation and obtain the following expression for the steady state total viral load , : ( 6 ) Note that is independent of the fitness cost , α . Similarly , during ENF re-administration we neglect the forward mutation rate as the sensitive virus replication is largely inhibited , and obtain the steady state total viral load in the presence of ENF , , as ( 7 ) In this case , the total viral load depends upon the fitness cost , α . However , using our estimated parameters ( Table 3 ) , the second term on the right hand side is ∼20-fold smaller than the first term and hence the effect is negligible as seen in Fig . 5a . Therefore , the fitness cost again does not have any effect on setting the total viral load . We next studied the effect of target cells on the total viral load . We considered two approaches: one by changing the initial target cell level and another by changing the recruitment rate of target cells . We did not observe any effect of the initial target cell level on the total viral load during ENF interruption ( Fig . 4c ) or re-administration ( Fig . 5c ) . Marked differences in the level of plasma viral load is seen when the target cell recruitment rate , λ , changes while keeping all other parameters fixed , during both ENF interruption ( Fig . 4d ) and re-administration ( Fig . 5d ) . After early transient changes in viral load upon ENF interruption , the plasma viral load level remains relatively constant during the interruption with the level related to the target cell source rate λ . A similar result is found during ENF re-administration except that it takes longer to initially stabilize the viral load level during ENF re-administration than during ENF interruption . While we demonstrated the dependence of the viral load on λ ( Figs . 4d and 5d ) , the level of plasma viremia can also be seen by simulation to depend on p , c and δ . This is also supported by the analytical expressions ( 6 ) and ( 7 ) for the steady state level of total virus , which to a good approximation are equal to , pλ/ ( cδ ) , during ENF interruption and re-administration . The changes over time of the CD4 count , and of the proportion of uninfected cells , cell infected with sensitive virus , and cell infected with resistant virus are shown in Figs . 6a and 6c , respectively . After ENF re-administration , the proportion of uninfected cells increases , reaches a peak and then decays to a steady-state level higher than the level before ENF re-administration . In a study by Deeks et al . [11] on a larger cohort of individuals , the subjects received an ENF-based regimen ( the same as the one received by individuals in this study ) for 34 weeks ( approximately the same period as in our study ) followed by the interruption of ENF . During a screening period of 4 weeks just before the interruption began , they found a negligible change in CD4+ T cell counts ( mean change: 0 . 13 cells/µl/week ) suggesting that steady state was reached by the end of this long-term treatment . They also observed the steady state T cell level after a long period of ENF interruption . Below we calculate from our model the steady state level of uninfected CD4+ T cells to understand how the uninfected target cell level differs between long-term ENF interruption and long-term ENF re-administration . The steady state level of target cells during ENF interruption , TE , and ENF re-administration , , can be calculated from our model and are given by ( 8 ) ( 9 ) respectively . Before ENF is re-administered , εr = 0 , as no drug is present . After drug is given , εr>0 and Eq . ( 8 ) shows that the target cell level should increase . Furthermore , in addition to the efficacy of the drug against resistant virus , εr , the fitness cost , α , also contributes to the maintenance of a higher level of uninfected target cells during ENF re-administration . In fact , even if the drug is completely ineffective against resistant virus ( i . e . , εr = 0 ) , and the viral load is approximately equal during both ENF interruption and ENF re-administration as shown above , HIV infected patients with ENF re-administration will still have a higher uninfected target cell level due to the fitness loss of resistant virus ( i . e . , for α>0 ) . Above we showed that during re-administration the total viral load , and hence the total number of infected cells also stays approximately constant . Hence the CD4+ T cell count , which includes both uninfected and infected CD4+ T cells , is expected to increase with the increase in target cells . This is an important result as it shows that even though the resistant virus becomes dominant during ENF re-administration ( Fig . 3 ) , the CD4+ T cell count should increase , which represents an immunologic benefit to patients . Before ENF interruption , the ENF-resistant viral load is on average 100-fold higher than ENF-sensitive viral load . After ENF interruption the proportion of ENF-resistant virus decreases ( Fig . 2 ) and after several weeks ENF-sensitive virus becomes dominant . According to our simulations , the time it takes for ENF-sensitive virus to take over the viral population mainly depends upon the fitness cost ( Fig . 4e ) and the initial proportion of ENF-resistant virus ( Fig . 4f ) . To look at this more closely , we simplify the problem by neglecting mutation and by assuming the target cell level remains constant , i . e . we assume , the steady state target cell in the presence of drug ( i . e . before the interruption ) . This results in a system of two linear differential equations in Is and Ir . Solving these equations , we obtain the following expression for r ( t ) = Vr ( t ) /Vs ( t ) , the ratio of the two strains: ( 9 ) where r ( 0 ) ≈100 , i . e . resistant virus is approximately 100-fold more plentiful than sensitive virus . As time off therapy increases the level of resistant virus falls and r ( t ) decreases . When r ( t ) <1 , the sensitive virus is the dominant strain . The time , tθ , for the proportion of resistant virus to reach r ( tθ ) during ENF interruption is ( 10 ) As indicated by the above expression , and as seen in Figs . 4e and 4f , an increase in the fitness cost , α , causes the ENF-sensitive virus to be dominant sooner , while an increase in initial ENF-resistant virus proportion , r ( 0 ) , results in a longer time for the ENF-sensitive virus to be dominant . Varying the T-cell count at the time of interruption from 50 to 250 or 500 µl-1 or increasing the T cell source rate , λ , does not significantly impact the proportion of ENF-resistant virus ( Figs . 4g and 4h ) . We also studied the competition of the virus populations during ENF re-administration ( Figs . 5f–j ) . Following ENF re-administration ENF-resistant virus reemerges rapidly and attains the proportion in an approximate time ( obtained as in ENF interruption case above ) given by ( 11 ) For the parameters in Tables 1 and 2 , the virus population changes more rapidly during ENF re-administration than during ENF interruption , and the time for the virus population to become dominated by resistant virus , i . e . for r>1 , mainly depends upon the combined effect of fitness cost and efficacy of ENF against the ENF-resistant virus . If ENF is sufficiently effective against ENF-resistant virus or if the fitness cost is sufficiently high , the turnover is significantly delayed . In addition to ENF efficacy and the fitness cost , there appear to be nominal effects of the initial ENF-resistant virus proportion , and target cell generation rate on the turnover of the virus population during ENF re-administration ( Figs . 5g , i ) . Despite the resistance to ENF , re-administration of ENF might provide some benefits if ENF has partial activity against resistant virus . Using our model to study this , we found re-administration of ENF results in transient nominal viral suppression for about 2 weeks followed by a rapid rebound in plasma HIV-1 RNA level and then attainment of a steady state viral load higher than the initial viral load in about 7 weeks ( Fig . 5e ) . This shows that ENF re-administration is not effective in suppressing plasma viral load . However , our model simulations show that re-administration of ENF helps in maintaining a higher CD4+ T cell level ( Fig . 6a ) . After ENF re-administration , the CD4 count increases , reaches a peak and decays to a steady state level higher than the steady state level before the re-administration . While the CD4+ T cell count decreases by 15% in the absence of ENF , re-administration of ENF results in an increase of the CD4+ T cell count by 18% over the treatment period of 3 months ( Fig . 6b ) , which can be clinically significant . This gain of about 35% in the CD4 count due to ENF re-administration predicted by our model is consistent with a ∼36 . 8% increase in CD4 count ( from 95 cells/µl to 130 cells/µl ) during ENF-treatment observed in a study of 25 individuals [11] . According to our model , this increase is observed because during re-administration of ENF , ENF-sensitive virus is replaced by ENF-resistant virus that has less ability to infect CD4+ target cells ( Fig 6c ) . Therefore , there appears to be an immunological benefit , i . e . , achieving a higher CD4+ T cell count , in patients taking ENF , even though they might suffer virologic failure due to the emergence of resistance . The level of CD4+ T cells increases as fitness cost or/and the efficacy of ENF against ENF-resistant virus increases because an increase in fitness cost or/and efficacy further decreases the infectivity of resistant virus .
The impact of antiretroviral drug-resistance on viral load , CD4+ T cell counts and clinical outcomes is complex . Although the emergence of resistance to protease inhibitors and reverse transcriptase inhibitors clearly affects viral fitness ( as defined in vitro and in vivo ) [2]–[5] , [32] , its impact on viral load and CD4+ T cell counts is unclear . At comparable plasma viral loads , drug resistant HIV can be associated with more sustained CD4+ T cell gains and reduction of the risk of morbidity and mortality [2] , [4] , [5] , [32] than wild-type ( drug-sensitive ) HIV . To understand the mechanism for this apparent beneficial effect on immunologic and clinical outcomes independent of viremia , we use ENF resistance as a “probe” to explore the impact of fitness on viral and immunologic dynamics in vivo . Although the data linking ENF resistance to viral load , CD4 and clinical outcomes is limited , the preliminary data that does exist is consistent with the more extensive literature pertaining to protease inhibitor resistance . Specifically , despite the emergence of ENF-resistant mutations , CD4+ T cell counts have been observed to increase during therapy as the ENF resistant virus with less capacity to infect T cell replaces the ENF-sensitive virus . A large prospective study has recently been completed in which ENF was given as a “pulse” to determine if the expansion of ENF resistance positively affects CD4+ T cell counts . Preliminary data from 3 individuals has previously been published [13] . Given the richness of this data-set , we developed a mathematical model to study the benefits of ENF re-administration after interruption of therapy due to virological failure . Interruption of ENF after the emergence of ENF resistance results in a rapid decay of the resistant variant [13] . One of the key questions is what factors play a role in the waning of the ENF-resistant virus and in determining the time for the ENF-sensitive virus to become dominant . Similar questions arise for the period of ENF re-administration in which ENF-resistant virus rapidly increases and takes over the ENF-sensitive virus . Moreover , despite the rapid turnover of the virus population , the plasma HIV-1 RNA level remains unchanged during ENF interruption raising a question of what determines the plasma viral load . In this study , we took advantage of mathematical models to address these issues . Our model , which describes the dynamics of ENF-sensitive virus , ENF-resistant virus , target cells , cells infected by ENF-sensitive virus and cells infected by ENF-resistant virus , includes the fitness cost of ENF-resistant virus as well as forward and backward mutations . The model was used to fit data concerning the level of ENF-sensitive viruses , V38A ENF-resistant viruses and the CD4+ T cell count from HIV-1 infected patients , who underwent ENF interruption and subsequent re-administration while continuing to receive the other drugs in their regimen [13] . The data fitting during ENF interruption allowed us to estimate the forward mutation rate , the backward mutation rate and the fitness cost of the ENF-resistant virus along with the virus production rate , the infected cell death rate , the infection rate , the source rate of the target cells and the fraction of T cells that were target cells . Moreover , the data fitting allowed us to estimate the ENF efficacy against ENF-sensitive and ENF-resistant viruses during ENF re-administration . Our parameter estimates , model analysis and numerical simulations produced several interesting observations . First , the magnitude of the backward mutation rate of V38A is approximately the same as that of the forward mutation rate . This indicates that ongoing viral evolution might have some contributions to the loss of ENF-resistance during ENF interruption , supporting the results of phylogenetic analysis in Kitchen et al . [9] . However , we observe that backward mutation barely contributes to the loss of drug resistant viruses ( 26 virions/ml in the first week and 70 virions/ml in the first month post interruption ) , and thus is not sufficient to achieve the rapid waning of ENF-resistant viruses observed when therapy is interrupted . Outgrowth of the wild-type virus with a fitness advantage in the absence of drug is a more plausible explanation . Second , we found that the fitness cost of ENF-resistant mutations has a major role in the loss of ENF-resistant virus and the turnover of the virus population during ENF interruption . We estimated that the ENF-resistant virus is 17±3% less fit than the ENF-sensitive virus . This reduced fitness of V38A ENF-resistant virus agrees with the experimental finding of reduced fitness of the V38A mutant virus compared to wild-type virus in vitro [1] . Our simulations and analysis showed that the time needed for the sensitive virus to dominate the resistant virus during ENF interruption is mainly determined by the combined effect of fitness cost and the initial ENF-resistant virus proportion . Not surprisingly , the higher the fitness cost , the shorter the turnover time; and the higher the initial ENF-resistant virus proportion , the longer the turnover time ( Fig . 4e , f ) . During ENF re-administration , the ENF efficacy against ENF-resistant virus also plays an important role in determining the time for the resistant virus to outcompete sensitive virus . A higher ENF efficacy against ENF-resistant virus results in a longer turnover time ( Fig . 5j ) . Interestingly , there is a negligible effect of the target cell level on determining the turnover time of the virus population ( Figs . 4g , h and 5h , i ) . Third , we found that the fitness cost and the initial proportion of the ENF-resistant virus do not have any observable role in defining plasma HIV RNA levels . Neither does ENF efficacy during ENF re-administration contribute to setting plasma viral levels . The plasma viral RNA level is determined mainly by the target cell generation rate , λ , the virus production rate , p , the infected cell death rate , δ , and the virus clearance rate , c . A higher value of p or λ and/or a lower value of δ or c give rise to a higher plasma HIV RNA level . Our next observation is related to the advantage or disadvantage of re-administrating the drug following an interruption . Once the drug is re-administered , the resistant virus rapidly reemerges and becomes dominant over the sensitive virus . This indicates a strong selective pressure of the drug on the virus population . The turnover rate of the virus population is more rapid during the drug re-administration than during the drug interruption . This shows that the advantage of the drug-resistant virus over drug-sensitive virus during the drug re-administration is greater than the disadvantage of the drug-resistant virus over the drug-sensitive virus during the drug interruption . The rapid reemergence of the resistant virus also indicates the persistence of actively replicating resistant virus as suggested in [33] , [34] . Our parameter estimates indicate that ENF when re-administered is ∼29% effective against ENF-resistant virus and ∼66% effective against ENF-sensitive virus . This supports the antiviral activity against ENF-resistant viruses observed previously [11] . After re-administration of ENF , our model predicts a small transient suppression of viral load followed by a rebound to a higher plasma RNA level , consistent with the pattern shown by the data . This shows that the re-administration of ENF cannot suppress plasma virus for the long term . One of the most interesting results demonstrated by our model is that despite sustained high levels of viral load , re-administration of ENF helps in maintaining a significantly higher level of CD4+ T cells ( Figs . 5e and 6 ) . During ENF re-administration patients can achieve more than 35% higher CD4+ T cell count over the period of 3 months compared to the same patients during ENF interruption ( Fig . 6b ) . The CD4 count increase predicted by our model ( ∼35% ) is consistent with the CD4 count gain in a study on a larger cohort of individuals [11] in which the subjects ( with the same background regimen as in our study ) maintained a 36 . 8% higher CD4 during ENF treatment than during ENF interruption . This immunologic benefit of ENF occurs even in the presence of high-level ENF resistance , in agreement with the findings in some individuals harboring viruses with ENF-resistance mutations under long-term ENF therapy [35] . This outcome on administering ENF can be explained by the presence of resistant viruses with a reduced infection capacity ( Figs . 2 and 6c ) . The treatment alters the fitness of the virus by selecting the less fit resistant virus that helps in maintaining a higher CD4 count even though it is ineffective in suppressing the viral load . A similar effect has been seen in patients treated with reverse transcriptase and protease inhibitors such as proD30N , rtK65R and rtM184V [2]–[5] . The benefit of the drug is mediated by changes in both the fitness of the virus and the efficacy of the drug against resistant virus . The CD4+ T cell level during the drug re-administration increases as the efficacy of the drug against resistant virus increases or/and the fitness cost of resistant virus increases . There are several limitations of this study . The results are based on limited data from only three subjects . Moreover , there are fewer data points available during ENF re-administration , which might produce more uncertainty in the results derived from ENF re-administration . To gain more confidence in the results obtained here , extensive studies with more data are necessary . We have considered the V38A mutant virus as a representative of all ENF-resistant viruses . However , there are many other mutant viruses , which may possess different fitness costs and different mutation rates . It should be noted that in the experiment only the proportion of V38A was measured , and so there might be other mutant virus resistant to ENF that would have been included in the ENF-sensitive viral load . A detailed quasi-species model , as in Murray and Perelson [36] , may provide a better explanation of the phenomena and help in estimating a more accurate value of the T-cell benefit . However , such complex models require more detailed data sets in which the population levels of other members of the quasi-species are measured . Currently , such data is unavailable . In summary , we have used mathematical models to help explain the viral dynamic properties of drug sensitive and resistant viruses in the presence and the absence of the drug ENF . Our results show that even though forward and backward mutations occur during therapy interruption , the primary factor leading to the loss of resistant virus during therapy interruption is the fitness cost of the resistant virus . In the presence of drug , the efficacy of drug against resistant virus is also one of the main factors determining dominance of the drug resistant virus in the population . More importantly , even though the drug is ineffective in suppressing plasma viral load due to the presence of resistant virus , our results support the concept that continued therapy may have a residual immunologic benefit by preserving peripheral blood CD4+ T cell levels . | The impact of antiretroviral drug-resistance on viral load , CD4+ T cells , and clinical outcomes is complex . We used mathematical models to evaluate the benefits of HIV drug therapy in the presence of drug-resistant virus . As an example , we considered resistance to enfuvirtide , the first FDA-approved fusion inhibitor . If viral load increases on drug therapy due to drug resistance , therapy with this drug may be stopped . We found that the drug resistant virus is less fit than the drug-sensitive virus in the absence of drug , and this fitness disadvantage causes the loss of drug-resistant virus during drug interruption . After the drug-sensitive virus replaces resistant virus , enfuvirtide therapy was re-administered . Analyzing the resulting viral kinetics , we demonstrate that despite the inability of the re-administered drug to suppress viral load because of the continued presence of drug resistant virus , therapy still provides benefit to the patient by preserving or increasing peripheral blood CD4+ T cell levels . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"mathematics",
"virology/immunodeficiency",
"viruses",
"immunology/immune",
"response",
"virology/antivirals,",
"including",
"modes",
"of",
"action",
"and",
"resistance",
"computational",
"biology/evolutionary",
"modeling",
"infectious",
"diseases/hiv",
"infection",
"and",
"aids",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance",
"virology/host",
"antiviral",
"responses"
] | 2010 | Treatment-Mediated Alterations in HIV Fitness Preserve CD4+ T Cell Counts but Have Minimal Effects on Viral Load |
Many infectious diseases are not maintained in a state of equilibrium but exhibit significant fluctuations in prevalence over time . For pathogens that consist of multiple antigenic types or strains , such as influenza , malaria or dengue , these fluctuations often take on the form of regular or irregular epidemic outbreaks in addition to oscillatory prevalence levels of the constituent strains . To explain the observed temporal dynamics and structuring in pathogen populations , epidemiological multi-strain models have commonly evoked strong immune interactions between strains as the predominant driver . Here , with specific reference to dengue , we show how spatially explicit , multi-strain systems can exhibit all of the described epidemiological dynamics even in the absence of immune competition . Instead , amplification of natural stochastic differences in disease transmission , can give rise to persistent oscillations comprising semi-regular epidemic outbreaks and sequential dominance of dengue's four serotypes . Not only can this mechanism explain observed differences in serotype and disease distributions between neighbouring geographical areas , it also has important implications for inferring the nature and epidemiological consequences of immune mediated competition in multi-strain pathogen systems .
Mathematical models based upon the various derivatives of the classic susceptible-infected-recovered ( SIR ) framework have greatly improved our understanding of the transmission and population dynamics of many important pathogens [1] , [2] . Common to this class of models is their propensity to exhibit damped oscillations around an approaching equilibrium where the rate of new infections equals the loss from the infectious pool due to recovery . In reality , however , many infectious diseases will not remain in this state of equilibrium but instead exhibit persistent oscillations , ranging from seasonal increases in incidence rates to multi-annual epidemic outbreaks . Measles and influenza are just two examples of pathogens for which incidence levels can vary by orders of magnitude within a single year [3] , [4] . External forces are often incorporated into models to reflect a seasonal increase or decrease in the number of infectious contacts or vector densities , for example , which move the system's dynamics away from its natural equilibrium into a regime characterised by periodic or chaotic oscillations , akin to those observed in nature [5] , [6] . For antigenically diverse pathogens , periods of high or low infection rates or the timing by which one dominant antigenic strain is replaced by another strain , are often out of sync with those dictated by the external forces , however . Theoretical studies have therefore concentrated on biological or pathogen-intrinsic factors instead . Immunological interactions between the constituent strains in the form of cross-immunity or cross-enhancement have been repeatedly highlighted as some of the most important determinants of the epidemiological dynamics of multi-strain pathogens . Under this scenario , enhanced competition for susceptible hosts can offer a temporary selective advantage to a particular strain or subset of strains , causing their amplification and subsequent decline . This process of immune-mediated selection has been proposed to underlie the population biology of a variety of important pathogens , including the influenza virus [7] , Plasmodium falciparum [8] , Vibrio cholerae [9] , dengue virus [10]–[12] , respiratory syncytial virus [13] and rotavirus [14] . Whereas many deterministic multi-strain models rely on the presence of immune interactions to destabilize the system , existing natural variabilities or stochasticities in the interactions between the relevant players have also been shown sufficient to generate regular or chaotic oscillations in single-strain and ecological predator-prey systems [15]–[19] . Furthermore , demographic stochasticities have been found to play an important role when relaxing the assumption of homogeneous mixing and when taking spatial ecological aspects into consideration . In this scenario , spatial heterogeneities due to host-population structure or local ecological variations can create short-lived spatial refuges [20] and significantly affect pathogen persistence [21]–[23] . The consideration of spatio-temporal variations is of particular importance for vector-borne pathogens , where the underlying drivers of the observed epidemiologies may be confounded by substantial heterogeneities in host and vector densities through space and time , as in the case of the dengue virus ( DENV ) . DENV's population comprises four antigenically related viral groups , or serotypes ( DENV1-4 ) , that are the cause of clinically indistinguishable illnesses in humans . Different immunological interactions in the form of antibody-dependent enhancement ( ADE ) or temporary and/or partial cross-immunity have been independently proposed as the driving forces behind the virus' complex epidemiology that comprises multi-annual epidemic outbreaks and sequential replacement of dominant serotypes [10]–[12] , [24]–[27] . Although these differential equation models qualitatively capture dengue's epidemiological dynamics , they do not consider the natural variability in disease transmission across time and space and thus cannot account for observed differences in incidence and serotype distribution within endemic regions ( see e . g . [28] ) . Meta-population and agent-based models allow a more explicit description and investigation of demographic and spatial , ecological stochasticities [29]–[31] , and thus provide a natural alternative to study these host-pathogen systems . Here , using dengue as a case study , we show that heterogeneities and stochasticities underlying host-vector contacts can give rise to persistent oscillations in multi-strain pathogen systems , even in the absence of immune competition between antigenic types . We demonstrate that viral persistence is significantly enhanced through the temporal generation of susceptibility pockets within the population , leading to highly heterogeneous distributions in disease and serotype prevalence that can explain observed geographic differences in dengue endemic regions . Complimentary to immune interaction , host demographic factors and vector ecologies thus emerge as important drivers of dengue's epidemiological dynamics .
Using a stochastic , agent-based framework we first analysed the dynamics of a host-pathogen system comprising 4 co-circulating antigenic types under the assumption of homogeneous mixing within the population . Contrasting the predictions of deterministic multi-strain models , in which the dynamics inevitably converge towards a stable equilibrium in the absence of strong immune competition , the system exhibited sustained oscillations in the total number of infections and out-of-phase oscillations in strain prevalence , as illustrated in Figure 1A . In agreement with previously studied stochastic single-strain systems [17] , [18] , these dynamics are driven by the amplification of stochastic effects at the individual level , which keep each strain in a transient regime rather than approaching the expected deterministic equilibrium . At the same time , short-term stochastic differences in each strain's transmission success accumulate in time and start to generate significant asymmetries in the immunity profile within the host population , which then leads to the desynchronisation between strains . This extreme case of minimal strain interaction more resembles a system of four co-circulating but unrelated pathogens . Not surprisingly , therefore , we found that the periods of oscillations in total incidence and strain prevalence were essentially the same , determined by the parameters relating to pathogen transmission and host demography ( Figure S1A in electronic supplementary material ) . In the case of dengue , however , differences between the inter-epidemic period and average cycle length in strain prevalence have been well documented [32] . We therefore extended the model to incorporate mosquito vectors and used dengue-relevant epidemiological parameters values ( see Table 1 ) to investigate the effect of stochastic amplifications on the virus's epidemiological dynamics and inter-epidemic periods . The resulting qualitative dynamics in terms of persistent oscillations in incidence and serotype prevalence appeared invariant to the addition of mosquito vectors but showed a significant increase in average disease prevalence ( Figure 1B ) . This increase was mainly caused by a reduction in the risk of stochastic extinction due to the inclusion of viral incubation periods as well as the increase in the basic reproductive number from in the directly transmission model to in the vector model . Importantly , also , we started to observe a divergence between the epidemic and serotype periodicities ( figure S1B in electronic supplementary material ) and also found epidemic peaks more likely to be comprised of multiple serotypes . Further including seasonality through annual variations in mosquito densities ( see Materials and Methods ) resulted in dengue-like epidemiological behaviour with a distinct seasonal signature , strong multi-annual periodicities in incidence and fluctuating distribution in serotype prevalence ( Figure 1C ) . This behaviour was further accompanied by a considerable increase in peak incidence levels and more pronounced epidemic troughs , which could partly be explained by an increase in the average to but also by the strong synchronizing effect of vector seasonality on serotype dynamics . We hypothesized that the occurrence of large epidemic outbreaks ( as seen in Figure 1C ) was partly facilitated by our assumption of homogeneous mixing , which facilitates rapid disease transmission throughout the whole population . We thus restructured our model into a meta-population formulation by subdividing the human and mosquito populations into sets of spatially arranged communities ( see Materials and Methods ) and examined the effect of spatial segregation between hosts on the epidemiological dynamics of this multi-strain system . Within this set-up we assumed that individuals get infected predominantly by mosquitoes of their own and surrounding communities and with a small probability , , by mosquitoes from distant communities through ( temporal ) human movements , or visits , to these communities . We argued that because of the limited flight range of Aedes mosquitoes , human movement is more important for long-distance transmission [33] and therefore assumed to be independent of geographic distance , contrasting continuous and distance-dependent dispersal kernels often employed in spatial ecological models ( but also see [33] and [34] for alternative realisations ) . With the addition of this spatial component the system exhibited more defined seasonal dynamics as well as a lower variability in the epidemic behaviour ( Figure 2A ) , with the overall temporal dynamics closely resembling epidemiological time series from dengue endemic regions with the characteristic multi-annual cycles in epidemic outbreaks and sequential serotype dominance ( Figure 2B showing data from Puerto Rico , and Figure S2 showing data from Thailand , Mexico and Vietnam ) . The periodicity in serotype prevalence also increased and settled onto a 8–9 year cycle ( figure S1C in electronic supplementary material ) , which is in line with the suggested periodicity derived from epidemiological time series [32] ( also apparent from Figure 2B ) and dengue's phylodynamics in Thailand [25] . In agreement with previous studies on meta-populations , the spatial segregation between hosts enhanced global disease persistence ( compare e . g . baseline incidence in Figures 1C and 2A ) but at the same time facilitated local extinction [17] , [22] , [35] . This created a spatially heterogeneous susceptibility landscape within the population ( Figure 3A , left panel ) upon which individual serotypes were sequentially selected and amplified , frequently exhibiting locally propagating waves ( Figure 3A , middle panel ) . This heterogeneity in susceptibility and disease prevalence also affected the timing between heterologous infections , here referred to as heterologous exposure period , or HEP , leading to a highly variable , spatio-temporal distribution in HEP across the population ( Figures 3A , right panel ) . We argued that these self-emergent phenomena could explain some of the spatial epidemiological differences in dengue-endemic countries , where markedly different distributions in serotype prevalence can be observed between geographically neighbouring regions or between urban and suburban districts ( Figure 3B ) . Importantly , these differences would be masked if only aggregate data were being considered . The spatio-temporal dynamics illustrated in Figures 2 and 3 clearly demonstrate the importance of human and vector demographic heterogeneities for the population dynamics of dengue [36]–[38] , which in our case are the result of stochasticities and spatial restrictions in disease transmission . To further address the effects of spatial structuring and host mobility on our simulated epidemiologies , we quantified key epidemiological properties , such as mean prevalence ( averaged over humans and vectors ) , extinction risk and serological age-profiles in the population , in response to changes in these parameters . Increasing spatial structuring , and thereby decreasing the size of each sub-population , reduced the variability in total annual outbreak size and local serotype co-circulation ( Figure 4A ) , here defined as the percent time where multiple serotypes are present in a given patch . Although the overall force of infection was not affected by the increase in population structure , as evidenced by the constant average ages of primary or secondary infections ( right panel , Figure 4A ) , total infection prevalence increased as a result of a reduction in the risk of serotype extinction . This indicates that spatial segregation between hosts greatly reduces the propensity for large-scale , population-encompassing outbreaks by restricting a pathogen's access to the susceptible pool , which is also in agreement with previous studies in the context of disease transmission through complex or/and heterogeneous networks [17] , [35] , [39] . In contrast to population structuring , increasing the probability of transmission between hosts of distant communities , , as a proxy for daily human mobility , had a more homogenizing effect and led to an increase in local viral co-circulation ( Figure 4B ) . More frequent and brief localized outbreaks could be observed , resulting in increased epidemic variability . However , this increase in outbreak size variability did not equate to an increase in mean infection prevalence levels because of localised extinction risk . In other words , the heterogeneous distribution of herd-immunity [40] to individual serotypes ( as illustrated in Figures 3A–C ) within the spatially structured population counteracts the occurrence of population-wide outbreaks that are otherwise expected from the synchronizing effect of higher mobility or dispersal rates [22] , [41] . We next analysed the degree of epidemic synchrony , or coherence , between communities under variations in host mobility . As mentioned above , the rate at which human hosts acquire infections in geographically distant communities , , has a significant effect on viral co-circulation and hence the susceptibility/immunity landscape in the population . This is further illustrated in Figure 5A for two different values of , showing a transition to a less variable but a more patchy distribution of susceptibility to DENV1 with an increasing rate of long-distance transmission events . When disease transmission was predominantly local ( ) , as expected , we observed that spatial synchrony was dependent on spatial distance ( blue line in Figure 5B , and Figure S3 ) . In contrast , as a result of a reduction in locally acquired infection with increasing , epidemic synchrony between neighbouring communities was disrupted , causing an overall low but homogeneous spatial coherence across the population ( , red line in Figure 5B ) . These results are in general agreement with a growing body of studies on dengue's epidemic , spatial scale . For instance , cases appear to cluster at the level of households or neighborhoods [42] , whereas epidemics across larger regions present strong spatial dependence [43] and appear to follow a power-law distribution , implying that outbreaks are predominantly driven by a limited set of spatial clusters [44] . It should be noted that migration , or dispersal , has previously been shown to increase synchronization between populations within different spatially explicit model frameworks [22] , [41] . However , this is not necessarily the case when local demographic stochasticity is considered [19] , [34] . For instance , within a spatially extended meta-population model , Blasius et al . demonstrated that phase-locking amongst patches is easily achieved by dispersal rates , while peak and trough abundances in each patch can remain chaotic and variably uncorrelated [34] . The same effect is observed in the local dynamics of the patches within our framework ( see Figure S3A and S3B for examples ) . It is thus not surprising that we only find low-to-intermediate coherence across space , even between close-range patches ( Figure 5B ) . Although dengue-characteristic dynamics could be obtained even in the absence of immune interaction between the virus's four serotypes , temporary ( serotype-transcending ) cross-immunity and ADE have previously been proposed as important drivers of dengue epidemiology , and we therefore analysed their effects within this spatial setting . As demonstrated in Figures 3A and 4 ( right panels ) , the time required for an individual to acquire a secondary , heterologous infection ( HEP ) was on average in the order of 4–5 years . While this is in general agreement with a previous study from Thailand [45] , and might also explain the peak in older children in the age-profiles of dengue haemorrhagic fever ( DHF ) in endemic regions [32] , it is much higher than the reported 3–9 months period of serotype-transcending immunity following a primary infection [46] . Consequently , and contrary to previous predictions based on continuous and homogeneous mixing models , the inclusion of temporary cross-immunity did not have a significant effect on the simulated , qualitative epidemiologies within our stochastic and spatially explicit framework . When quantifying key epidemiological characteristics under changes to the duration of temporary immunity , we found that only once this period increased beyond 12 months there was a small , negative effect on infection prevalence and epidemic variability ( Figure 6A ) . On the other hand , even short periods of transcending immunity had a significant effect on both serotype extinction risk and periodicity , suggesting its regulatory role on how the different viruses can explore the susceptibility ( spatial ) landscapes . In contrast to temporary cross-immunity , immune enhancement through the process of ADE had a more noticeable and anticipatory effect . That is , increasing the probability of transmission through the enhancement of secondary , heterologous infections led to an increase in disease prevalence along with an increase in epidemic variability , serotype co-circulation and viral extinction risk ( Figure 6B ) , which is broadly in line with previous studies [10] , [12] , [24] . The increase in prevalence did not significantly affect the average age at which individuals experience their first infection , however , whereas the age of secondary infection showed a more dramatic reduction . In fact , due to the combined effect of elevated serotype co-circulation and an increase in the susceptibility to secondary infections through ADE , even moderate levels of enhancement caused the HEP to go below the average time at which individuals experience their first infection . Hence , in the presence of population structure , ADE , and especially its proposed susceptibility enhancing manifestation , may induce a signature in the epidemiological age-profiles of the population that is characterised by a longer period for first infection than the time required for heterologous exposure , which has indeed been observed in studies of clinical infections in dengue endemic areas [32] , [45] , [47] . Finally we turned our attention to the effect of changes to disease transmission within this spatial setting . Differences in the estimates of a pathogen's transmission potential , or , can be attributed to a multitude of factors , and in the case of dengue , this has resulted in a wide spectrum of estimations , ranging in values from close to 1 to bigger than 20 ( see Table S1 for an overview ) . To quantify the effects of changes to viral transmission , and in general , we analysed the model behaviour , in the absence of immune interactions , under variations in key parameters related to dengue's basic reproductive number , whose derivation within this framework can be found in the Materials and Methods section . Specifically , we investigated the effects of through variations in the probability of transmission per mosquito bite , using both symmetric and asymmetric transmission probabilities , viral incubation periods ( both intrinsic and extrinsic ) and mosquito vector density . The results were mostly in accordance with those expected from increasing parameters related to in equivalent continuous multi-strain models and can be found in Figure S4 in Supplementary Material; here , we only highlight two of the more important findings . First , assuming symmetric transmission probabilities between host and vectors we found that the viral extinction risk was not monotonously associated with changes in transmissibility and was in fact minimized for ( Figure S4A ) , in range with estimations using age-stratified indexation of sero-conversion rates [48] . Notably , this was also the range in which the age profiles of infection where more similar to what is commonly described for endemic regions in South East Asia [32] , [45] , [49] , [50] . Secondly , our model confirmed that changes in the extrinsic incubation period had a much more dramatic effect on dengue epidemiology than incubation periods in the human host ( Figure S4D and S4E ) , as this directly affects the duration of infectiousness in the mosquito . Crucially , this re-emphasizes the notion that seasonally driven temperature , and its effect on viral incubation , is as important a determinant of dengue epidemiology , as is vector density [51] .
Understanding the evolutionary forces that shape the spatio-temporal patterns of pathogen populations is essential for disease control and public health planning . Important new insights into the population dynamics of host-pathogen systems have been gained by the application of deterministic mathematical models to the study of many important infectious diseases [1] . Nevertheless , stochastic and discrete events significantly influence the real world counterpart of such systems and their explicit incorporation can provide alternative frameworks in which to examine major determinants of the observed epidemiologies [17] , [39] , [52] , [53] . In this context , demographic stochasticity has been suggested to be an important driver for population oscillations in single-strain epidemiological systems [5] , [6] , [17] , [18] . Here we advanced upon previous findings by studying the dynamical behavior of dengue's four antigenic types within a stochastic and spatially explicit framework . Dengue's epidemiological dynamics have been the focus of extensive theoretical research that often concentrated on the immunological interactions between its four serotypes [10]–[12] , [24]–[26] . Protective and infection-enhancing effects of cross-reacting antibodies have been well documented both in vivo and in vitro [46] , [54] , [55] . Less clear , however , is their contributing effect to disease transmission and general epidemiology . For example , although a short period of 3 to 9 months of serotype-transcending immunity following a primary infection has been demonstrated by direct experiment , the average time between consecutive , heterologous infections is often found to be an order of magnitude higher [45] . Equally , despite the reported increase in within-host viral replication through antibody-dependent enhancement of secondary , heterologous infections and observed correlations between disease severity and previous exposure , it is currently not known if and how much this increase in viral load contributes to total dengue transmission , especially when taking into consideration that severe , clinical cases may constitute only a small fraction of all dengue infections [32] , [45] , and that viraemia appears to peak earlier but also clears faster during secondary , heterologous infections [56] . In contrast to previous model predictions , our results could not ascertain a decisive role of either temporary cross-immunity or ADE in driving the complex epidemiological dynamics of dengue . That is , while our findings do not question the pathological or clinical significance of immune interactions per se , they suggest that the strength of within-host serotype interactions , and therefore the consequences of acquired immunity , are unlikely to be the sole drivers of the complex epidemiological dynamics of dengue . Crucially , the results herein presented further suggest that such cross-immunological reactions , at least within biological reasonable ranges , would not cause significant spatio-temporal signatures that could allow the inference of their presence to be unambiguously resolved from studying epidemiological time series alone . More detailed data , for example from human infectivity studies that relate infection history with clinical outcome and infection/transmission probabilities , are essential to close the gap in our understanding of the full transmission potential of dengue . Furthermore , to better understand the importance of host demographic factors and spatial ecology highlighted in this work , a phylodynamics approach could be considered in which the spatio-temporal evolution of dengue genotypes is simulated and compared to available data from different settings across the endemicity spectrum . Dengue's recent molecular evolution is characterized by strong intra-serotype purifying selection with no clear trend for continuous antigenic change . As DENV has evolved to replicate efficiently in both the vertebrate and arthropod hosts , it is thought to express a compromise genome in which most structural mutations are expected to be deleterious and selectively removed from the population [57] . On the other hand , strong ecological bottlenecks and inter-serotype competition can severely hamper the emergence of viral mutants even if they express advantageous phenotypes [58] . The cyclical replacement of dengue's four serotypes is therefore not expected to be driven by the same inter-strain selective forces that have ( reportedly ) shaped the phylodynamics of antigenically rapidly evolving pathogens , such as influenza A [59] , for example . It instead argues for a critical role of demographic and ecological stochasticities underlying both dengue's epidemiology and molecular evolution . The strong impact of host population structures and mobility highlighted in this work also corroborates the hypothesis that DENV's ( re- ) emergence and world-wide success is mainly due to current demographic and ecological trends rather than viral adaptation [39] , [57] . To understand dengue's epidemiology in the long-term , it is therefore crucial to establish how these meta-population disease dynamics correlate with evolutionary constraints and respective selective signatures . Importantly , the discrete nature of our framework and its meta-population formulation readily allow to explore more realistic population structures , including heterogeneities in ( host and vector ) population sizes and/or connectivity between sub-populations , for example by means of complex network structures , and to simulate viral evolution in time and space within these frameworks . Our model thus presents itself as a good starting point for a more thorough investigation of DENV's phylodynamics [60] . Accounting for community-specific vector control and drug intervention policies is equally possible within this meta-population formulation and constitutes another important extension for future studies on the control of vector-borne diseases . For example , candidate vaccines against dengue that are in advanced stages of clinical trials might require a prime-boost protocol running over a period of up to 12 months , which has been indicated as a potential concern due to the risk of severe disease during the time when antibody-levels are at sub-neutralizing titers [61] , [62] . By reproducing the spatial heterogeneity in disease prevalence and serotype distribution we found the timing between consecutive , heterologous infections to be highly variable in space . Our observations thus reassert that spatially explicit epidemiological frameworks , as the one presented here , are essential for assessing the risks and efficacies of vaccine introduction strategies against dengue [62] . In summary , the results presented here have highlighted the importance of considering spatial segregation between individual hosts and vectors and stochasticities in disease transmission for understanding the epidemiology of dengue and other related pathogens . Previous theoretical studies have demonstrated that immune interactions can significantly influence the population dynamics of multi-strain pathogen systems . The inclusion of host and vector ecologies adds to this understanding and provides complimentary hypotheses about the underlying causes for the oscillatory nature in incidence and serotype distributions that commonly characterize their complex epidemiologies .
To study the stochastic dynamics of a multi-strain pathogen we used an individual-based model , realised as a discrete-time , random process with finite state-space ( Markov chain ) , is which a state refers to the host's epidemiological profile , such as infection status and immune history . Demographic , biological and ecological stochasticities were derived from the probabilistic nature of state transitions , e . g . in the probability that the bite of an infectious mosquito leads to an infection . The size of the host population was kept constant with deaths being replaced by newborns . We assumed an age-dependent risk of mortality for both humans and mosquitoes , described by the continuous Weibull distribution:where is the host age , and and are the shape and scale parameters , respectively . Spatial structure was added by subdividing the host population into a spatially organized set of communities , forming a squared and non-wrapping lattice wherein each community had neighbors ( ) . Individuals were assumed to mix homogeneously within each , such that each mosquito bite took place between a vector and human chosen randomly from this community . We further assumed that mosquitoes disperse only locally , implying that each vector in community will only bite human individuals belonging to the set of communities , i . e . within and its neighboring communities . Long distance transmission was considered through human movement by allowing mosquitoes to bite humans of randomly chosen , distant patches with probability ( the probability of human hosts temporarily ‘visiting’ these communities ) , which reduces the local transmission rate to . This formulation differs from the ones considered in other meta-population studies , which often assume a constant ( continuous ) , and possibly distance-dependent migration or dispersal term between any two patches or communities . | The population dynamics of multi-strain pathogens are often characterized by persistent and irregular fluctuations in disease incidence and strain prevalence levels over time . Previous theoretical approaches have often evoked strong immunological interactions between individual strains , such as cross-immunity , in order to explain these complex epidemiologies; however , spatial segregation between hosts and stochastic heterogeneities in transmission success are rarely considered in these studies . Here , with specific reference to dengue , we show that the stochasticities underlying disease transmission within a spatially explicit , agent-based model can give rise to multi-annual epidemic outbreaks and fluctuating pathogen population structures - even in the absence of immune competition . In contrast to previous modeling studies , which have resulted in ambiguous predictions about the exact nature and strength of interactions between dengue's four serotypes , our results present a parsimonious , demographic mechanism , that highlights the importance of spatial ecology for understanding and interpreting the epidemiological dynamics of dengue and other multi-strain pathogen systems . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Natural, Persistent Oscillations in a Spatial Multi-Strain Disease System with Application to Dengue |
Altered transcriptional programs are a hallmark of diseases , yet how these are established is still ill-defined . PBX1 is a TALE homeodomain protein involved in the development of different types of cancers . The estrogen receptor alpha ( ERα ) is central to the development of two-thirds of all breast cancers . Here we demonstrate that PBX1 acts as a pioneer factor and is essential for the ERα-mediated transcriptional response driving aggressive tumors in breast cancer . Indeed , PBX1 expression correlates with ERα in primary breast tumors , and breast cancer cells depleted of PBX1 no longer proliferate following estrogen stimulation . Profiling PBX1 recruitment and chromatin accessibility across the genome of breast cancer cells through ChIP-seq and FAIRE-seq reveals that PBX1 is loaded and promotes chromatin openness at specific genomic locations through its capacity to read specific epigenetic signatures . Accordingly , PBX1 guides ERα recruitment to a specific subset of sites . Expression profiling studies demonstrate that PBX1 controls over 70% of the estrogen response . More importantly , the PBX1-dependent transcriptional program is associated with poor-outcome in breast cancer patients . Correspondingly , PBX1 expression alone can discriminate a priori the outcome in ERα-positive breast cancer patients . These features are markedly different from the previously characterized ERα-associated pioneer factor FoxA1 . Indeed , PBX1 is the only pioneer factor identified to date that discriminates outcome such as metastasis in ERα-positive breast cancer patients . Together our results reveal that PBX1 is a novel pioneer factor defining aggressive ERα-positive breast tumors , as it guides ERα genomic activity to unique genomic regions promoting a transcriptional program favorable to breast cancer progression .
The implementation of transcriptional programs is central to the commitment of pluripotent cells occurring throughout development [1] , [2] . Likewise , diseases commonly arise from altered transcriptional programs . This requires active reprogramming characterized by chromatin remodeling and altered epigenetic signature at lineage-specific functional genomic elements [2]–[5] . The estrogen receptor alpha ( ERα ) is a nuclear receptor central to breast cancer development . Upon estrogen stimulation , it binds at thousand of genomic loci defining its cistrome to promote a pro-proliferative transcriptional program [6]–[9] . Its genomic actions are in part dependent on the pioneer factor FoxA1 [6] , [7] , [8] , [10] , [11] , [12] , [13] , [14] . Pioneer factors are an emerging class of DNA binding proteins . They play a central role in defining transcriptional programs as they can integrate and remodel condensed chromatin rendering it competent for transcription factor binding [6] , [15] , [16] , [17] , [18] , [19] . Their recruitment to the chromatin is sequence specific and can be facilitated by an epigenetic signature dependent on histone methylation [6] , [20] . PBX1 ( Pre-B-cell leukemia homeobox 1 ) is a member of the Three Amino acid Loop Extension ( TALE ) -class homeodomain family required for diverse developmental processes including hematopoiesis [21] , skeleton patterning [22] , pancreas [23] , and urogenital systems organogenesis [24] , [25] . While it is best known as an oncoprotein when fused to E2A in pre-B-cell leukemia [26] , it also contributes to prostate , ovarian and esophageal cancer [27]–[30] . It is also highly expressed in breast cancer [31] . PBX1 is a cofactor for homeobox ( HOX ) transcription factors as it increases their affinity and specificity to chromatin [32] , [33] . However , recent interactome studies have revealed that 12% of PBX1 putative partners are non-homeodomain transcription factors [34] , [35] . In agreement , PBX1 modulates the transcriptional activity of nuclear receptors such as the thyroid and glucocorticoid receptors and was recently proposed to act as a pioneer factor for the bHLH factor MyoD [36]–[38] . However , the contribution of PBX1 to chromatin structure and epigenetic signatures regulating transcription in ERα-positive breast cancer cells is unknown . In the present study , we have investigated the pioneer function of PBX1 towards ERα genomic activity in breast cancer .
Condensed chromatin constitutes a barrier for the recruitment of transcription factors to the DNA . FoxA1 binding at specific genomic regions allows for chromatin remodeling favorable to ERα recruitment at a subset of its cistrome [6] , [8] , [13] , [19] , [39] . However , ERα is recruited to thousands of FoxA1-independent sites across the genome [6] . To identify candidate pioneer factors guiding ERα recruitment to the chromatin at these sites we performed seeded motif analyzes using the Cistrome-web application ( http://cistrome . dfci . harvard . edu/ap/ ) . This revealed that over 85% of the ERα cistrome harbors the DNA motif recognized by PBX1 ( Figure 1A and 1B ) . Noteworthy , the presence of the PBX1 motif in ERα binding sites was significantly different from another similar size cistrome ( androgen receptor ( AR ) cistrome from LNCaP cells , p<1e-99 ) ( Figure S1A ) . Analyzing expression profiles from the NCI60 panel of cancer cells compiled on bioGPS ( http://biogps . gnf . org ) [40] , [41] reveals that PBX1 is significantly co-expressed with ERα ( co-expression coefficient 0 . 7784 using probe 205253_at ) ( Table S1 ) . This was also revealed by comparing PBX1 mRNA expression across 47 distinct ERα-positive and negative breast cancer cells ( p = 8 . 98e-7 ) ( Figure 1C ) . ERα mRNA expression was also significantly correlated with ERα-histological status of breast cancer cells ( p = 1 . 71e-8 ) ( Figure 1C ) . These results are further supported by RT-qPCR , immunofluorescence and western blot analyzes in ERα-positive MCF7 and ERα-negative MDA-MB231 breast cancer cells demonstrating co-expression of ERα and PBX1 at the mRNA and protein level ( Figure 1D ) . PBX1 is one of four PBX family members [33] . RT-qPCR against other PBX1 genes demonstrates that PBX1 is the predominant family member expressed in ERα-positive breast cancer cells ( Figure S1B ) . Analyses of 41 independent breast cancer expression profile studies , such as van de Vijver study , demonstrate that PBX1 and ERα are also co-expressed in primary breast tumors ( p = 2 . 72e-13 for the van De Vijver study and p≤1e-4 for all other studies ) ( Figure 1E ) [42] . The correlation between ERα mRNA expression and ERα-histological status is also reported for the van de Vijver study ( p = 2 . 27e-74 ) ( Figure 1E ) . To address the functional relation between PBX1 and ERα we assessed the role of PBX1 on estrogen-induced growth in the ERα-positive MCF7 breast cancer cells . PBX1 mRNA and protein levels were significantly depleted ( ∼70% ) in MCF7 breast cancer cells transfected with one of two independent siRNA against PBX1 ( Figure 2A and 2B ) . In agreement with a role for PBX1 in breast cancer [27] , PBX1 depletion completely prevented the estrogen-induced proliferation of MCF7 breast cancer cells ( Figure 2C and S2A-B ) . Importantly , PBX1 depletion in MCF7 breast cancer cells did not affect ERα or FoxA1 expression both at the mRNA and protein level ( Figure 2D ) . Overall these results support a functional role for PBX1 in mediating the response to estrogen in ERα-positive breast cancer . Estrogen signaling involves ERα activation and subsequent recruitment to the chromatin . Pioneer factors can therefore be identified through their role at the chromatin prior to estrogen treatment . Immunofluorescence assays against PBX1 in MCF7 breast cancer cells deprived of estrogen demonstrate its localization to the nucleus ( Figure 3A ) . While PBX1 and FoxA1 have a similar nuclear distribution , confocal immunofluorescence analysis against FoxA1 reveals that it only partially overlaps with PBX1 ( Figure 3A and Figure S3A and S3B ) . To demonstrate that PBX1 occupies the chromatin in MCF7 breast cancer cells we performed a ChIP-seq assay in cells maintained in full media . This identified 24254 high-confidence PBX1 sites ( p≤1e-5 ) predominantly localized a distant regulatory elements ( Figure 3B and Figures S4A and S4B , S5 , S6 , S7 , S8 ) . Directed ChIP-qPCR assays on 37 randomly selected PBX1 bound sites identified by ChIP-seq demonstrates that it is loaded to the chromatin in absence of estrogen ( Figure S4B ) . Approximately 50% of the estrogen-induced ERα cistrome overlaps with PBX1 bound sites ( Figure 3B ) . A significant overlap between ERα and PBX1 is also observed for all publically available ERα cistromes ( Figure S9 ) [6] , [7] , [9] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] . FoxA1 is loaded to the majority of these sites ( Figure 3B ) . In fact , ChIP-reChIP assays in MCF7 breast cancer cells maintained in estrogen free media demonstrates that both pioneer factors co-localize on the chromatin at shared sites ( Figure S11 ) . Importantly , over 37% of the FoxA1-independent ERα binding sites overlap with PBX1 ( Figure 3B ) . Expression profile analysis in MCF7 breast cancer depleted of PBX1 reveals that a 71% of estrogen-induced target genes are dependent on PBX1 ( Table S2 and Figure S12 ) . Importantly , the estrogen signature identified by this expression profile was highly enriched for genes defining ERα-positive primary breast tumors ( p = 5 . 75e-10 ) [52] . To assess the relation between genome-wide binding and expression profiles we cross-examined the estrogen responsive gene lists ( all estrogen responsive genes and PBX1-dependent estrogen responsive genes ) defined in MCF7 breast cancer cells against the binding profiles for ERα , PBX1 and FoxA1 . This was accomplished by determining the number of estrogen responsive genes ( all or PBX1-dependent ) harboring at least one binding sites shared or unique to a given factor within ±20 kb from their transcription start site ( TSS ) . This was repeated for the null list consisting of all genes from the refseq gene list not regulated upon estrogen stimulation in MCF7 breast cancer cells . The ratio of estrogen responsive genes associated with binding events within ±20 kb of their TSS over the number of genes from the null list associated with binding events within ±20 kb of their TSS was then plotted in a radar format . Estrogen target genes were significantly associated with PBX1-ERα shared sites ( 7% of total estrogen-responsive genes ) and PBX1-FoxA1-ERα shared sites ( 12% of total estrogen-responsive genes ) ( blue line , Figure 3C ) . FoxA1-ERα shared sites did not preferentially associate with estrogen regulated genes ( Figure 3C ) . Remarkably , PBX1-dependent estrogen target genes were specifically associated with PBX1 unique and PBX1-ERα shared sites ( red line , Figure 3C ) . This was validated through RT-qPCR against estrogen target genes dependent on PBX1 , FoxA1 or both . Indeed , PBX1 depletion disrupted only the regulation of shared or PBX1-dependent estrogen target genes in MCF7 breast cancer ( Figure 3D and Figure S13 ) . Conversely , FoxA1 silencing impacted only the regulation of shared and FoxA1-dependent estrogen target genes ( Figure 3D and Figure S13 ) . Collectively , these data support the notion that PBX1 is required to regulate a specific subset of estrogen responsive genes . Moreover , they suggest that PBX1 is required for the implementation of an estrogen regulated transcriptional program distinct from FoxA1 . ERα-dependent transcriptional response is dependent on its recruitment to the chromatin following estrogen stimulation . To test if PBX1 directly impacts ERα genomic activity we first assessed PBX1 occupancy through ChIP-qPCR assays at known ERα binding sites in MCF7 breast cancer cells treated or not with estrogen . Focusing on both FoxA1-dependent and independent ERα binding sites overlapping with PBX1 ( Figure S4C ) , our results demonstrate that PBX1 is pre-loaded on the chromatin prior to estrogen treatment and remains bound following estrogen treatment ( Figure 4A ) . These sites were chosen from our genome-wide analysis since they are proximal to genes fundamental for breast cancer proliferation and ERα biology . For instance , Myc , CCND1 , FOS and EGR3 are well-studied ERα targets promoting breast cancer growth and progression [53] , [54] , [55] . TFF1 ( also known as PS2 ) is the prototypical estrogen target gene [56] . Sequential ChIP assays ( ChIP-reChIP ) against ERα and PBX1 in both estrogen treated and untreated MCF7 breast cancer cells demonstrates that both factors co-occupy the same sites following ERα recruitment ( Figure 4B ) . ChIP-qPCR assays against ERα in PBX1 depleted MCF7 breast cancer cells demonstrate that ERα recruitment following estrogen treatment is dependent on PBX1 ( Figure 4C ) . Importantly , ERα recruitment is disrupted selectively at sites with pre-loaded PBX1 but not at PBX1-independent sites ( Figure 4D and Figure S4D ) thus ruling out the possibility of a widespread non-specific impact on ERα ability to bind DNA in cells depleted of PBX1 . Overall these results demonstrate that PBX1 can occupy the chromatin prior to ERα recruitment and is required for its genomic activity driving estrogen target gene expression . This is in agreement with a role for PBX1 as a novel pioneer factor in breast cancer . Chromatin structure inherently represents an obstacle for transcription factor activity . Through their ability to integrate and open condensed chromatin , pioneer factors act as molecular beacons for other transcription factors . Using FAIRE ( Formaldehyde Assisted Isolation of Regulatory Elements ) assays [39] , [57] to measure chromatin condensation/openness prior to estrogen stimulation , we demonstrate that PBX1 acts as a pioneer factor . Indeed , genome-wide FAIRE-seq assays in MCF7 breast cancer cells [44] reveals that PBX1 occupied chromatin is already highly accessible ( Figure 5A and Figure S14 ) . Interestingly , the pioneering activity of PBX1 and FoxA1 is synergistic on shared sites ( Figure 5A ) . Sites only bound by FoxA1 are the least accessible ( Figure 5A ) . Comparing FAIRE signal in estrogen starved MCF7 breast cancer cells depleted or not of PBX1 through siRNA revealed a significant decrease in chromatin openness in PBX1-depleted compared to control cells at the majority of tested sites ( Figure 5B ) . In agreement , we demonstrate that PBX1 depletion in MCF7 breast cancer cells seen at the mRNA and protein level ( Figure 2A and 2B ) also significantly decreases its occupancy on the chromatin ( Figure S10B ) . These results suggest that PBX1 plays a central role in increasing chromatin accessibility essential for transcription factor recruitment further supporting its role as a pioneer factor in breast cancer cells . Immunofluorescence , ChIP-seq assays and ChIP-reChIP against PBX1 and FoxA1 suggests that they co-occupy genomic regions in MCF7 breast cancer cells ( Figure 3A and 3B , Figures S3A and S3B , S9 , S10 , and S11 ) . To determine if they collaborate with each other at these genomic regions or if they are part of a common complex we profiled FoxA1 binding following PBX1 depletion in estrogen starved MCF7 breast cancer cells . In agreement with both pioneer factors acting independently of each other , FoxA1 depletion did not alter PBX1 binding to the chromatin ( Figure 5C ) . Similarly , PBX1 depletion did not affect FoxA1 recruitment to the chromatin ( Figure 5D ) . Overall , these results reveal that PBX1 acts as a pioneer factor guiding ERα genomic activity independently of FoxA1 in breast cancer . Covalent modifications are a main staple of epigenetic regulation . Previous reports have demonstrated that methylation of histone H3 on lysine 4 ( H3K4me ) can define functional regulatory element [58]–[61] . Furthermore , cell type-specific distribution of the mono and di-methylated H3K4 ( H3K4me1 and me2 ) epigenetic modifications are central to cell type-specific transcriptional responses [6] , [59] , [60] . In cancer cells , depletion of H3K4me2 interferes with FoxA1 binding to chromatin [6] , [39] . However , the relationship between FoxA1 and H3K4me2 may not be unidirectional , recent evidence suggesting that FoxA1 can favor H3K4me2 deposition [62] . Genome-wide analysis revealed that H3K4me2 is present on approximately 50% of the PBX1 cistrome ( Figure 5E ) . A similar proportion of FoxA1 cistrome overlaps with the H3K4me2 distribution in MCF7 breast cancer cells ( Figure 5E ) . To test if H3K4me2 favors PBX1 binding to the chromatin we overexpressed H3K4me2 demethylase KDM1 ( LSD1/BCH110 ) and determined PBX1 chromatin occupancy through ChIP-qPCR assays . KDM1 over-expression led to a significant reduction of bound PBX1 in estrogen starved MCF7 cells ( Figure 5F ) . In contrast , PBX1 depletion had no effect on H3K4me2 levels and did not affect KDM1 expression ( Figure S15A and S15B ) . Hence , similarly to FoxA1 , the H3K4me2 epigenetic signature favors PBX1 binding . ERα drives proliferation in over 70% of all breast cancers . Accordingly it serves both as a therapeutic target and prognostic factor [63] . In addition , ERα is to date the most exploited marker in the clinic and generally associates with good outcome [64] . FoxA1 does not appear to provide any additional power to discriminate breast cancer subtypes in comparison to ERα profiling [65]–[67] . To assess the prognostic value of PBX1 in breast cancer we performed a meta-analysis using breast tumor expression studies with follow-up data available through Oncomine ( Compendia Bioscience , Ann Arbor , MI ) . We differentiated breast cancer patients according to high ( top 10% ) or low ( bottom 10% ) PBX1 mRNA levels and then generated Kaplan-Meier curves according to the metastasis-free survival status of breast cancer patients . In addition , we independently generated Kaplan-Meier curves using the KMplot web application [68] . Results derived from this analysis performed against FoxA1 confirmed previous reports limiting its prognostic value to identify ERα-positive breast cancers within all breast cancer subtypes . PBX1 expression did not discriminate outcome in these same patients ( Figure 6A and 6B and B ) Interestingly , while FoxA1 mRNA levels where predictive of ERα status , PBX1 levels were evenly distributed in the ERα-positive breast cancer subgroups or all-cases ( Figure S17 ) . By focusing our analysis on ERα-positive breast cancer patients ( as defined by pathological staining ) we revealed the prognostic value of PBX1 . Indeed , ERα-positive breast tumors with high PBX1 expression levels are associated with a reduced metastasis-free survival compared to ERα-positive breast tumors with low PBX1 expression ( p<0 . 002 ) ( Figure 6C and Figure S16C and S16D ) . FoxA1 expression could not stratify metastasis-free survival within ERα-positive breast cancer patients ( Figure 6D and Figure S16C and S16D ) in agreement with the redundant prognostic value of FoxA1 and ERα [67] . These results are further supported by comparing the PBX1-dependent estrogen induced transcription ( Table S2 and Figure S12 ) against expression profiled from breast tumors using Oncomine ( Compendia Bioscience , Ann Arbor , MI ) . This reveals the strong correlation between PBX1-dependent estrogen target genes and twenty-two expression signatures typical of poor-outcome in breast cancer patients ( ex: metastasis , mortality , recurrence and high grade ) ( p<0 . 01 , O . R . >2 ) ( Figure 6E ) . In contrast , the FoxA1-dependent estrogen target genes [44] are significantly associated with only one poor-outcome expression signature ( mortality ) from breast cancer ( Figure 6E ) . Taken together , this suggests that PBX1 drives a very specific transcriptional response underlying progression in ERα-positive breast cancer and reveal the potential prognostic potential for PBX1 within this breast cancer subtype to predict outcome .
Accurate regulation of complex transcriptional programs is central to normal organ development . This is dependent on several layers of controls including DNA sequence , epigenetic signatures and chromatin structure . However , how these different elements are integrated to generate lineage-specific transcriptional programs and how they are affected in the course of disease development is ill defined . In particular , we still misunderstand how epigenetic signatures and chromatin structure affect the transcriptional response to estrogen stimulation in breast cancer . Here we demonstrate that PBX1 acts as a pioneer factor guiding ERα genomic activity in breast cancer ( Figure 7 ) . Indeed , PBX1 translates the H3K4me2-based epigenetic signature to remodel specific genomic domains rendering them accessible for ERα . PBX1 was show to be crucial for histone H4 acetylation [69] and previous reports focusing on the recruitment of MyoD and PDX1 to the chromatin in myeloid and pancreatic islet cells , respectively , were suggestive of the pioneering role of PBX1 [36] , [70] . Considering that PBX1 plays a fundamental role in the development of diverse organs [21] , [24] , [25] and contributes to various types of cancers , namely leukemia , prostate , ovarian and esophageal cancers [26]–[30] , its pioneering functions are likely to apply beyond breast cancer . Similarly , the genomic activity of a wide-range of transcription factors including both homeodomain ( HOX , MEIS , etc ) and non-homeodomain protein ( MyoD , GR , TR , etc ) is promoted by PBX1 [32] , [33] , [36] , [37] , [38] , [71] , [72] . Hence , PBX1 pioneering functions are expected to affect additional transcriptional programs . Finally , we reveal that PBX1 and FoxA1 can co-occupy specific genomic regions in breast cancer cells . While co-occupancy of specific genomic region by pioneer factors , such as PU . 1 and GATA1 has previously been reported [73] , our results demonstrates that this translates into greater chromatin accessibility . Furthermore , we reveal that FoxA1-independent PBX1 bound sites are more accessible than PBX1-independent FoxA1 sites . In agreement , the estrogen induced transcriptional response is preferentially associated with ERα binding at PBX1 or PBX1-FoxA1 shared sites . This also relates to a distinct prognostic value for FoxA1 and PBX1 . Indeed , while FoxA1 expression in ERα-positive primary breast tumors does not discriminate their metastasis-free outcome , elevated PBX1 expression has significant prognostic potential towards metastasis . Gene signatures such as the Oncotype DX or MammaPrint have been successfully employed in the clinic to discriminate outcome in breast cancer based mostly on their ability to identify specific breast cancer subtypes [74] , [75] . However they do not perform as well when restricted to ERα-positive patients [76] , [77] . Our study introduces PBX1 as a potential clinical tool with additive prognostic value to ERα . Indeed , all patients with ERα-positive metastatic breast cancer and half or more of ERα-positive early stage breast cancers develop resistance to endocrine therapies leading to a poor outcome [78] . Hence , it is fascinating to speculate a role for PBX1 in the development of drug resistance in breast cancer . Taken together , these results reveal the intricate interplay between distinct pioneer factors required for the implementation of specific transcriptional response to estrogen in breast cancer and distinguishes PBX1 as a prognostic marker .
FoxA1-independent ERα binding sites across the genome were identified by subtracting the False Discovery Rate ( FDR ) 20% FoxA1 cistrome from the FDR1% estrogen-induced ERα cistrome from MCF7 breast cancer cells . This was accomplished using the bedfiles that specifies the genomic coordinates for the FoxA1 cistrome called by MAT available through the Cistrome website ( http://cistrome . dfci . harvard . edu/ap/ ) using a cutoff based on the FDR 20% and the bedfile that specifies the genomic coordinates for the ERα cistrome called by MAT using a cutoff based on FDR 1% . These files were loaded on the Cistrome website and the FoxA1 bedfile was subtracted from the ERα bedfile using the “Operate on Genomic Intervals - subtract” [79] . To define the proportion of the ERα cistrome overlapping or not with FoxA1 harboring the PBX1 DNA recognition motif ( Transfac M01017 ) we used the default settings of the “Integrative Analysis – Screen motif” function available on the Cistrome website . Expression correlation between ERα and PBX1 from the NCI60 cancer cell panel using BioGPS ( http://biogps . gnf . org ) . Expression correlation analysis between ERα and PBX1 in breast cancer cells or primary tumors was achieved using Oncomine ( https://www . oncomine . com ) . Venn diagrams were generated by defining the proportion of sites shared and unique between different bedfiles using the functions found under “Operate on Genomic Intervals” within the Cistrome website . Overlapping binding sites were defined by having at least one base pair in common . Genome structure correction ( GSC ) [80] was run to establish the significance of the overlap between datasets . The software was run with the following setting: ( region fraction ) -R = 0 . 2 , ( sub-region fraction ) –S = 0 . 4 and basepair_overlap_marginal ( -bm ) as statistic text . P values for results presented on Figure S6A and S6B have been corrected using the Bonferroni post-test based on 12 comparisons . For immunofluorescence , MCF7 cells were treated as previously described [81] . PBX1 was stained using PBX1 monoclonal antibody ( Abnova Corporation ) . FoxA1 was stained using FoxA1 polyclonal antibody ( Abcam ) . Secondary antibodies Alexa 488 and 555 were purchased from Invitrogen . Digital images were analyzed with ImageJ ( http://rsbweb . nih . gov/ij/index . html ) . MCF7 cells were maintained in phenol red-free medium ( Invitrogen ) supplemented with 10% CDT-FBS as described previously ( Lupien et al . 2008 ) [6] prior to transfection . Following two days of estrogen starvation cells were transfected with siPBX1 #1 ( Darmachon ) or siPBX1 #2 ( Invitrogen ) . Small-interfering RNA against Luciferase was used as a negative control [8] . Transfection was performed using Lipofectamine2000 according to manufacturer's instructions ( Invitrogen ) . For cell proliferation assays , cell number or O . D . ( 450 nm ) ( WST-1 assay , Takara Bio Inc ) was determined every 24 h after estrogen ( E2 ) addition ( 1×10−8 M final ) . For expression assays , RNA was extracted 3 h following E2 stimulation . RNA samples from siControl or siPBX1 treated MCF7 in the presence or absence of estrogen were hybridized on HT12 human beads array ( Illumina Inc . ) . Analyses were performed using BRB-Array Tools Version 3 . 8 . 1 . Raw intensity data were log2 transformed , median normalized and filtered to remove non-detected spots as determined by Illumina Software . The normalization was performed by computing a gene-by-gene difference between each array and the median ( reference ) array , and subtracting the median difference from the log intensities on that array , so that the gene-by-gene difference between the normalized array and the reference array is zero . Two class non-paired comparison analyses were performed by computing a t-test for each gene using normalized log-intensities . Differentially expressed genes were determined at a significance level of p less than 0 . 01 . A four class ANOVA at p less than 0 . 01 was also performed to identify genes expressed differentially across the four groups . Hierarchical clustering was employed using a Euclidean distance measure to generate heat maps for subsets of significant genes using the open source software Cluster/Treeview . The data can be accessed in GEObrowser under superSeries GSE28008 FoxA1 dependent gene-signature was obtained from previously published microarray data [44] . ChIP qPCR was performed as described previously [82] . Antibodies against PBX1 ( Abnova ) FoxA1 , H3K4me2 ( Abcam ) and ERα ( Santa cruz biotechnology ) were used in these assays . ChIP–reChIP was performed as described previously [83] . Statistically significant differences were established using a Student's t-test comparison for unpaired data versus an internal negative control . Primer sequences used in this assay are found in Table S3 . ChIP assay were conducted as described above . Library preparation for next-generation sequencing was performed according to manufacturer's instruction starting with 5 ng of material ( Illumina Inc . ) . Single paired libraries were sequenced using the GAIIx ( Illumina Inc ) . Over 28 and 31 million reads were generated through the GAIIx for the PBX1 ChIP and Input samples , respectively . Of those , 88% and 96% , respectively , were aligned to the human reference genome . These reads were aligned using the ELAND software . The MACS peak-calling algorithm was used to call significantly enriched peaks using default settings ( P<10−5 ) and specifying the peak size = 200 bp . The data is accessible on the GEObrowser ( accession number: PBX1:GSE28008 and H3K4me2:GSE31151 ) . FAIRE analysis was performed as previously described [39] , [84] . FAIRE-seq data were already published [44] . MCF7 cells were maintained in DMEM ( Invitrogen ) supplemented with 10% FBS as described previously ( Lupien et al . 2008 ) [6] prior to transfection . MCF7 cells were transfected with the pCMX-KDM1construct or the control empty vectors ( 10 µg per well in 6 well plates ) using Lipofectamine 2000 DNA transfection reagent according to the manufacturer's instructions ( Invitrogen ) . ChIP assays against PBX1 were performed 48 h post-transfection . Several expression profiles [42] , [63] , [85] , [86] , [87] , [88] , [89] , [90] , [91] compiled in Oncomine ( https://www . oncomine . com ) were used to define PBX1 and FoxA1 mRNA expression levels . ERα stratification was based on protein levels provided in each independent expression study employed in this analysis . Samples were ranked according to processed probe signal provided by each independent expression study ( Max to Min ) and top and bottom 10% were classified as high and low expression respectively . Each sample was then matched with its associated outcome with a 1 , 3 and 5 years follow-up provided by each independent study ( metastasis-free survival: alive or dead ) . Statistical analyses were performed using Fisher exact test . PBX1-dependent or FoxA1-dependent estrogen ( E2 ) upregulated gene signatures [44] were analyzed against several expression profiles previously shown to be significantly associated with breast cancer outcome using Oncomine . [86] , [87] , [88] , [90] , [92] , [93] , [94] , [95] , [96] , [97] , [98] , [99] , [100] , [101] , [102] , [103] Significant association was established at a pValue of at least <0 . 01 and an Odds Ratio >2 . | Approximately two-thirds of breast cancers depend on the estrogen receptor alpha ( ERα ) for their growth . Its capacity to act as a transcription factor binding DNA following estrogen stimulation is central to promote a pro-tumorigenic transcriptional response . Importantly , different classes of ERα-positive breast tumors can be discriminated based on outcome . However , the underlying mechanisms driving these differences are unknown . Here we demonstrate that PBX1 acts as a pioneer factor recognizing a specific epigenetic modification to remodel chromatin and guide ERα genomic activity . This translates in a specific transcriptional program associated with poor-outcome in breast cancer patients . Even more , PBX1 expression alone is sufficient to identify a priori ERα-positive breast cancer patients at risk of developing metastasis . Overall , this study defines the mechanisms dependent on the pioneer factor PBX1 that drives an aggressive response in a subset of ERα-positive breast cancers . These features highlight the uniqueness of PBX1 and demonstrate its potential prognostic value . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"medicine",
"endocrinology",
"biology",
"genomics",
"diabetes",
"and",
"endocrinology",
"genetics",
"and",
"genomics"
] | 2011 | PBX1 Genomic Pioneer Function Drives ERα Signaling Underlying Progression in Breast Cancer |
Typhoid fever remains a public health problem in Vietnam , with a significant burden in the Mekong River delta region . Typhoid fever is caused by the bacterial pathogen Salmonella enterica serovar Typhi ( S . Typhi ) , which is frequently multidrug resistant with reduced susceptibility to fluoroquinolone-based drugs , the first choice for the treatment of typhoid fever . We used a GoldenGate ( Illumina ) assay to type 1 , 500 single nucleotide polymorphisms ( SNPs ) and analyse the genetic variation of S . Typhi isolated from 267 typhoid fever patients in the Mekong delta region participating in a randomized trial conducted between 2004 and 2005 . The population of S . Typhi circulating during the study was highly clonal , with 91% of isolates belonging to a single clonal complex of the S . Typhi H58 haplogroup . The patterns of disease were consistent with the presence of an endemic haplotype H58-C and a localised outbreak of S . Typhi haplotype H58-E2 in 2004 . H58-E2-associated typhoid fever cases exhibited evidence of significant geo-spatial clustering along the Sông H u branch of the Mekong River . Multidrug resistance was common in the established clone H58-C but not in the outbreak clone H58-E2 , however all H58 S . Typhi were nalidixic acid resistant and carried a Ser83Phe amino acid substitution in the gyrA gene . The H58 haplogroup dominates S . Typhi populations in other endemic areas , but the population described here was more homogeneous than previously examined populations , and the dominant clonal complex ( H58-C , -E1 , -E2 ) observed in this study has not been detected outside Vietnam . IncHI1 plasmid-bearing S . Typhi H58-C was endemic during the study period whilst H58-E2 , which rarely carried the plasmid , was only transient , suggesting a selective advantage for the plasmid . These data add insight into the outbreak dynamics and local molecular epidemiology of S . Typhi in southern Vietnam .
The Mekong river delta is located in the south of Vietnam ( Figure 1 ) in an area of 40 , 000 square kilometres ( 12% of Vietnam's land mass ) and is home to over 20% of Vietnam's population . It is the area where the Mekong river divides into multiple channels and drains into the South China sea . The low-lying nature of the land and the seasonal fluctuation in water level make the region particularly vulnerable to flooding . The human-restricted disease typhoid fever is endemic to the Mekong delta region [1] , [2] , with a mean incidence of ∼80 cases per 100 , 000 people per year [1] , [2] , [3] , [4] . Salmonella Typhi ( S . Typhi ) , the bacterium causing typhoid fever , is transmitted human-to-human in areas with poor sanitation . The first multidrug resistant ( MDR; defined as resistance to chloramphenicol , ampicillin and co-trimoxazole ) typhoid outbreak in Vietnam occurred in Kien Giang in the Mekong river delta in 1993 [5] , and since then the fluoroquinolones have become the first choice for the treatment of typhoid fever . MDR S . Typhi is usually associated with self-transferrable IncHI1 plasmids carrying multiple resistance genes encoded within mobile genetic elements [6] , [7] , [8] , [9] , [10] . Between 1994 to 1998 , over 80% of S . Typhi strains isolated in the Mekong delta region were reported to be MDR [11] , and declined to approximately 50% between 2002 and 2004 [5] , [11] , [12] . This decline may have been catalysed by the change in treatment policy and the widespread use of fluoroquinolones ( such as ciprofloxacin and ofloxacin ) , which are effective against MDR strains [13] , [14] . While high-level resistance to fluoroquinolones remains uncommon in Vietnam and other endemic typhoid regions , there has been a sharp increase in the proportion of S . Typhi isolates that are resistant to nalidixic acid [11] . Nalidixic acid ( Nal ) is a quinolone antimicrobial ( the precursor of fluoroquinolones ) and the main mechanism for Nal resistance in S . Typhi is mutation of the DNA gyrase gene , gyrA [11] , [15] . S . Typhi strains with Nal resistance-conferring mutations in the gyrA gene usually have elevated minimum inhibitory concentrations ( MIC ) to fluoroquinolone antibiotics such as ciprofloxacin ( MIC ≥0 . 125 µg/ml ) [16] . However , these organisms are not resistant according to CLSI guidelines , which are currently defined by MIC ≥4 µg/ml to ciprofloxacin [17] . Even though these strains are not classified as resistant , they are of clinical importance since typhoid patients infected with such strains respond less well to fluoroquinolone therapy [14] , [15] , [18] , [19] . Such patients frequently have a protracted fever and an increased rate of relapse , compared to those infected with strains that do not have an elevated MIC to fluoroquinolones ( MIC <0 . 125 µg/ml to ciprofloxacin and <0 . 25 µg/ml to ofloxacin ) [15] , [18] , [19] . Resistance to Nal is therefore often used as a marker to predict how well a patient will respond to therapy with fluoroquinolones . The incidence of typhoid fever has declined in Vietnam . Between 1991 and 2001 approximately 17 , 000 cases of typhoid fever ( blood culture confirmed and syndromic cases ) were reported annually through the Vietnamese national surveillance system [1] , [2] , while only 4 , 323 and 5 , 030 annual typhoid fever cases were reported in 2004 and 2005 , respectively ( Source: National Institute of Health and Epidemiology , Ministry of Health , Vietnam ) . However , 75% of these cases occurred in the Mekong delta [1] , [2] , likely associated with high population density and the propensity of the land to become saturated with floodwaters . In this region , the occurrence of S . Typhi isolates that are MDR and Nal resistant severely limits treatment options . More than 95% of S . Typhi isolated in the Mekong delta are now resistant to Nal , placing a considerable pressure on the effective use of fluoroquinolones [11] , [12] . To compare alternative therapies for typhoid fever patients infected with strains that are MDR and Nal resistant , a randomized controlled trial comparing gatifloxacin ( a newer 8-methoxy fluoroquinolone ) and azithromycin ( a macrolide ) was conducted during 2004–2005 in the Mekong delta region [20] . Typhoid patients ( adults and children ) were recruited into the study at three hospitals in the south of Vietnam ( details in Materials and Methods , locations are highlighted in Figure 1B ) . Here , we used a high-throughput single nucleotide polymorphism ( SNP ) typing assay to investigate the population structure of S . Typhi collected during the study [20] , and to determine the genetic mechanisms of drug resistance in this S . Typhi population .
The study was conducted according to the principles expressed in the Declaration of Helsinki and approved by the Institutional Review Board of the Hospital for Tropical Diseases and the Oxford Tropical Research Ethics Committee ( OXTREC ) . All patients provided written informed consent for the collection of samples and subsequent analysis ( written informed consent was provided by the parents or guardian of children under 18 years of age ) . S . Typhi isolates were collected during a multicenter clinical trial [20] conducted between January 2004 and December 2005 at ( a ) the Hospital for Tropical Diseases in Ho Chi Minh City ( n = 10 ) , ( b ) Dong Thap Provincial Hospital , Cao Lanh , Dong Thap province ( n = 25 ) and ( c ) An Giang Provincial Hospital , Long Xuyen , An Giang province ( n = 232 ) . Locations of ( b ) and ( c ) are shown in Figure 1B . Adults and children over 6 months of age were eligible to be included in the study if they had clinically suspected or culture-confirmed uncomplicated typhoid fever and if fully informed written consent had been obtained . Patients were tested for typhoid carriage ( via stool culture ) during follow-up appointments at 1 , 3 and 6 months after discharge from hospital . The 267 isolates presented in this study constitute nearly the full complement of 287 S . Typhi isolated from culture-confirmed typhoid patients enrolled in the trial; the recruitment flow for which is described in detail in [20] . Antimicrobial susceptibility testing was performed at the time of initial isolation by disc diffusion according to Clinical Laboratory Standards Institute ( CLSI ) guidelines [17] . Antimicrobial agents tested were: ampicillin , chloramphenicol , trimethoprim-sulfamethoxazole ( co-trimoxazole ) , nalidixic acid , ofloxacin , ciprofloxacin and ceftriaxone ( Oxoid , Basingstoke , UK ) . Minimum Inhibitory Concentrations ( MICs ) for amoxicillin , chloramphenicol , nalidixic acid , ofloxacin , ciprofloxacin , gatifloxacin , ceftriaxone and azithromycin were determined by E-test ( AB Biodisk , Solna , Sweden ) . Multidrug resistance ( MDR ) of isolates was defined as resistance to chloramphenicol ( MIC ≥32 µg/mL ) , ampicillin ( MIC ≥32 µg/mL ) and trimethoprim-sulfamethoxazole ( MIC ≥8/152 µg/mL ) . Nalidixic acid resistance was defined by an MIC ≥32 µg/mL . After initial isolation , S . Typhi was stored at −70°C in a 20% glycerol solution until required for further analysis and DNA extraction . To revive frozen organisms , MacConkey and Xylose Lysine Decarboxylase ( XLD ) agar plates were inoculated from the glycerol solution and incubated at 37°C overnight . To ensure correct identification , colonies were checked using slide agglutination with serotype specific antisera ( Vi , O9 ) and an irrelevant antisera as a negative control ( O4 ) ( Murex , Dartford , United Kingdom ) . Two mL of nutrient broth were inoculated with single S . Typhi colonies and incubated overnight . Overnight cultures were centrifuged and S . Typhi DNA was extracted using Wizard Genomic DNA Purification kit ( Promega , USA ) as recommended by the manufacturer's guidelines . DNA was stored at −20°C . DNA was quantified using the Quant-IT PicoGreen dsDNA Reagent and Kit ( Invitrogen , UK ) . S . Typhi DNA concentrations were adjusted to 50 ng/mL and 250 ng of DNA were pipetted into 96-well plates . Each 96-well plate contained two isolates in duplicate and the sequenced S . Typhi isolate CT18 as control for assay reproducibility . The chromosomal haplotype of S . Typhi isolates was determined based on alleles present at 1 , 485 chromosomal SNP loci identified previously from genome-wide surveys [12] , [21] and listed in [22] , [23] . IncHI1 plasmid haplotypes were determined based on eight SNPs identified previously [22] , [24] and resistance gene sequences were interrogated using additional oligonucleotide probes ( listed in Table S1 ) . All loci were interrogated using a GoldenGate custom oligonucleotide array according to the manufacturer's standard protocols ( Illumina ) , as described previously [22] , [23] . A maximum-likelihood phylogenetic tree based on chromosomal SNPs was constructed using the RAxML software [25] . Clinical data were entered into an electronic database ( Epi Info 2003 , CDC , Atlanta , USA ) . For comparison of patient characteristics according to infecting S . Typhi haplotypes , Kruskal-Wallis tests were used for analysis of continuous variables ( age , length of stay in hospital , fever clearance time ) and logistic regression was used for categorical variables ( presence of symptoms ) . Odds ratios were adjusted for duration of fever prior to admission and use of antibiotics prior to admission by including these variables in the logistic regression model . Where data was missing for a particular patient and variable , that patient was excluded from analysis of that variable ( N≤35 patients ) . Two-tailed p-values are reported; statistical analysis was performed using the R package ( http://www . r-project . org/ ) . Oligonucleotide primers for the amplification of the quinolone resistance determining regions in the S . Typhi gyrA gene were as follows [11]: GYRA/P1 5′-TGTCCGAG ATGGCCTGAAGC-3′ and GYRA/P2 5′-TACCGTCATAAGTTATCCACG-3′ . Predicted PCR amplicon size was 347 bp . PCR was performed under the following conditions; 30 cycles of: 92°C for 45 seconds , 45–62°C for 45 seconds and extension at 74°C for 1 minute , followed by a final extension step at 74°C for 2 minutes . PCR products were purified and directly sequenced using the CEQ DTCS - Quick Start Kit ( Beckman Coulter , USA ) and the CEQ 8000 capillary sequencer . The resulting DNA sequence was analyzed using CEQuence Investigator CEQ2000XL ( Beckman Coulter , USA ) . All sequences were verified , aligned and manipulated using Bioedit software ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) and compared to other gyrA sequences by BLASTn at NCBI . Patient addresses were recorded at the time of hospital admission . The latitude and longitude of the residences of typhoid fever patients ( to the hamlet/village level ) was assigned from the collected address data using 1/50 , 000 scale maps ( Source: Cartographic Publishing House and VinaREN , Ministry of Natural Resources and Environment , Vietnam ) and cross-checked using the websites http://www . basao . com . vn and http://ciren . vn . Location data was analysed using Quantum GIS version 1 . 4 . 0 ( http://www . qgis . org/ ) . Locations were colour-coded according to S . Typhi haplotype and clustering of specific haplotypes was calculated using the nearest-neighbour analysis function . Nearest-neighbour analysis examines the distances between each point and the closest point to it , and then compares these to expected values for a random sample of points from a CSR ( complete spatial randomness ) pattern . Significant clustering was inferred by Z-score value ( standard normal variable ) of less than 0; a positive score was interpreted as dispersion of locations .
A recently developed typing system , based on the simultaneous interrogation of 1 , 485 S . Typhi chromosomal single nucleotide polymorphisms ( SNPs ) using a custom Illumina GoldenGate array [22] , [23] , was used to analyse each of the S . Typhi isolates . This approach facilitates the unequivocal assignment of isolates to haplotypes , allowing closely related strains to be distinguished phylogenetically based on single nucleotide changes . From 287 patients with culture confirmed typhoid fever recruited between January 2004 and December 2005 [20] , 267 S . Typhi were available for SNP typing . These included 264 S . Typhi isolated from blood culture at admission [20] one relapse isolate and two faecal carriage isolates . A total of 24 S . Typhi ( 23 isolated from An Giang and one from Dong Thap , randomly distributed throughout the study period ) were not available for SNP typing . A total of 261 S . Typhi isolates ( 98% ) were of the common H58 haplogroup . The remaining isolates were of haplotypes H1 ( isolates BJ105 , BJ63 , BJ64 ) , H45 ( isolate BJ264 ) , H50 ( isolate BJ9 ) and H52 ( isolate BJ3; see Figure 2 and Table 1 ) . The H58 S . Typhi isolates displayed variation at 10 SNP loci ( detailed in [23] ) , which differentiated seven distinct sub-H58 haplotypes , shown in Figure 2 . However , 242 ( 93% ) of these isolates belonged to just three closely related H58 haplotypes , designated C , E1 and E2 in Figure 2 ( numbers given in Table 1 ) . The genome of S . Typhi strain AG3 , isolated during the study ( March 2004 ) from a typhoid fever patient living in An Giang province , was sequenced previously [21] . AG3 belongs to the H58-E2 haplotype , and the SNPs separating E2 from haplotypes E1 and C were originally identified by analysis of the AG3 genome . Therefore , the ability to differentiate within the cluster of 242 S . Typhi isolates was dependant on the inclusion of strain AG3 in the initial genome sequencing study used to identify SNP loci [21] . All but one S . Typhi isolated from the blood culture of patients admitted to An Giang Provincial hospital ( 231/232 ) , as well as the two faecal S . Typhi strains isolated from chronic carriers in An Giang , belonged to the S . Typhi H58 haplogroup . The remaining S . Typhi isolate BJ264 ( see Figure 2 ) was of the H45 haplotype and was isolated from a typhoid fever patient who was resident in neighbouring Can Tho province . One patient at An Giang Provincial hospital relapsed with symptoms of typhoid fever and had S . Typhi isolated from blood culture 11 days after the initial treatment ( gatifloxacin ) had been completed . The mother of the patient was found to be a chronic S . Typhi carrier . All three S . Typhi strains - the patient's admission and relapse blood culture isolates and the mother's faecal isolate - belonged to the S . Typhi H58-E2 subtype . The patient's isolates were both MDR and carried the IncHI1 ST6 plasmid ( see below ) , whereas the mother's S . Typhi isolate was plasmid-free and susceptible to all first line antimicrobials at the time of isolation . All three isolates were Nal resistant but sensitive to gatifloxacin ( MIC 0 . 19 mg/ml ) . Stool cultures were taken at 1 month ( 96% of patients ) , 3 months ( 93% ) and 6 months ( 44% of follow-up . Chronic faecal carriage of S . Typhi was detected in only one trial patient . This was a MDR H58-C strain isolated from stool 6 months after treatment ( with gatifloxacin ) , which was indistinguishable at all assayed loci from the patient's original blood culture isolate . Both isolates were Nal resistant but sensititve to gatifloxacin ( MIC 0 . 19 mg/ml ) . At Dong Thap Provincial Hospital , only 3 of the 25 S . Typhi isolates did not belong to the H58 haplogroup . Two H1 isolates ( BJ63 and BJ64; Figure 2 ) were identical at all assayed loci and were isolated on consecutive days from two patients resident in Dong Thap . A third H1 strain ( BJ105; Figure 2 ) differed from BJ63 and BJ64 at 16 chromosomal SNP loci and was isolated in Dong Thap 14 months after these isolates . Two siblings from Dong Thap province were admitted on consecutive days in 2004 and were both infected with MDR S . Typhi of the haplotype H58-C . Of the ten S . Typhi strains isolated at the Hospital for Tropical Diseases in Ho Chi Minh City , eight were members of the H58 haplogroup , with patients resident in Ho Chi Minh City ( n = 4 ) , Long An ( n = 1 ) , Kien Giang ( n = 2 ) and An Giang ( n = 1 ) provinces , reflecting the larger catchment area of the hospital . The remaining two S . Typhi were of haplotypes H52 ( BJ3 ) and H50 ( BJ9 ) and were isolated from patients living in Binh Hoa province and Ho Chi Minh City , respectively . There was no simple association between S . Typhi haplotype and patient age , length of stay in hospital , fever clearance time , vomiting , abdominal pain , hepatomegaly or relapse ( Table 2 ) . However , upon admission , patients infected with S . Typhi haplotype H58-E2 tended to report lower frequencies of diarrhoea and headache and higher frequencies of constipation compared to patients infected with other haplotypes , including H58-C ( see Table 2 ) . The GoldenGate assay incorporated probes targeting IncHI1 plasmid sequences , allowing for detection of the presence of IncHI1 plasmid within the genomic DNA extracted from each S . Typhi isolate . The assay indicated that a total of 139 S . Typhi isolates harboured an IncHI1 plasmid . All plasmids were of the IncHI1 ST6 sequence type [24] and all plasmid-bearing isolates belonged to the S . Typhi H58 haplogroup ( see Table 1 ) . The MDR IncHI1 plasmid was more common among H58-C isolates than H58-E2 isolates ( 86% vs 19% , see Table 1 ) . Of the 139 S . Typhi isolates giving positive signals for IncHI1 SNP loci , 137 ( 99% ) were classified as MDR by antimicrobial susceptibility testing conducted at the time of isolation . One other IncHI1-positive isolate tested positive by GoldenGate assay for the genes sul1 , sul2 , dfrA7 , tetACDR , strAB , bla and cat ( resistance genes; functions outlined in Table S1 ) like the MDR isolates , yet had low MICs for chloramphenicol , ampicillin and trimethoprim-sulfamethoxazole . An additional S . Typhi isolate , BJ5 , was resistant to ampicillin and trimethoprim-sulfamethoxazole but sensitive to chloramphenicol . This was consistent with GoldenGate assay results , which gave positive signals for the repC replication initiation gene of IncHI1 , resistance genes strAB , bla , sul1 , sul2 , dfrA7 , but no signal for sequences from the cat gene encoding chloramphenicol resistance . A further 17 S . Typhi isolates were recorded as MDR according to their antimicrobial susceptibility pattern at the time of isolation , but did not test positive for IncHI1 plasmid loci . This likely reflects loss of the IncHI1 plasmid in culture or storage between the time of isolation and DNA extraction . The MDR status of the infecting S . Typhi isolate was not associated with fever clearance time ( p = 0 . 3 , two-sided T-test ) or treatment failure ( p = 0 . 18 , Chi2 test ) . A total of 257 S . Typhi isolates were resistant to nalidixic acid ( Nal ) . All of these isolates belonged to the H58 haplogroup ( Table 1 ) and all were susceptible to gatifloxacin , ciprofloxacin and ofloxacin according to current CLSI guidelines [17] . S . Typhi haplotypes H58-C , H58-E1 and H58-E2 were uniformly resistant to Nal , with the exception of a single H58-C isolate which had an intermediate MIC of 28 µg/mL ( resistance defined as MIC ≥32 µg/mL ) . The sequenced H58-E2 isolate AG3 harbours a mutation changing serine ( TCC ) to phenylalanine ( TTC ) at codon 83 in the gyrA gene ( GyrA-Ser83Phe ) [21] , which is known to confer resistance to Nal [26] . In the present study we sequenced the gyrA gene in 223 of the Nal resistant isolates ( 87% ) and found the same GyrA-Ser83Phe amino acid substitution in all isolates tested . Figure 3 shows the spatial distribution of the residences of 160 typhoid patients ( this information was not available for the remaining patients ) . Of the patients admitted at An Giang Provincial Hospital and Dong Thap Provincial Hospital , sufficient address detail to allow for assignment of latitude and longitude was provided in 61% and 73% of cases , respectively . This represents 50% and 20% of all blood culture confirmed typhoid fever patients at An Giang Provincial Hospital and Dong Thap Provincial Hospital , respectively , during 2004–2005 . In An Giang , patients' homes clustered around the An Giang Provincial Hospital , but also around the Sông H u branch of the Mekong river ( see Figure 3 ) . Most S . Typhi isolated from patients living near this point in An Giang province were of the H58-E2 haplotype ( orange in Figure 3 ) , and this group demonstrated significant clustering using nearest-neighbour analysis ( n = 57 , Z-score = −14 . 145 ) . In contrast , S . Typhi of the H58-C haplotype were isolated relatively frequently in neighbouring provinces and had a more sporadic clustering pattern ( red in Figure 3 ) . While isolates from An Giang Provincial Hospital are overrepresented in this spatial analysis , the apparent increase in typhoid density in An Giang is consistent with total Typhi isolation rates at the two hospitals during the study period ( 284 at An Giang Provincial Hospital and 90 at Dong Thap Provincial Hospital ) . The temporal distribution of S . Typhi haplotypes over 2004 and 2005 is shown in Figure 4 . Typhoid fever cases peaked just prior to the onset of the wet season in each year , as has been observed previously in this region [1] , [3] ( see monthly rainfall , solid line in Figure 4 ) . In 2004 , H58-E2 and H58-C were both prevalent ( 62 C , red in Figure 4; 103 E2 , orange in Figure 4 ) , whereas few isolates of H58-E2 Typhi were observed during 2005 ( 55 C , 4 E2; see Figure 4 ) . The decline of H58-E2 may be associated with selection for the IncHI1 MDR plasmid , which was much more common in H58-C ( Table 1 ) . As Figure 4 highlights , the majority of isolates collected during the second season were MDR and carried the IncHI1 plasmid ST6 .
During 2004–2005 , typhoid in the Mekong river delta region of Vietnam was almost exclusively caused by a single Nal-resistant clonal complex of S . Typhi . This reflects a higher level of clonality than observed in other localised S . Typhi populations studied to date , which may be indicative of higher transmission rates in this location . The high level of Nal resistance and multidrug resistance , frequently in the same strains , is concerning and continues to pose problems for the successful treatment of typhoid fever . | Typhoid fever remains a serious public health issue in some parts of Vietnam , including the Mekong delta region . Typhoid is caused by the bacterium Salmonella Typhi , which is frequently multidrug resistant and shows reduced susceptibility to fluoroquinolone-based drugs . We assayed single nucleotide variation in the genomes of S . Typhi organisms isolated from 267 patients with typhoid fever in the Mekong delta between 2004 and 2005 , and identified genetically distinct S . Typhi strains . We also detected the presence of genes or mutations that confer drug resistance in those strains . We found that the vast majority of typhoid cases were caused by one of two subgroups of H58 S . Typhi , referred to as H58-C and H58-E2 . The H58-E2 group appeared to cause an outbreak in 2004 , affecting patients living in a small zone near the Mekong River . The other group , H58-C , was present throughout the study period and affected patients living in a broader area of the Mekong River delta . Most of the H58-C strains were resistant to multiple drugs and carried a plasmid encoding multiple resistance genes . However very few H58-E2 strains were multidrug resistant , which may explain why the strain did not persist after the initial outbreak . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] | 2011 | Temporal Fluctuation of Multidrug Resistant Salmonella Typhi Haplotypes in the Mekong River Delta Region of Vietnam |
Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes . An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens . Recently , systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels . To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents , we integrated host transcriptomes and proteomes using a network-based approach . Our approach combines expression-based regulatory network inference , structured-sparsity based regression , and network information flow to infer putative physical regulatory programs for expression modules . We applied our approach to identify regulatory networks , modules and subnetworks that drive host response to multiple influenza infections . The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis , splicing , and interferon signaling processes in the differential response of viral infections of different pathogenicities . We used the learned network to prioritize regulators and study virus and time-point specific networks . RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators . Taken together , our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection .
To combat infections from diverse pathogens , mammalian immune systems must be able to mount appropriate and specific responses to pathogenic infections . A key challenge in current infectious disease research is to understand the molecular mechanisms that make the host immune system more or less susceptible to a particular strain of a pathogen , for example , different influenza A virus strains , than another . Transcriptional regulatory networks that connect regulatory proteins to target genes are central players in how mammalian cells mount appropriate responses to different pathogenic infections . Because the components and connectivity of these networks are largely not known , a significant amount of effort has been invested to collect high-throughput datasets that provide a comprehensive molecular characterization of host response to multiple viruses at multiple levels , including the transcriptome and proteome [1–4] . These genomic datasets provide unique opportunities to identify molecular network components of host response that are conserved across multiple viruses or specific to a virus of a particular pathogenicity . Such networks can be used to prioritize regulators for follow up validation studies that provide greater insight into the mechanisms by which the host cell perceives and responds to different pathogenic infections . Network- and module-based approaches for analyzing omic datasets have been powerful for dissecting mammalian cellular response to different environmental perturbations , including response to various pathogenic infections [5 , 6] . The majority of these approaches have used genome-wide transcriptomic data and can be grouped into those that infer correlational networks [3 , 7–12] , or Module Networks , in which regulators are inferred for groups of co-expressed genes called modules [9 , 13 , 14] . A few approaches have integrated additional data types , e . g . physical protein-protein or signaling networks with transcriptional data [15–20] , that vary in the number of samples needed to infer networks . While these approaches have provided important insights into the host response , they have not integrated multiple types of omic measurements ( e . g . mRNA and protein levels ) , which can differ in quality and sample size . A second challenge is that experimental validation of large-scale predictions is expensive and most of the generated predictions have not been validated experimentally . Although several network-based prioritization methods have been proposed , the results of a prioritization scheme have been followed up with experimental validation in a handful of studies [21] . To gain a more complete understanding of the molecular networks driving host response , we integrated transcriptomic and proteomic measurements of mammalian host response with existing protein-protein interactions . These omic measurements capture human and mouse cellular response to multiple Influenza A viruses exhibiting different levels of pathogenicities . Our starting point is an expression-based regulatory network connecting transcription factors and signaling proteins to target genes and gene modules . We then use proteomic measurements to predict additional regulators of gene modules by applying a structured sparsity-inducing regression approach , Multi-Task Group LASSO , to find proteins whose levels are predictive of mRNA levels of entire modules . By decoupling the mRNA and protein-based regression into two steps , our approach is less sensitive to varying sample size for each type of omic data . Finally , we predict physical regulatory programs connecting mRNA and protein-based regulators through a small number intermediate nodes using Integer Linear Programming ( ILP ) -based network information flow . We used our integrated regulatory networks to study the host response across the different viruses . We tested prioritized regulators using small interfering RNAs and found several regulators that significantly impact viral replication , five of which have not been previously associated with influenza related response . We examined host response dynamics at the network level , identifying regulatory network components that are active or missing under different viral treatments . Our inferred gene modules capture strain- and pathogenicity-specific patterns of mammalian immune response to influenza infections that recapitulate and expand upon known immune response pathways . In particular , we identified one module that was enriched for interferon signaling and exhibited repressed expression in the high-pathogenicity wild-type H5N1 , but was induced in low pathogenicity H1N1 strains . Another module suggests that apoptosis-related pathways might be down-regulated in low-pathogenicity viruses . Our findings of host regulatory modules together with their upstream regulatory programs suggest that our network-based approach is a powerful way to systematically characterize immune response to diverse pathogenic infections .
To perform a systematic , integrative analysis of transcriptome and proteome measurements of host response to influenza virus infections , we began by inferring a regulatory module network in two stages , followed by three major downstream analyses ( Fig 1 ) . We ( 1 ) infer a regulatory module network based on changes in mRNA abundance under viral infection and ( 2 ) predict protein regulators whose abundances are predictive of gene expression in the modules . This integrated regulatory module network enabled ( 3 ) prioritization of regulators for validation of their ability to modulate viral replication , ( 4 ) an examination of network dynamics across virus treatments , and ( 5 ) a further integration with external protein-protein interactions to predict directed physical connections between the mRNA , protein-based regulators and known influenza host response genes . The first component of our approach , a regulatory network inference algorithm , was needed because mammalian regulatory networks are incomplete for most biological processes . We used a recently developed network inference algorithm , 'Modular regulatory network learning with per gene information' ( MERLIN [22] ) that uses genome-wide mRNA levels from multiple biological samples ( time points or treatments ) to predict regulatory relationships between regulators ( e . g . transcription factors or signaling proteins ) and target genes . Our rationale for selecting MERLIN was to enable the study of host response regulatory networks both at the individual gene level and at the module level . Alternative methods either infer the regulators for individual genes [23] or for gene modules [13 , 24] , but not both . We applied MERLIN within a stability selection framework to the host transcriptional measurements of mouse lungs and human bronchial epithelial cells ( Calu-3 cell line ) infected with one of six influenza virus strains exhibiting a range of pathogenicity levels ( Materials and Methods ) . On the human Calu-3 cell line ( Fig 2 ) , we identified 41 consensus modules of at least 10 genes comprising a total of 4 , 801 genes ( ~67% of the input , Table 1 ) . On the mouse data , we identified 56 modules , encompassing 2 , 944 genes ( 41% of the input , Table 1 ) . The average expression patterns in each inferred module revealed commonalities and differences between strains and pathogenicity levels ( Fig 2 , Calu-3; S1 Fig , mouse ) . Based on a hypergeometric test with FDR correction ( FDR<0 . 05 ) , 32 out of the 41 human Calu-3 modules ( 40 of 56 mouse modules ) exhibited enrichment in one or more of the annotation categories representing Gene Ontology processes , KEGG pathways , and influenza related gene sets identified from 10 high-throughput RNAi studies and viral-host protein-protein interaction screens ( Fig 2 , S1 Fig , S1 Table , S2 Table ) . Moreover , 17 of the human modules were enriched specifically for immune response related processes ( Fig 3A ) suggesting that the modules were biologically coherent and relevant to immune response to influenza infections . Importantly , compared to ordinary expression-based gene clusters ( identified by Gaussian Mixture Modeling , 40 clusters ) , MERLIN modules exhibited greater fold enrichment in innate immune system categories and motif-based targets of transcription factors ( S3 Table ) . In particular , MERLIN modules had enrichment for targets of immune response-relevant regulatory elements IRF1 , 2 , 7 and ISRE and targets of inflammatory response regulator NF-kB , while expression-based clusters were not enriched or enriched at a lower level . In addition to the modules , we defined consensus regulatory networks for the Calu-3 and mouse transcriptome data by selecting regulatory edges with a confidence at least 0 . 3 ( Materials and Methods ) . The consensus networks predicted regulatory connections between 1 , 250 regulators ( signaling proteins and TFs ) and 7 , 132 target genes in human , and 1 , 252 regulators and 7 , 134 target genes in mouse ( S4 Table , S5 Table ) . Both Calu-3 and mouse regulatory networks were significantly enriched in transcription factor target interactions cataloged in MSigDB ( Fig 3B , Conserved Motifs ( MSigDB ) ) suggesting that the predicted regulatory-target connections are supported by sequence specific motifs . We also compared the inferred MERLIN mouse network to two additional networks: a pathogen-responsive regulatory network inferred from gene expression profiles after RNAi-based transcription factor knockdowns ( Fig 3B , Mouse pathogen , siRNA [5] ) , and a computationally constructed regulatory network for Th17 cellular response to LPS stimulation ( Fig 3B , Mouse Th17 , Yosef , [21] ) . The MERLIN mouse network significantly overlaps with both of these networks ( Fold enrichment > 1 . 5 , Fig 3B ) , indicating that MERLIN’s predicted regulatory network interactions are recapitulated in other immune response regulatory networks . To more directly test the predicted edges of MERLIN , we compared the predicted targets of four ( IRF7 , NMI , STAT1 , TCEB1 ) of our top ranked regulators using two published experimentally generated networks obtained from genome-wide expression profiles in single-gene knockdown siRNA screens in other cell lines [25 , 26] . We found significant overlap ( FDR<0 . 05 ) between the MERLIN and independently identified targets of three regulators ( IRF7 , NMI , STAT1 ) , suggesting that the edges predicted in the MERLIN network are associated with functional changes in expression . As a final type of evaluation we compared the extent of conservation of immune response of our two host systems to influenza infection ( Materials and Methods ) . At the module level , we estimated the significance of overlap of genes for all module pairs between the two species according to a hypergeometric test . We identified 15 pairs of modules that significantly overlapped in the gene content ( p-value<0 . 05 ) between the two species ( Fig 4A ) . The number of genes involved in this overlap was fairly modest , illustrating the challenges of integrating data from two separate organisms , with the mouse lung system being significantly more complex and heterogeneous than the human cell line system . Despite the low overlap in the number of genes , some of the cross-species module pairs shared enriched annotation categories . Specifically , Module 1549 in human and Module 3203 in mouse were both enriched for antigen processing and interferon-stimulated genes ( ISGs ) , and human Module 1549 also had a significant overlap with mouse Module 3003 , which was associated with cytokine signaling and innate immune system response . In another case , the mouse Module 3203 significantly overlapped with human Module 1484 , with both being enriched with hallmarks of the adaptive immune response , namely , antigen processing , B cell activation and leukocyte and lymphocyte activation . Analogously , at the network level , the networks overlap significantly ( hypergeometric test p-value<1e-4 , fold enrichment 2 . 9 , Fig 3B ) , including a core set of 96 interactions , 48 of which form subnetworks with at least 3 genes ( Fig 4B ) . This conserved regulatory network contained many key players from the interferon production and JAK-STAT pathways ( STAT1 , NMI , IRF7; [27 , 28] ) as well as regulators about which little is known , perhaps representing new relevant host processes . One conserved regulator with many conserved targets was ZNHIT3 ( also known as TRIP3 ) , a zinc finger HIT domain-containing protein that binds to thyroid hormone receptor [29] , but which is otherwise poorly characterized in the pathway databases . In summary , the enrichment of immune related functions , motif instances of known immune response TFs , agreement with existing computational and experimentally generated immune response related networks , and the conservation of the key immune related modules and networks between two distinct host systems is indicative of valid regulatory programs that can be explored with further experimental analysis . We used the MERLIN inferred networks to develop a regulator prioritization strategy wherein regulators were ranked according to the loss in the MERLIN model's predictive accuracy when a regulator was omitted from its targets' regulatory programs ( Materials and Methods; S6 Table ) . In comparison to three other ranking schemes ( outgoing regression weight , out-degree , and right eigenvector centrality; S1 Text ) , this strategy identified the most known influenza host genes among high-ranking regulators ( S2 Fig ) . We next used these rankings to guide our experimental validation ( Materials and Methods ) . We selected 20 regulators based primarily on the human rankings , the known annotations of the regulators , expression of the regulator’s module and to exclude well-studied immune response regulators ( IRF7 , NMI , STAT1 ) . To experimentally validate our network-based prioritization scheme , we measured H1N1 virus replication in human lung epithelial cells ( A549 ) following knockdown of predicted human regulators of host response by siRNA ( Materials and Methods , S7 Table ) . For each gene , four siRNAs were used , in order to mitigate off-target or cytotoxic effects of single siRNAs . We called a gene a high confidence hit using a stringent criteria requiring at least two siRNAs ( out of four used for each gene ) to yield a statistically significant ( t-test p-value <0 . 05 ) and high-magnitude ( 10 fold ) change in virus titer compared to negative controls , and if none of the siRNAs yielded a significant change in the opposite direction . Using these strict criteria , three out of the twenty tested regulators were called hits: BOLA1 , HCLS1 , and HOXA7 ( Table 2 ) . Three additional regulators were medium-confidence hits with > 5 fold change but significant and consistent effects in multiple siRNAs: FGFR3 , IRAK3 , and YTHDC1 . Knockdown of all six of the above consistently resulted in reduced virus titer , suggesting that the genes are important for virus production . Other tested regulators had multiple significant siRNAs , but conferred lower fold changes or showed divergent changes in viral titer between different siRNAs for the same gene . We note that some of the genes in our study for which multiple siRNAs had statistically significant but inconclusive direction or magnitude of effects were identified as hits by genome-wide screens ( S7 Table ) . While understanding the detailed role of these predicted regulators in viral replication will require further experiments , these results suggest that our network inference and prioritization method can successfully identify important regulators of host response . To integrate proteomic measurements with the host transcriptional response , we used a predictive modeling approach to identify proteins whose levels are predictive of the mRNA levels of gene modules . In theory , the MERLIN network inference algorithm could be used to integrate these proteins as additional regulators of a target gene’s expression levels . However , there were three reasons that prevented us from doing this . First , entire time courses of protein measurements were missing , and integration into the initial network inference step would require either extensive interpolation of entire time courses or excluding many mRNA measurements . Second , only ~20% of our candidate regulators ( signaling proteins and TFs ) with available mRNA levels were measured at the protein levels ( S4 Table , S5 Table ) . Third , compared to mRNA levels of regulators , protein levels were not good as predictors of target gene mRNA level , likely because of the smaller dynamic range of proteomic measurements ( S3 Fig ) . Our predictive modeling approach used a structured-sparsity based regression framework , called Multi-Task Group LASSO ( MTG-LASSO , Fig 5A ) [30 , 31] . Our approach is based on using group LASSO for multi-task feature selection [30 , 31] . Unlike LASSO [32] , which solves one regression problem at a time , MTG-LASSO aims to solve multiple regression problems simultaneously , one for each gene in a module . Our regression formulation has two properties: ( a ) multi-task regression ( where each task is the regression problem of each gene in a module ) and ( b ) group LASSO , to enable selection of the same regulators ( with possibly different regression weights ) for all genes in a module . LASSO-based approaches have been applied extensively for mRNA-based regulatory network inference [24 , 33]; however , to our knowledge , our MTG-LASSO approach is the first to employ grouping structure in order to integrate sparse protein-level data with comparatively higher-coverage mRNA-level data . A module-based regression enables us to pool information from all genes in the module to select regulators that are informative for all genes in the module . The MTG-LASSO regression problem is illustrated in Fig 5A . The full protein data matrix , X consists of m samples for p proteins . All p proteins are used as covariates . The target gene expression matrix for a module , Y is an m X n matrix , each column representing the expression profile of a gene in the module . The regression weight matrix , W is a p X n matrix , each row representing the regression weight of a protein for all n genes . In the group LASSO framework , a pre-defined grouping structure of covariates is used to select or de-select a group of coefficients together . We define a group as the set of regression weights for a single protein’s association to all module genes , resulting in p groups . The framework uses a mixed L1/L2-norm penalty to impose smoothness and sparsity: the number of proteins ( groups ) with any nonzero regression weights should be small , and the weights within a group should be similar . We compared the performance of the MTG-LASSO models to models learned from randomized protein data ( using one-sample z-tests; Materials and Methods ) . About half of the human modules , and all but two mouse modules , were predicted better than chance for multiple λ values . This result was consistent for both RMSE and Pearson correlation measures of predictive quality . From this analysis , we concluded that the protein data does indeed contain predictive signal for many modules; however , it should be used conservatively as predictive quality is not equally good across modules . The alternative to our MTG-LASSO approach is to perform traditional LASSO for each gene independently , ignoring the module structure . To assess the advantage of MTG-LASSO over regular LASSO , we applied both methods to each module separately . We compared the methods on the basis of ( i ) sparsity of the model measured by the average number of regulators chosen for a module across the 10 folds , and ( ii ) prediction quality measured by Pearson's correlation and the root mean square error ( RMSE ) between the measured and predicted set using 10-fold cross-validation . Both MTG-LASSO and LASSO have a regularization term , λ , which controls the tradeoff between the model complexity penalty and a model's predictive power . We examined five settings of λ , between 0 . 99 ( highest penalty imposed , requiring few regulators ) and 0 . 01 ( least penalty imposed , allowing many regulators ) . The regularization term λ is a number between 0 and 1 . It denotes a fraction of λmax ( maximum possible regularization before reaching a 0 solution ) , and is comparable between MTG-LASSO and LASSO ( see Materials and Methods ) . First , we observe that LASSO identified more regulators per module at every value of λ compared to MTG-LASSO ( Fig 5B , S4A Fig ) . Second , when matched at the same λ , MTG-LASSO and LASSO yielded highly similar predictive quality scores per module based on both Pearson correlation and RMSE values ( Fig 5C , S4B and S4C Fig ) . The comparable predictive power of MTG-LASSO is observed over all modules and values of λ compared ( two-sided Kolmogorov-Smirnov test: human p-value = 1 . 0 Pearson , 0 . 98 RMSE; mouse p-value = 1 . 0 Pearson , 0 . 58 RMSE ) . The proteins selected by MTG-LASSO were contained within and important to the LASSO models . In particular , when the LASSO proteins were ranked by their average absolute outgoing weight for the same λ , the MTG-LASSO regulators appeared at the top of the list . We quantified the ranking by the area under ROC curve ( AUROC ) , treating MTG-LASSO as the positive class and the additional LASSO regulators as the negative class . The AUROC gives the probability that a randomly chosen MTG-LASSO regulator ranks above a randomly chosen LASSO regulator . For human , AUROC ranged between 0 . 96–0 . 99 , and for mouse 0 . 91–0 . 98 when considering all genes together . The high ranking of MTG-LASSO regulators in the LASSO selected regulators is observed on a per-module level as well ( S4D and S4E Fig ) . Because MTG-LASSO learned a subset of the LASSO regulators , we asked if the regulators that were identified by LASSO but not MTG-LASSO were known to be important based on existing siRNA screening studies . Focusing on modules that were predicted better than random ( 11 in human and 36 in mouse ) , we defined consensus regulators per-module as regulators that were selected in at least 6 of 10 folds of cross-validation after determining a module-specific value of λ ( Materials and Methods , Fig 5D and 5E ) . Our analysis identified a total of 17 human consensus protein regulators for 11 modules in human ( 1–6 protein regulators per module , Table 3; selected regulators shown in Fig 3A ) and a total of 33 consensus protein regulators predicted for 36 modules in mouse ( 1–6 regulators per module , Table 4 , S8 Table ) . A similar analysis for LASSO identified 60 different regulators for human and 79 for mouse , which contained the MTG-LASSO regulators entirely ( S9 Table ) . Comparable proportions of the consensus regulators identified by each method have been found as hits in published influenza screening studies ( for human , MTG-LASSO 23 . 5% and 18% LASSO; for mouse , MTG-LASSO 18% and LASSO 20% ) . Because the MTG-LASSO regulators were included in the LASSO rankings we also computed the precision after excluding the MTG-LASSO regulators . In human the precision was markedly lower ( 16 . 3% ) , and slightly lower in mouse ( 19 . 6% ) . Thus for the human cell line data , there is a distinct advantage of using MTG-LASSO to quickly identify the important regulators . The mouse data is more challenging likely due to the heterogeneous nature of the cell populations . Together , these results suggests that MTG-LASSO is able to learn as good a predictive model as regular LASSO , and is particularly advantageous for identifying regulators at the module level . Below we further experimentally validate the MTG-LASSO regulators . We first qualitatively examined our regulators based on known literature and annotation of these regulators . Several of our MTG-LASSO protein regulators are known to be associated with immune response pathways , cellular membranes and intracellular transport , RNA splicing machinery , mitochondrial inflammation , and may be involved in viral replication , entry or transport within the cell ( Tables 3 and 4 ) . We experimentally validated seven of the 17 human MTG-LASSO regulators for which we already had siRNA libraries ( Table 5 , S7 Table ) , transfecting cells with 4 siRNAs per regulator . For all seven regulators at least two of the four siRNAs resulted in a significant change in virus titer , and had at least one siRNA resulted in a fold-change far beyond 10-fold . Unlike in the mRNA case , where most of the significant and high magnitude changes were in one direction , for several of the protein regulators , different siRNAs targeting the same gene resulted in both significant and high magnitude increase and decrease in viral replication . We therefore used a majority rule on the significant changes to phenotypically characterize the effect of knockdown of a particular protein regulation . Specifically , a gene was called “pro” viral replication if the number of significant decreases in viral replication was greater than the number of significant increases . Similarly , a gene was called “anti” viral replication if knocking it down resulted in more significant increases than decreases in viral replication . Among the seven proteins that were tested , four were classified as “pro” viral replication with more significant decreases in viral replication ( HIST1HB , ISG15 , PRPF31 , THBS1 ) , while three were called as “anti” viral replication with significant increase in viral replication ( APP , ITGB4 , SERPINA3 ) . PRPF31 , which had the strongest , consistent effect across all significant siRNAs , is a pre-mRNA splicing factor that was previously implicated in viral replication by a genome-wide screen . It is known that the host RNA splicing machinery is hijacked by influenza virus [34] . ISG15 is a ubiquitin-like protein that is stimulated by interferon alpha and beta and is associated with diverse cellular functions including cell-to-cell signaling and anti-viral activity . Hence , ISG15’s pro-viral phenotype was surprising . However , this protein has been observed to be attached to both host and viral proteins [35] and has been previously shown to reduce viral replication on knockdown in a previous study [36] . THBS1 is a ligand of CD47 and an inhibitor of T and dendritic cells and may play a role in inhibiting inflammation [37] . SERPINA3 and APP in particular are interesting candidates as potential inhibitors of viral replication . The serine protease inhibitor SERPINA3 ( module 1484 ) and its mouse homologs Serpina3k ( two modules ) and Serpina3m ( eight modules ) were identified independently for both species . SERPINA3 belongs to the family of serine protease inhibitors that have diverse roles in innate immunity [38–40] . While SERPINA3 has not been shown to affect viral replication , another member of this family , SERPINE1 , was shown to reduce the infectivity of the virus particle [41] . APP has been mostly studied for its role in neurodegenerative diseases: beta amyloid plaques are associated with neuronal cell death in Alzheimer’s Disease [42] but a recent study also suggests it may have an antiviral role , based on observations of decreased influenza A replication ( H1N1 and H3N2 ) in cells treated with beta amyloid [43] , consistent with the increase in viral titer observed in this study . Taken together , our results indicate that MTG-LASSO can identify regulator candidates that are relevant to immune responses to viral infections . Overall MTG-LASSO's ability to highly rank the most relevant regulators is an important factor for tractable downstream interpretation and validation of selected regulators . We next used the integrated mRNA and protein-based networks to examine the temporal dynamics of host response . This type of analysis can be used to predict which network components may be repressed or differentially wired in response to different viruses . We built time point-specific active networks by overlaying time point-specific expression on the integrated regulatory network connecting both mRNA and protein regulators to their target genes . For each time point and virus , we obtained a time point-specific regulatory network by including inferred edges between regulator nodes ( mRNA or proteins ) and target genes ( mRNA ) that were significantly up-regulated ( z-score ≥ 2 , compared to all expression values at that time point ) . For this analysis we focused on the Calu-3 data set . Examination of the active network size ( number of edges , regulators and targets ) over time showed that the size of networks for each viral treatment tended to change by adding more edges over time ( S5 Fig ) , with the H5N1 mutants and wild-type achieving the largest networks . Interestingly , H1N1 NL's network grew more slowly than that of H1N1 CA04 ( S5 Fig ) , although the networks for the two viruses were highly similar in terms of which nodes and edges they contained by the last time point ( S6 Fig ) . To identify network components that were common to a subset of viruses or time-points , we clustered the edges from the active networks according to their presence-absence pattern across all samples ( Materials and Methods , Fig 6 ) . We obtained five clusters of edges ( Fig 6A ) four of which included edges active at multiple early time points ( Fig 6A; Clusters A-D ) and a fifth containing edges that were only present in the 18-hour ( very late ) time points for medium or high-pathogenicity viruses ( Fig 6A; Cluster E ) . Further , Clusters A-C exhibited a sustained pattern of edge presence with edges present through the entire time course , while Cluster D was associated with edges present during the later time points . Clustering was strongly driven by presence-absence pattern of edges in viruses rather than individual time points . Edges from the two low-pathogenicity H1N1 strains ( CA04 and NL ) tended to be placed in the same cluster ( Fig 6 , Clusters A , B ) , and edges from the medium-pathogenicity ( PB2-627E ) and two high-pathogenicity strains ( PB1-F2Del , H5N1 wildtype ) did as well ( Cluster B , C , D ) . The networks for the medium and high pathogenicity mutants additionally distinguished activation responses that occurred at later time points ( Cluster A , D ) from those that were present at all time points ( Clusters B , C ) . To identify specific processes that were associated with these edge clusters , we tested them for enrichment of pathways ( Table 6 , Materials and Methods ) . Cluster A was significantly enriched with immune response processes , namely anti-viral and interferon response . This cluster was active for H1N1 infections as well as H5N1 NS1trunc . Predicted regulators in this cluster included key immune response regulators at the mRNA ( e . g . IRF7 , STAT1 , TRIM21 , NMI ) and the protein level ( ISG15p ) . In terms of pathogenicity , the NS1trunc virus is more lethal than the H1N1 strains; however , truncated NS1 protein disrupts the virus' ability to effectively suppress host immune response . The H5N1 NS1trunc mutant in particular displayed an interesting clustering pattern across the other clusters . It was represented in two clusters that represent late response to other medium- and high-pathogenicity viruses ( Clusters D , E ) , but absent from a cluster that represents early-onset sustained edges among H5N1 strains ( Cluster C ) as well as from Cluster B , which included edges from all other viral strains . Cluster B and Cluster C , which are associated with the two highly pathogenic viruses are associated with GPCR signaling and muscle contraction and include some of our siRNA based regulators ( YTHDC1 , Cluster B ) and ( HCLS1 , HOXA7 , Cluster C ) . Both Clusters D and E , which include active edges primarily from the high and medium pathogenicity viruses , are associated with signaling pathways including Wnt signaling and the circadian clock . Wnt signaling has been implicated in influenza infections [14 , 44] , while disruptions in circadian clocks have been shown to increase susceptibility of the immune system to infections [45] . In summary , our active network analysis identifies different subnetwork components that are specific to different viruses , and implicates additional processes that might be relevant in a strain-specific manner . Our analyses thus far inferred two sets of regulators for each module: ( 1 ) mRNA-based regulators , signaling proteins and TFs , predicted by MERLIN based on transcriptome changes , and ( 2 ) protein-based regulators predicted by MTG-LASSO based on proteome and transcriptome changes . Both these types of regulators were predicted based on patterns of co-variation of regulators and targets ( at the individual gene or module levels ) . What is missing is information about the underlying physical mechanisms that connect the signaling proteins to transcription factors , and the protein-level regulators to the mRNA-level regulators . To gain insight into the underlying physical mechanisms that connect the regulators , and to link them to host genes identified from functional screening studies , we integrated the predicted regulators with physical protein-protein interactions , transcription factor-target interactions , and metabolic reactions from multiple public databases ( Materials and Methods ) . Typically , the interaction data do not provide evidence for direct interactions between regulators , requiring the identification of additional intermediate nodes that connect these regulators . However , even allowing only one intermediate node can result in large subnetworks that are unwieldy for interpretation ( see for example Fig 7A ) . Furthermore , because available interactions are not condition-specific , they may contain false positive interactions and may be missing relevant connections . To identify high-confidence physical subnetworks that are easily interpretable , we formulated a constrained optimization problem to find directed paths that connect MERLIN-identified signaling proteins to transcription factors and MTG-LASSO-identified proteins to MERLIN regulators using a minimal set of intermediate nodes ( Materials and Methods ) , prioritizing the inclusion of host genes that were recently identified by Watanabe et al . [46] using both RNAi and co-immunoprecipitation with viral proteins . Such nodes are important in the virus-specific protein-protein interaction network and provide additional context for interpreting our predicted transcriptome and proteome-based regulators . We applied an Integer Linear Programming ( ILP ) approach to solve this optimization problem , which has been shown to find more precise solutions compared to heuristic algorithms [47–49] . Using our ILP approach we identified high-confidence physical subnetworks for 16 human modules , which included between 0–8 intermediate nodes , including a subset of the Watanabe hits and protein interaction partners ( S2 Text , S1 Table , Supporting Website ) . Nine of the module subnetworks included predicted protein regulators . The high-confidence subnetworks were able to connect more true regulators with fewer intermediate nodes than subnetworks inferred from random regulators with the same degree distribution , suggesting that the sparsity of the high-confidence physical subnetworks was not merely due to the degree of the input regulators in the physical interaction network ( S3 Text ) . We also used the random input subnetworks to compute an empirical FDR for each protein by measuring the frequency at which the protein appears randomly , and found that it ranged from 0–0 . 4 , demonstrating that many of the proteins used in the subnetworks are unlikely to be identified by chance ( Materials and Methods ) . A low FDR is a conservative measure of a protein's importance , as many relevant proteins are network hubs and are likely to be identified by chance . These modules , together with their subnetworks , provide an integrated view of different types of regulators that are interacting to drive the downstream expression pattern of the host response . We discuss several below and provide all others on a Supporting Website . Our integrated network analysis identified several modules that captured different components of the host immune response machinery . One of these modules was Module 1549 , which was associated with immune response-related processes by many of our computational analyses ( Figs 7 and 8 ) . Module 1549 exhibited a particularly interesting strain-specific pattern of induced expression under infection with low-pathogenicity viruses and the medium pathogenicity virus , H5N1-NS1trunc , and repressed expression under infection with high pathogenicity viruses ( Fig 8 ) . Module 1549 is also enriched for genes associated with interferon signaling , which are critical for mounting the innate immune response . The influenza NS1 ( 'non-structural' ) protein is already known to inhibit the host's antiviral type I interferon response [50] , suggesting that this module would be a good candidate for further investigation into the mechanism of action of NS1 . Furthermore , the genes in Module 1549 overlapped significantly with two mouse modules ( Fig 4A ) , and its regulators featured prominently in the conserved regulatory network identified by the intersection of the human and mouse consensus networks ( Fig 4B ) . This module is associated with NFS1 , APP , SERPINA3 , and ITGBP protein regulators , and IRF7 , NMI , and STAT1 mRNA regulators , which are well-known members of the interferon response and JAK-STAT antiviral response pathway [27 , 28] . The subnetwork analysis applied to the regulators of this module highlighted HSPA4 , also known as Hsp70 , as a hub that connects gene members from multiple immune response pathways ( Fig 7B ) . HSPA4 has been proposed as both an antiviral factor [51] as well as a chaperone required for viral replication [52] . HSPA4's direct subnetwork connections include members and modulators of the antiviral JAK-STAT pathway ( ISG15[53] , FGFR3 , STAT1 ) and also inflammation ( APP ) . FGFR3 was a hit in our MERLIN-network based prioritization siRNA study and acts as a modulator of the JAK-STAT pathway in growth disorders such as achondroplasia ( OMIM ) . APP was identified as an inhibitor of viral replication in our siRNA validation . Other important genes in this subnetwork are the protein regulator THBS1 and an mRNA-based regulator , MLKL . THBS1 is involved in apoptosis [54] and potentially inhibiting inflammation [37] , and our siRNA validation results classify it as a pro-viral replication gene , while MLKL induces necroptosis ( inflammatory cell death; [55] ) . As a whole , the subnetwork for Module 1549 ties together multiple immune response pathways , namely , antiviral interferon signaling , inflammation and apoptosis . In contrast to Module 1549 , Module 1540 ( Fig 9A ) shows a pattern of repressed expression in response to low-pathogenicity viruses , and increased expression over time in response to high-pathogenicity viruses . The subnetwork analysis revealed connections between the mRNA ( ANKDRD2 , SET ) and protein-based regulators ( HIST1HB1 , THBS1 ) of this module via intermediate nodes , SIRT1 and ELAVL1 and the Watanabe host gene , TP53 ( Fig 9B ) . The SIRT family of proteins have been identified as antiviral factors for multiple viruses [56] . The subnetwork suggests that part of its antiviral activity is mediated through interactions with anti-apoptotic signaling protein SET [57] as well as through direct interactions with histones . The other proteins in the subnetwork have roles in apoptosis and the p53 pathway: TP53 ( p53 ) itself , ELAVL1 ( which stabilizes p53 mRNA; [57] ) , ANKRD2 [58] , and THBS1 . In summary , Module 1540 identified interactions between histone proteins and various pro- and anti-apoptotic factors , some of which may explain the independently observed antiviral activity of SIRT1 . A third characteristic pattern was exhibited by Module 1472 ( S7 Fig ) . Genes in this module were associated with a general pattern of induced expression but differed in the intensity of induction ( stronger induction in the high pathogenicity strains compared to the low pathogenicity strains ) . This module was predicted to be regulated by several transcription factors ( ANKRD2 , EMX1 , EN2 , FOXC2 , SOX17 , RLX2 , ZNF205 ) and two signaling proteins , GRIN1 and SIK1 . SIK1 is a protein kinase which is involved in phosphorylation of HDACs ( histone deacetylases ) which can in turn modulate innate anti-viral responses [59] and interferon signaling genes [60] . Moreover , the subnetwork analysis linked SIK1 to the predicted protein regulator THBS1 ( an inflammation inhibitor ) through the intermediate node FYN , a tyrosine kinase with potential role in NFKB-mediated adaptive immunity . Finally , a fourth module of interest was Module 1482 ( S8 Fig ) , which exhibited a pattern of high expression in the low pathogenicity viruses compared to both high and medium pathogenicity viruses . Both the candidate and minimal subnetworks for this module provided useful information for generating hypotheses about mechanistic interactions between the regulators . The module was associated with both mRNA and protein regulators as well as hits from the Watanabe study: SNW1 , which interacts with viral NS1 protein , and PSMC1 , which interacts with viral HA , M1 , NA , PA , PB1 , and PB2 proteins . The subnetwork analysis connects predicted mRNA regulators ( SRC , BTRC , PPM1F ) and protein-based regulators ( THBS1 , EHD4 ) through SNW1 and PSMC1 ( S8C Fig ) . EHD4 is a regulator of endocytosis [61] , suggesting a role in virus entry , and SRC is a tyrosine kinase that has been identified to be involved in antiviral signaling [62] . Taken together , our integrated analysis identified the major regulatory modules of host transcriptional response and predicted mechanistic regulatory programs associated with these modules . The modules exhibited pathogenicity or strain-specific patterns and were enriched in immune related processes and predicted to be regulated by genes from diverse pathways including innate immune response and apoptosis . These module case studies support our regulators as important players of host response and provide an integrated view of how host response may be regulated at multiple levels , from mRNA , to protein , to interaction networks .
Identification of the molecular networks that underlie host response to different pathogenic infections is important to understand both the mechanisms of immune response as well as to design better therapeutics . Towards this end , we performed an integrative regulatory network-based analysis that combines transcriptomic , proteomic and existing molecular interaction datasets to identify important genes , modules and subnetworks . We found that the key components of innate immune response processes namely , interferon production and signaling , and important regulators of immune response ( STAT1 , NMI , IRF7 ) , were conserved between a human cell-line ( in vitro ) and mouse lung ( in vivo ) model system . Our approach was able to predict novel regulators at the mRNA and protein level and implicate molecular pathways that may drive virus-specific host responses . Prioritization of predictions , including important genes , interactions and networks , is important in systems biology studies , which can easily generate a large number of hypotheses . While a large number of prioritization methods , including network-based strategies , have been proposed [63] and computationally validated , relatively few have been used to inform experimental validation in a medium to high-throughput manner [21 , 64] . Towards this end we used our inferred MERLIN networks to prioritize important regulators of host response and test 20 regulators using siRNA . Six of the 20 regulators exhibited highly significant and consistent effects on viral replication and included 4 novel mRNA regulators ( BOLA1 , HOXA1 , HCLS1 , FGFR3 ) in addition to IRAK3 and YTHDC1 , which were identified by genome-wide studies using a different influenza virus [65 , 66] . Mice lacking IRAK3 have an increased mortality rate compared to wild type in influenza-induced pneumonia [67] . BOLA1 is a mitochondrial protein , which helps maintain mitochondrial morphology and oxidative stress [68] . Mitochondria provide important innate immune functions , including cellular response to double-stranded viral DNA by induction of cytokines through the MAVS ( mitochondrion antiviral signaling ) protein [69] . Influenza A virus proteins have also been shown to translocate to mitochondrial membranes and promote mitochondrial fragmentation [70] . HCLS1 , which is a hematopoetic lineage specific protein [71] , is also interesting due to its role in signal transduction pathways in B and T cells [72 , 73] . HOXA7 is a member of the homeobox family of transcription factors , known to have critical roles in differentiation and embryonic development . The HOXA7 gene has to our knowledge not been associated with specific immune related functions , however , a closely related gene , HOXA9 was shown to be involved in lymphoid and B cell development , which are important cell types of the immune system [74] . The HOXA7 gene was also shown to code an antigen in specific tumor types [75] and could be involved in differentiation programs of immune cell types in response to influenza infections . Finally , FGFR3 , a fibroblast growth factor receptor gene is known to have diverse roles in multiple cellular functions [76–78] . The FGFR1-4 genes were investigated for their role in influenza A viruses [79] . FGFR1 was shown to significantly impact cellular internalization of two influenza A viruses but FGFR3 was not expressed in the cell line tested , leaving open the possibility of its potential role in the influenza life cycle . To our knowledge , these regulators have no previously known role in influenza response , but serve as promising leads for further in-depth validation studies using in vivo models . While approaches to infer and examine networks from mRNA are routinely used in systems biology studies of complex responses [2 , 6 , 80] , examining proteomic datasets and especially integrating them with transcriptomic data to gain insight into regulatory mechanisms is an open challenge that has been addressed by relatively few approaches [7 , 49 , 81] . This is because , unlike mRNA levels , proteomic technologies are still maturing and datasets have lower genome coverage and higher frequency of missing values [82] . To tackle this challenge we introduced a novel structured sparsity inducing approach , Multi-task Group LASSO ( MTG-LASSO ) , which enabled us to leverage the overall signature of expression at the level of modules . Our experiments confirmed that while the MTG-LASSO and LASSO achieved comparable prediction performance on held-aside data , the MTG-LASSO approach identified a sparser set of regulators per module . Proteins with the strongest contributions to module expression prediction were involved in innate immune response pathways , RNA splicing , membrane organization and transport , that are relevant to different parts of virus life cycle . Experimental validation of these regulators further associated pro or anti-viral replication functions with them . Both directions are interesting from the point of view of understanding the mechanisms of immune response as well as for designing vaccines that could disrupt viral replication and growth . PRPF31 , which is involved in RNA splicing , was particularly notable as an example of a pro-viral replication regulator , given the emerging role of post-transcriptional process in diverse pathogenic infections including bacterial pathogens [83] . SERPINA3 ( anti-viral , serine protease inhibitor ) is also interesting as a candidate anti-viral drug target due to the known roles of serpins in innate immunity [38–40] . We also predict a pro-viral role of ISG15 , an interferon stimulated protein and that is involved in diverse processes including anti-viral activity . Importantly , several predictive protein regulators were not identified as differentially expressed at the mRNA level ( 7/17 for human; 22/33 for mouse; S4 Table , S5 Table ) , emphasizing the importance of measuring multiple types of cellular components . These results were further bolstered with our subnetwork analysis that combined the mRNA and protein regulators through physical interactions ( identified as interaction partners in the physical subnetwork ) . Even though no information about known relevant pathways was provided as input , our approach was able to give interaction-driven predictions for how these different regulators are coordinated . A notable example was a newly identified gene , HSPA4 , in module 1549’s regulatory program , that connected FGFR3 ( discussed above ) , STAT1 ( a member of the JAK-STAT signaling pathway ) , and inflammation and cell death pathways ( represented by THBS1 and APP ) . Without the subnetwork analysis , it would not be possible to identify HSPA4 , as it was not part of the input set of mRNA and protein regulators . Our approach identified several important modules that exhibit strain and pathogenic specific patterns of expression . In particular , Module 1549 , which was associated with interferon signaling and interferon-stimulated genes , exhibited a striking pattern of differential expression of repression in the wild-type H5N1 virus . Our results are consistent with those of [84] , who observed a differential pattern of expression of the ISG genes and showed that the differential expression of these genes were inherently tied to host response . Another module , Module 1540 exhibited an opposite pattern of expression and is associated with cell-cell signaling . One caveat to the validation was that we tested the siRNAs only in pandemic H1N1 and therefore we do not know how impact of these regulators in different virulent strains . The physical regulatory programs together with the expression phenotype associated with these modules enables us to make intriguing hypothesis of potential mechanisms by which upstream regulatory networks drive context-specific expression , which could be followed by further validation . Some of the same mRNA time courses have been previously studied with computational network approaches [8 , 85] , offering additional points of reference against which to compare our inferred multi-virus regulatory network . Mitchell et al . [8] prioritized influenza host genes from wild-type H1N1 and H5N1 samples . They learned a correlational network from which they identified central nodes as well as modules . They also used a regularized regression approach ( Inferelator , [24] ) to identify sparse regulator sets that predict the average module expression . Our top predicted regulators ( both protein and mRNA ) intersected with theirs on only one gene ( NMI ) . The limited overlap may not be surprising as the bulk of our prioritized regulators were restricted to transcription factors and signaling proteins; however , similar gene families were present in our list and theirs ( including DDX , HOX , and ISG genes ) . Additionally , McDermott et al . [85] previously analyzed the H5N1 mRNA time courses using a similar approach: first identifying modules through hierarchical clustering , followed by identification of regulators for the modules' average expression . That study identified a set of conserved clusters across human , mouse , and macaque , and a list of prioritized regulators . We found significant overlap of several of those clusters with several our modules ( S9 Fig ) , but no overlap in the presented prioritized regulators . However , both our study and theirs identified shared functional processes ( cytokine signaling and production , inflammation , apoptosis , and cell cycle regulation ) and gene families ( IRF genes ) . Our work can be extended in several ways . One limitation of the current subnetwork approach is that the protein-protein interactions employed are not necessarily functionally relevant to the tissues or conditions under study; a future direction of work is to integrate tissue-specific interactions at this step , such as from large-scale computationally-inferred compendia [86 , 87] . Another direction of future work is to jointly learn modules and their physical regulatory programs using an iterative framework while integrating proteomic measurements . We anticipate that as systems biology studies expand to more viruses , host systems and diseases , approaches such as ours are going to be increasingly useful to characterize host responses at multiple omic levels , prioritize genes and subnetworks for validation . The outcomes from such studies will be important to assemble a comprehensive picture of the mechanisms responsible for healthy and disease states , and ultimately guide the design of effective therapeutics .
We obtained background corrected and between-arrays quantile normalized host mRNA response data from multiple strains and dosages of influenza virus in Calu-3 human cells ( GEO Accessions GSE28166 , GSE37571 , GSE40844 , GSE40844 , GSE43203 , GSE43204 ) and 20-week old C57BL/6 mice ( GSE33263 , GSE37569 , GSE37572 , GSE43301 , GSE43302 , GSE44441 , GSE44445 ) ( full details , [4] ) . All experiments were performed using Agilent microarrays . In the original work , each array was subject to quality control , background correction , and quantile normalization . We directly used the processed data available . The viruses include three wild-type and four mutant strains . Wild-type strains include A/California/04/2009 ( H1N1 ) , A/Netherlands/602/09 ( H1N1 ) , and A/Vietnam/1203/2004 ( H5N1 ) . The mutant strains of H5N1 each affect different aspects of the virus life cycle [1] . HAavir , used in the mouse experiments only , has restricted tissue tropism due to a mutated cleavage site in the hemagglutinin glycoprotein . The wild-type H5N1 virus has a lysine at position 627 in the PB2 polymerase protein that is associated with the adaptation of H5N1 viruses to mammals . Mutation of this amino acid to glutamic acid ( PB2-627E ) reduces polymerase activity in mammalian cells and pathogenicity in mice . NS1trunc has a shortened version of the NS1 protein , thereby interfering with the virus' ability to suppress host antiviral responses through the RIG-1 pathway . PB1-F2del is missing viral protein PB1-F2 , which is involved in many aspects of virus pathogenicity , including polymerase activity and host immune regulation . For each virus infection in human cell line , six or nine time points were collected , spanning 48 hours for low-pathogenicity viruses ( at 0 , 3 , 7 , 12 , 18 , 24 , 30 , 36 , 48 hours ) and 24 for medium and high ( at 0 , 3 , 7 , 12 , 18 , 24 hours ) . Each time point had at least three biological replicates . An additional four-time-point replicate series was collected for H1N1 ( hours 0 , 12 , 24 , 48 ) and two additional two-point series were collected for H5N1 ( hours 7 , 24 ) . In the mouse system , multiple dosages were available for some viral treatments . All time points were taken at days 1 , 2 , 4 , and 7 after infection , with the exception of the highest dosage of H5N1 , which omitted day 7 due to complete lethality . We collapsed replicates of each time point using the median value of a gene’s expression level . In total , after collapsing replicates , there were 50 samples per human gene and 51 per mouse gene . Using MLD50 values [1] , we classified the viruses into high , medium and low pathogenicity groups: high pathogenicity included WT H5N1 and H5N1-PB1-F2del; medium pathogenicity included H5N1-NS1-trunc and H5N1-PB2-627E , and low pathogenicity included H5N1-HA-avir and H1N1 . Instead of HAavir , the Calu-3 data included a different strain of wild-type H1N1 ( A/Netherlands/602/09 , or NL ) ; both viruses have the same low level of pathogenicity . Having viruses exhibiting similar extents of pathogenicity enabled us to perform a systematic comparison of host response divergence under the same type of perturbation . Because our focus was to compare findings between in vivo ( mouse ) and in vitro ( human cell lines ) we started with genes that were conserved ( had orthologs ) between human and mouse , and exhibited differential patterns of expression between high , medium and low pathogenicities . We first obtained the relative expression value of a gene to the same gene's expression in an untreated mock sample from the same time point . We included a gene if its relative expression profiles compared to mock in either species were significantly different between any two pathogenicity groups ( assessed by t-test , p-value<0 . 01 for human and p-value<0 . 05 for mouse ) . The resulting gene set comprised 7 , 192 genes in the human cell line and 7 , 240 genes in mouse lung . As regulators , we selected transcription factor and signaling proteins [5 , 57 , 88] that were present in the differentially expressed gene set . This included a total of 1 , 396 encoded candidate regulators in human and 1 , 394 candidate regulators in mouse . We used protein level data that was available for a subset of the same samples as the mRNA data from [1 , 3 , 4 , 8 , 89] . Briefly , peptide-level abundances were obtained by liquid chromatography—mass spectrometry ( LC-MS ) and matched to protein levels following normalization and quality control . For human , the H1N1 NL time course and a short replicate time course of H1N1 CA04 were missing ( see gray boxes in protein regulator heatmap at the bottom of Figs 8 , 9 , S7A and S8A ) ; for mouse , the missing samples spanned H5N1 HAavir and two of four dosages of H1N1 CA04 . These data provided 37 unique samples for human and 42 for mouse . We selected proteins with fewer than 50% missing values ( resulting in 3 , 026 for human and 1 , 908 for mice ) , and imputed remaining missing values using the mean value for existing samples ( within the same time course ) . To prepare data for input into the MTG-LASSO method , we normalized both protein and mRNA data by their row means . We used MERLIN [22] , a network inference algorithm , to learn regulatory module networks for the human and mouse datasets separately . The input of MERLIN is a matrix of gene expression data and a list of candidate regulators ( e . g . , transcription factors and signaling proteins ) ; the output is a regulatory network and a set of regulatory modules . This dual output is a unique feature of MERLIN compared to other network inference methods . It uses an iterative procedure that alternates between learning the network structure using a greedy search for regulators of individual targets ( giving a regulatory program per gene ) and performing hierarchical clustering on the target genes based on both co-expression and the similarity in their current assigned regulator sets . The module assignments are also used in the network learning step to provide a prior preference for adding regulators to a gene's regulatory program if the regulator is already assigned to another gene in the module . We embedded MERLIN in a stability selection framework [90] , in which MERLIN networks are learned independently for 40 random sub-samples of the expression data . Each subsample consisted of about 90% of the total samples ( 45/50 for human , 45/51 for mouse ) . The resulting ensemble of networks provides a confidence value for each edge in the regulatory network , thereby enabling the identification of a robust consensus regulatory module network . For each individual network , we set MERLIN's three parameters according to recommendations from the original publication based on simulated data [22] , setting p = -5 , r = 4 , h = 0 . 6 . The parameter p controls regulatory network sparsity ( more negative values , fewer edges ) , r controls network modularity ( higher values , stronger preference for sharing regulators in a module ) , and h specifies a threshold on the distance used to cut a hierarchical clustering into gene modules ( lower values , more modules ) . We derived a consensus regulatory module network in several steps from the ensemble of networks that were produced under stability selection . First , to derive a consensus network of regulator-target edges , we applied a threshold of 0 . 3 confidence to the confidence-weighted regulator-target edges produced by stability selection . This threshold was picked based on its FDR , assessed by comparing to a random consensus regulatory network generated by running the approach on 40 randomizations of the expression data . We calculated FDR as the ratio of the fraction of edges from the random network that would be accepted at the threshold , over the fraction of edges from the true network that were accepted by the threshold . The FDR of human and mouse networks were 0 . 30 and 0 . 17 , respectively . Next , to independently derive co-expressed , co-regulatory modules , we began by hierarchically clustering the genes , defining the similarity between any pair of genes as the frequency at which the two genes were clustered together across the 40 separate module assignments . We then applied a distance threshold of 0 . 5 on this new clustering to define consensus modules . Finally , we identified consensus regulators for each module ( at the module level ) by assessing the significance of overlap of each regulator's consensus targets within each consensus module , as measured by the hypergeometric test ( FDR < 0 . 05 ) . We evaluated the modules based on their enrichment with various sources of gene sets and pathways . We call each gene set or pathway an annotation category , and performed enrichment testing independently on groups of annotation categories coming from the same source . To assess significance of the enrichment of the modules with an annotation category , we computed a hypergeometric p-value specifying the probability of observing k or more genes from a module with n genes to have an annotation a , given that there are a total of M genes with annotation a among a total of N genes . Considering together all of the annotation categories for a module , we applied the Benjamini-Hochberg procedure to control FDR at 0 . 05 and accepted corrected p-values < 0 . 05 . The groups of annotation categories that we tested include Gene Ontology Biological Process [91] , targets of transcription factors from MSigDB [92–94] as well as those determined by scanning the promoters of genes using known motifs in the JASPAR database [95] with FIMO [96] . We additionally included gene sets available from MSigDB , Reactome [97] , BioCarta ( http://www . biocarta . com ) and KEGG [98] . In addition to the above general curated pathways , we also used experimental , literature-based , and manually curated gene sets that were specifically associated with influenza and with innate immune response . We assembled hit sets from a group of RNAi and protein-protein interaction screens [14 , 36 , 46 , 65 , 66 , 99–103] . We also created an immune response group of annotation categories consisting of Calu-3 interferon stimulated genes [84] , curated targets of the NF-kB transcription factor ( http://www . bu . edu/nf-kb/gene-resources/target-genes ) , genes differentially expressed in response to inflammatory interleukins IL-1 or IL-6 [104] and members of curated immune response pathways from InnateDB [105] , downloaded November 2014 ) . For the influenza and immune response gene sets , we obtained human-mouse gene orthologs from the Mouse Genome Database [106] . We used a hypergeometric test and a fold enrichment to assess the significance of the overlap in edges between the MERLIN consensus regulatory network and other immune response and transcriptional regulatory networks described in Fig 3B ( MSigDB motifs , [92] Mouse pathogen ( Amit ) [5] , Mouse Th17 ( Yosef ) [21] ) . We refer to the MERLIN network as the "query" network , and the other network as the "test" network . Because the networks were directed , we first defined the shared universe of regulators and the universe of targets as the intersections of the respective node sets from the two networks . Then , we defined the shared universe of edges as all possible edges between regulators and targets . The size of the universe , u , is calculated as the product of the numbers of regulators and targets in the universe . We measure overlap , o , as the number of edges in the universe that are common to both query and test . We measure the size of the query and test networks , q and t respectively , as the number of edges in each network restricted to the shared universe . To test significance of the size of the overlap , we use the hypergeometric distribution to assess the probability of identifying o or more overlapping edges in a random draw of q edges from a universe of size u that contains t test edges . Fold enrichment is defined as the ratio of observed to expected fraction of edge overlap between the two networks , or ( o/q ) / ( t/u ) . A module was considered to exhibit an interesting strain or pathogenicity-specific pattern of expression ( module catalogs , S1 Table , S2 Table ) if the mean expression of genes in that module was significantly higher or lower for any two pairs of conditions . Conditions were defined based on high vs low , high vs medium , and low vs high pathogenicities . In addition , we considered those modules that exhibited different patterns of expression between the two wild-type viruses H1N1 ( CA04 ) and H5N1 ( VN1203 ) . For significant expression we used a t-test p-value < 0 . 01 for human and 0 . 05 for mouse . We relaxed the threshold for mouse lung because the data represents transcriptional response from a more heterogeneous collection of cells as compared to the human cell line; we also omitted day 7 from all mouse data due to lack of data ( due to lethality ) for the high pathogenicity viruses . To classify the modules based on expression differences between different pathogenicities , we used an additional criteria of selecting modules whose mean expression in one pathogenicity type was different in sign compared to mean expression in the second pathogenicity type . We implemented MTG-LASSO using the mtLeastR function available as part of the Sparse Learning with Efficient Projections package for MATLAB ( SLEP 4 . 1; [104] ) . The objective function for MTG-LASSO is defined as minW12||XW−Y||22−λ||W||l1l2 The first term in the objective function is the least squares loss obtained by the difference between the observed gene expression data matrix , Y , and the predicted values from the product of the protein data X and learned regression weight matrix W ( Fig 5A ) . The second term is the Group LASSO norm penalty on the complexity of the weight matrix . This norm penalizes the number of groups ( according to the one-norm ) and encourages smoothness among the weights within each group ( according to the Euclidean two-norm ) . The parameter λ controls the trade-off between loss and the regularization term . We evaluated the MTG-LASSO and LASSO methods over a range of λ , expressed as a fraction of its maximum possible value λmax , above which the coefficient vector will be forced to zero . The SLEP package calculates λmax for each model as follows . For MTG-LASSO , λmax is the largest two-norm of rows in X'Y , where X' is the matrix of protein data ( transposed from Fig 5A , now with proteins/groups on rows , samples on columns ) and Y is the matrix of mRNA data for one module ( samples on rows , genes/tasks on columns ) . For LASSO , λmax is the largest absolute element in the vector X'y , where X' is the protein data and y is the expression vector for one gene . We varied λ between several values from its minimum ( 0 . 01 , almost no sparsity imposed ) to its maximum ( 0 . 99 , significant sparsity imposed ) . The complete set of tested values were {0 . 01 , 0 . 10 , 0 . 25 , 0 . 50 , 0 . 75 , 0 . 99} . Because λ is normalized by the λmax it represents a comparable regularization strength between both regression techniques . We obtained predicted mRNA values for all protein-matched , mRNA samples of all module genes using a 10-fold cross-validation approach , where a consecutive set of about 10% of samples were held aside from each fold ( comprising most of a time course ) . For the very largest modules ( modules 1592 , 1594 in human ) , MTG-LASSO was computationally intractable , and therefore we could not identify any regulators . To select regulators for each module , we first chose a setting of λ for each module based on λ-correlation curves that plotted correlation of the predicted values and the true data against λ for each module ( Fig 5D and 5E , S1 Dataset ) . Surprisingly , we observed that the curve did not have the same shape for each module . Among human modules , we found three categories of modules based on these curves . The first consisted of 10 modules for which correlation is roughly constant across all values of λ; that is , all predictive performance on the held-aside data was entirely due to a very small set of regulators . The second consisted of another 10 modules for which performance improved as MTG-LASSO was allowed to use more regulators ( as sparsity decreased ) . A final third category contained those modules that could not be predicted more accurately than random for more than one setting of λ , usually the highest or lowest tested value ( all remaining modules ) . In contrast to the curves for the human modules , many mouse modules yielded λ-correlation curves that showed a visible inflection point , with high correlation before a particular λ and low correlation after . We grouped the modules based on a visual determination of the inflection point . Only two mouse modules were not predicted more accurately than random for multiple values of λ ( based on either RMSE or Pearson ) . We only considered λ values for which accuracy was significantly greater than random based on z-tests described in the next section . Plots for Pearson correlation are shown for example human and mouse modules in Fig 5D and 5E , with stars indicating the chosen values . All curves are available in S1 Dataset . For human modules , we chose λ = 0 . 75 for modules with constant correlation ( such as Module 1596 , Fig 5D ) , and λ = 0 . 10 for modules with correlation that decreased as λ increased ( such as Module 1549 , Fig 5D ) . For the mouse modules , the curves were not so obviously matched into 'constant' and 'decreasing' categories . We chose based on the visible inflection point in the curve , preferring the next higher ( sparser ) λ if the drop in correlation was not dramatic . After choosing λ , we defined consensus regulators using both frequency in cross-validation for the specific λ value and the magnitude of regression weights . First , we considered regulators that received nonzero regression weight in at least 6 of the 10 folds . Next , we applied a threshold on average absolute regression weights ( across all genes , across folds with nonzero weight ) , followed by a Bonferroni-corrected significance z-test ( p-value<0 . 05 ) to assess whether the same protein would be given a weight above that threshold by chance . We used a x¯ = 0 . 20 for human regulators and x¯ = 0 . 10 for mouse regulators , choices that resulted in approximately 1–6 regulators per module . Mean and standard deviation for the z-test were estimated from the random regression weights . See S10 Table , S11 Table for frequencies of chosen regulators across folds . We evaluated the significance of MTG-LASSO and LASSO predictive quality using one-sample z-tests [107] . To assess module-level predictive quality , we obtained one statistic , x¯ ( Pearson's correlation , RMSE ) , for a module from all predictions from 10 folds of cross validation . We then assembled a null distribution of statistics by running the method on N = 40 random permutations of protein data and real module gene expression data , obtaining the null mean μ0 and variance σ0 . We calculated the Z-score for the statistic as x¯−µ0σ0/N and obtained a one-sided p-value from the normal distribution . We developed a regulator prioritization score that is based on the loss in predictive power of the consensus regulatory network under in silico perturbations . For each regulator r , we held aside the regulator from the consensus regulatory network N , creating a "lesioned" network , N-{r} . We then re-learned regulator-target regression weights for the lesioned network using five-fold cross-validation . We use this network to score each regulator according to the average increase in prediction error when that regulator is removed from each of its targets' regulatory programs: Score ( r ) =1|targets ( r , N ) |∑t∈targets ( r , N ) etN−{r}−etN where targets ( r , N ) is the set of r's target genes as predicted by the consensus regulatory network N , etN is the mean squared prediction error for the expression profile of gene t using network N ( obtained by cross-validation ) , and etN−{r} is the mean squared prediction error for the same gene t given by the lesioned regulatory network N-{r} . We tested 20 regulators from MERLIN prioritization using siRNA . These regulators were selected based on their rankings in human and were additionally informed by their rankings in mouse . Nineteen of these regulators were in the top 40 for human . An additional candidate , FIG4 ( rank 42 ) was added because it was ranked in the top 100 of the mouse regulator list ( 96 ) . Other regulators that were in the top 100 of mouse included well-studied regulators ( IRF7 , NMI , STAT1 ) and were already in the top 40 of human rankings . For siRNA transfections , human lung epithelial cells ( A549 ) were seeded into 24-well plates ( 8x104 cells/well ) and allowed to settle for 2 hours before transfection with 10 nM siRNA ( final concentration ) and 1 μl of lipofectamine RNAiMax reagent ( Invitrogen ) . For each candidate regulator a gene-specific package of four preselected siRNAs were used ( FlexiTube Genesolution siRNA , Qiagen ) ( S7 Table ) . The following siRNAs were used as controls: a cell death inducing blend of siRNAs ( AllStars Hs Cell Death , catalog number 04381048 , Qiagen ) for visual confirmation of efficient siRNA delivery , a validated nontargeting siRNA ( AllStars Negative Control , cataglog number 1027281 , Qiagen ) as a negative control and a previously described siRNA targeting influenza virus NP mRNA ( NP-1496; synthesized by Qiagen ) as a positive control . Each siRNA was evaluated in triplicate . Cells were incubated for 48 h before infection with 500 plaque forming units of A/Oklahoma/vir09-1117003813/2009 ( pandemic H1N1 ) per well . Supernatants were collected from each well 48 h post infection and viral titers were determined by plaque assay in Madin-Darby Canine Kidney epithelial ( MDCK ) cells . Results for each siRNA were statistically assessed separately . First , we log-transformed the virus titers obtained by suppressing the expression of the candidate genes using siRNAs . Next , we compared the replicates of each candidate siRNA to the replicates of the negative control ( All-star siRNA ) , using one sided , unpaired T-tests . The p-values were not adjusted because the number of candidates was small and by adjusting the p-values we would likely lose true positives [106] . Finally , we calculated the fold-change and the log-fold change for each siRNA candidate compared to the negative control , and used these two measures ( significance , fold-change ) to identify hits that significantly changed the virus titers in the cells . To identify coarser temporal and virus-specific patterns among the active regulatory subnetworks derived from each sample , we clustered the edges according to the samples in which they were active . In order to focus on early stage immune response rather than late-stage cell death responses , we held aside the 18-hour time point from the medium and high-pathogenicity virus treatments ( H5N1 and all mutants ) , placing them in their own cluster ( Cluster E , Fig 6 ) . We performed average-linking hierarchical clustering using Manhattan distance , specifying the number of clusters k . We performed clustering for k = 3 , 4 , 5 , 6 , 7 , and inspected the resulting clustering by eye and by silhouette index . While k = 3 gave the highest silhouette index ( which decreased with k ) , we chose k = 4 because it made a distinction between a sustained , nearly pan-virus cluster ( Cluster B , Fig 6 ) and a sustained medium/high pathogenicity cluster ( Cluster C , Fig 6 ) . We used hypergeometric test-based enrichment ( 0 . 05 FDR corrected p-value < 0 . 05 ) to interpret the clusters using annotated pathways from MSigDB [92–94] , Reactome [97] , BioCarta ( http://www . biocarta . com ) and KEGG [98]; results are summarized in Table 6 , "Enriched pathways" . We also used a hypergeometric test to identify whether any regulators were cluster-specific , rather than common to all clusters ( Table 6 , "Enriched Regulators" ) . First , we took the union of the sample-specific subnetworks and identified the union target set of every regulator . For each of those regulators , we tested whether any cluster subnetwork was enriched for the targets of the regulator relative to the union ( FDR corrected p-value < 0 . 05 ) . To integrate the signaling proteins , transcription factors and protein regulators predicted for each MERLIN module , we used an integer linear programming-based ( ILP ) method for extracting subnetworks from a background network , similar to previous work [47–49] . The ILPs were modeled using GAMS modeling system v . 24 . 0 . 1 and the ILOG CPLEX solver v . 12 . 4 . 0 . We applied this method separately to each human module . The subnetworks that are extracted by this approach are composed of paths through a background physical interaction network ( provenance described below ) . We searched for two kinds of paths: ( i ) paths that begin with MERLIN signaling proteins and terminate in MERLIN TFs ( sinks ) that share at least three targets in common , and ( ii ) paths that begin with MTG-LASSO-based protein regulators ( sources ) and terminate in any MERLIN regulator ( signaling protein or TF , sinks ) . For all paths , we allowed only one intermediate node between the source and sink . For several modules , the sources and sinks were not sufficiently close ( or represented ) in the background network to allow for the generation of any paths . We also excluded the largest two modules ( 1592 and 1594 ) , which had a large number of regulators , and only a few regulators could be included in short paths . For some modules , the union of candidate paths resulted in small and visually interpretable subnetworks , as in Fig 9 . However , for most modules , the resulting subnetwork was not visually interpretable ( as in Fig 7 ) . Our ILP-based method can identify high-confidence interpretable subnetworks for all modules that had paths , regardless of size . The ILP-based approach ( described in detail in S2 Text ) finds an ensemble of connecting subnetworks and assigns confidence values to paths according to how important they are for connecting the regulators using a small number of additional nodes . We extract a subnetwork that connects all regulators by solving an integer linear program in which instructions for how paths may be chosen are expressed as linear constraints , and the objective function optimizes a global property of the subnetwork . Our constraints require the inclusion of reachable predicted regulators , and specify that each protein-protein interaction may only be used in one direction within the subnetwork . In the objective function , we encoded a preference that the subnetwork should use the influenza host genes from Watanabe et al . [46] as intermediates whenever possible , and to otherwise minimize the use of intermediate nodes . Because many subnetworks may satisfy these constraints , we combine multiple solutions to the ILP into an ensemble . We score each path by the fraction of solutions that contain that path . We defined a high confidence subnetwork as the paths that received at least 0 . 75 confidence over the ensemble . To estimate the false discovery rate of nodes and edges by this approach , we generated a null distribution of subnetworks by running the method on randomized input data . Randomization was performed as follows . We first replaced the consensus MERLIN regulators with randomly drawn TFs and signaling proteins from the set that was provided as input to MERLIN , maintaining the degree distribution ( in the background network ) of the entire group . We also drew random replacements for the protein regulators from the input set of proteins . Then , for each module , we mapped the original pairs of MERLIN sources and sinks , and protein sources and MERLIN sinks , to their randomized counterparts , searched for paths to connect them , and assigned confidence values using the ILP approach as we did for the true predicted regulators . We performed 40 randomizations , and calculated the FDR of a protein or an edge at a particular confidence level as the fraction of the random subnetworks that included it in a path of that confidence level . We assembled a human background network composed of protein-protein , protein-DNA , and metabolic interactions from the STRING database v9 . 1 ( [108]; excluding interactions labeled as 'expression' ) , high-confidence interactions from HIPPIE ( [109]; downloaded September 2014 , using high-confidence threshold of 0 . 73 as recommended on the website ) , low-throughput physical interactions from BioGRID ( [110]; downloaded September 2014 ) , and a human kinase-substrate network [111] . Each resulting background network consists of both directed ( e . g . , post-translational modifications such as phosphorylation and ubiquitination ) and undirected ( e . g . , binding ) interactions . We then removed all interactions involving ubiquitin ( UBB , UBC , UBD ) , SUMO ( SUMO1-4 ) , and 11 additional ubiquitin fusion proteins . These proteins are used as post-translational modifiers and are recorded as binding partners for large proportions of proteins in the background network . The ubiquitin and SUMO systems are still represented in the background network in the form of directed ubiquitination and sumoylation events between ligases and substrates . Networks in figures were developed using Cytoscape [112] and supporting website visualizations were developed with Cytoscape . js ( http://cytoscape . github . io/cytoscape . js ) We provide the inferred modules with their mRNA-based regulators , protein-based regulators , and physical subnetworks ( for human only ) as a navigable resource at http://pages . discovery . wisc . edu/~sroy/integrative_influenza/ . Code for MTG-LASSO and physical subnetwork identification is available from our repository at https://bitbucket . org/roygroup/integrative_networks | An important challenge in infectious disease research is to understand how the human immune system responds to different types of pathogenic infections . An important component of mounting proper response is the transcriptional regulatory network that specifies the context-specific gene expression program in the host cell . However , our understanding of this regulatory network and how it drives context-specific transcriptional programs is incomplete . To address this gap , we performed a network-based analysis of host response to influenza viruses that integrated high-throughput mRNA- and protein measurements and protein-protein interaction networks to identify virus and pathogenicity-specific modules and their upstream physical regulatory programs . We inferred regulatory networks for human cell line and mouse host systems , which recapitulated several known regulators and pathways of the immune response and viral life cycle . We used the networks to study time point and strain-specific subnetworks and to prioritize important regulators of host response . We predicted several novel regulators , both at the mRNA and protein levels , and experimentally verified their role in the virus life cycle based on their ability to significantly impact viral replication . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"pathogens",
"immunology",
"microbiology",
"viruses",
"regulator",
"genes",
"gene",
"types",
"small",
"interfering",
"rnas",
"medical",
"microbiology",
"gene",
"expression",
"microbial",
"pathogens",
"viral",
"replication",
"immune",
"response",
"biochemistry",
"rna",
"nucleic",
"acids",
"gene",
"regulatory",
"networks",
"virology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"non-coding",
"rna",
"organisms"
] | 2016 | Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens |
To form a veridical percept of the environment , the brain needs to integrate sensory signals from a common source but segregate those from independent sources . Thus , perception inherently relies on solving the “causal inference problem . ” Behaviorally , humans solve this problem optimally as predicted by Bayesian Causal Inference; yet , the underlying neural mechanisms are unexplored . Combining psychophysics , Bayesian modeling , functional magnetic resonance imaging ( fMRI ) , and multivariate decoding in an audiovisual spatial localization task , we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain . At the bottom of the hierarchy , in auditory and visual areas , location is represented on the basis that the two signals are generated by independent sources ( = segregation ) . At the next stage , in posterior intraparietal sulcus , location is estimated under the assumption that the two signals are from a common source ( = forced fusion ) . Only at the top of the hierarchy , in anterior intraparietal sulcus , the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference . Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex . It unravels how the brain accomplishes Bayesian Causal Inference , a statistical computation fundamental for perception and cognition . Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world .
Our senses are constantly bombarded with many different signals . Imagine you are crossing a street and suddenly hear a loud motor noise . Is that motor noise coming from the car on the opposite side of the street or from a rapidly approaching car that you have not yet spotted ? To locate the source of the motor noise more precisely , you should integrate the auditory signal with the sight of the car only if the two inputs pertain to the same object . Thus , estimating an environmental property ( e . g . , spatial location ) in multisensory perception inherently relies on inferring whether sensory signals are caused by common or independent sources [1 , 2] . Past research in perception and cue combination has mostly ignored the causal inference problem and focused on the special case in which sensory signals arise from a common source . A large body of research has demonstrated that observers integrate signals near-optimally weighted by their reliability in these “forced fusion” settings [3–9] . Yet , in our complex natural environment forced fusion would be detrimental and the brain needs to balance integration and segregation according to the underlying causal structure ( i . e . , common versus independent sources ) [10] . Hierarchical Bayesian Causal Inference provides a rational strategy to arbitrate between information integration and segregation in perception and cognition . In case of a common source , signals should be integrated weighted by their relative sensory reliabilities [3 , 4] . In case of independent sources , they should be processed independently . Critically , the observer does not know the underlying causal structure and needs to infer it from spatiotemporal or higher order ( e . g . , semantic ) congruency cues [2] . To account for the uncertainty about the causal structure , an observer should compute a final estimate by averaging the estimates ( e . g . , spatial location ) under the two potential causal structures weighted by the posterior probabilities of these structures ( i . e . , model averaging ) . Indeed , recent psychophysics and modeling efforts have demonstrated that human observers locate audiovisual signal sources in line with Bayesian Causal Inference by combining the spatial estimates under the assumptions of common and independent sources weighted by their posterior probabilities [2] . For small spatial disparities , audiovisual spatial signals are integrated weighted by their relative sensory reliabilities leading to strong crossmodal spatial biases [3]; for large spatial disparities , these crossmodal biases are greatly attenuated [11 , 12] , because the final spatial estimate relies predominantly on the segregated option . However , the neural mechanisms that enable Bayesian Causal Inference are unknown . In particular , it is unclear whether the brain encodes the spatial estimates under the assumptions of common and independent sources in order to perform Bayesian Causal Inference . Does the brain explicitly represent several spatial estimates that enter into Bayesian Causal Inference ?
At the behavioral level , we first investigated how participants integrate and segregate sensory signals for auditory and visual spatial localization . Fig 2 shows the histograms of response deviations as a function of task-relevance ( i . e . , auditory versus visual report ) , audiovisual spatial disparity , and visual reliability . If participants were able to determine the location of the task-relevant auditory or visual signal precisely , the histogram over response deviations would reduce to a delta function centered on zero . Thus , the difference in widths of the histograms for auditory and visual report indicates that participants were less precise when locating auditory ( green ) as compared to the visual signals ( red ) . Likewise , as expected visual localization was less precise for low ( red dashed ) relative to high visual ( red solid ) reliability . Importantly , for auditory localization , the response distribution was shifted towards a concurrent spatially discrepant visual signal . This visual spatial bias on the perceived auditory location was increased when the visual signal was reliable , thus replicating the classical profile of the spatial ventriloquist effect [3] . Moreover , it was more pronounced for 13 . 3° than for 20° disparity . In other words , as expected under Bayesian Causal Inference , the influence of a concurrent visual signal on the perceived auditory location was attenuated for large spatial discrepancies , when it was less likely that auditory and visual signals came from a common source . Next , we analyzed visual and auditory localization reports more formally by comparing three models . ( i ) The full-segregation model assumes that auditory and visual signals are processed independently . ( ii ) The forced-fusion model assumes that auditory and visual signals are integrated weighted by their reliabilities in a mandatory fashion irrespective of the environmental causal structure . ( iii ) The Bayesian Causal Inference model computes a final auditory ( or visual ) spatial estimate by averaging the spatial estimates under forced-fusion and full-segregation assumptions weighted by the posterior probabilities of each causal structure ( i . e . , model averaging , see S3 and S4 Tables for other decision functions ) . Using a maximum likelihood procedure , we fitted the parameters ( e . g . , visual variances σV12 − σV22 for the two reliability levels ) of the three models individually to each participant’s behavioral localization responses . Bayesian model comparison corroborated previous results [2] and demonstrated that the Bayesian Causal Inference model outperformed the full-segregation and forced-fusion models ( 82 . 4% variance explained , exceedance probability of 0 . 95 ) ( Table 1 ) . In other words , human observers integrate audiovisual spatial signals predominantly when they are close in space and hence likely to come from a common source . Next , we asked how Bayesian Causal Inference emerged along the auditory and visual cortical hierarchies ( Fig 3 ) . In particular , Bayesian Causal Inference entails four spatial estimates: the full-segregation unisensory ( i ) auditory ( ŜA , C = 2 ) and ( ii ) visual estimates ( ŜV , C = 2 ) , ( iii ) the “audiovisual forced-fusion estimate” ( ŜAV , C = 1 ) , and ( iv ) the final Bayesian Causal Inference estimate ( ŜA & ŜV , pooled over conditions of auditory and visual report ) that is obtained by averaging the forced-fusion and the task-relevant unisensory estimates weighted by the posterior probability of each causal structure . We obtained these four spatial estimates for each of the 64 conditions and each participant from the Causal Inference model fitted individually to participant’s behavioral data ( Fig 3B , bottom ) . Using cross-validation , we trained a support vector regression model to decode each of these four spatial estimates from fMRI voxel response patterns in regions along the cortical hierarchies defined by visual retinotopic and auditory localizers ( Fig 3C ) . We quantified the decoding accuracies for each of these four spatial estimates in terms of their correlation between ( i ) the spatial estimates obtained from the Causal Inference model fitted individually to participants’ localization responses ( i . e . , training labels for fMRI decoding ) and ( ii ) the spatial estimates decoded from fMRI voxel response patterns . To determine which of the four spatial estimates is primarily encoded in a particular region , we computed the exceedance probability that a correlation coefficient of one spatial estimate was greater than that of any other spatial estimate by bootstrapping the decoding accuracies ( Fig 3D ) . The profile of exceedance probabilities demonstrates that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain: At the bottom of the hierarchy , in auditory and visual areas , location is represented on the basis that the two signals are generated by independent sources . Thus , primary sensory areas predominantly encoded the spatial estimate of their preferred sensory modality under information segregation , even though they also showed limited multisensory influences as previously reported [15–21] . At the next stage , in posterior intraparietal sulcus ( IPS1–2 ) , location is estimated under the assumption that the two signals are from a common source . In other words , IPS1–2 represented primarily the reliability-weighted integration estimate under forced-fusion assumptions . It is only at the top of the hierarchy , in anterior intraparietal sulcus ( IPS3–4 ) , that the uncertainty about whether signals are generated by common or independent sources is taken into account . As predicted by Bayesian Causal Inference , location is estimated in IPS3–4 by combining the full-segregation and the forced-fusion estimates weighted by the posterior probabilities of common and independent sources . Thus , according to Bayesian Causal Inference the spatial estimates in IPS3–4 should be influenced by task-irrelevant sensory signals primarily for small spatial disparities , when signals were likely to be generated by a common event . Critically , while no region could uniquely be assigned one type of spatial estimate , the profile of exceedance probabilities reveals a hierarchical organization of the computational operations in human neocortex . Recent elegant neurophysiological research in non-human primates has shown how single neurons and neuronal populations implement reliability-weighted integration under forced-fusion assumptions [22–24] . In other words , they presented visual and vestibular signals only with a very small discrepancy , so that signals could be assumed to arise from a common source . Yet , to our knowledge this is the first neuroimaging study that moves beyond traditional forced-fusion models and demonstrates how the brain performs hierarchical Bayesian Causal Inference [2] . Thus , future neurophysiological and modelling research will need to define how single neurons and neuronal populations implement computational operations of Bayesian Causal Inference , potentially via probabilistic population codes [25] . Accumulating evidence has suggested that multisensory interactions are pervasive in human neocortex [18 , 26–31] starting already at the primary cortical level [15–21] . Indeed , our multivariate decoding analysis also revealed multisensory influences ubiquitously along the auditory and visual processing streams with limited multisensory influences emerging already in primary sensory areas . To link our study more closely with previous fMRI results of spatial ventriloquism , we have interrogated our data also with a conventional univariate analysis of regional blood-oxygen-level dependent ( BOLD ) responses . Converging with our model-based findings , this conventional analysis also suggested that low-level sensory areas are predominantly driven by signals of their preferred sensory modality ( e . g . , visual cortex by visual signals ) . Yet , in line with previous reports [27 , 31] , visual signals influenced the BOLD response already in the “higher auditory area” ( hA ) encompassing the planum temporale . Moreover , while activations in parietal areas were still influenced by visual location , they were progressively susceptible to effects of task-context mediated either directly or in interaction with visual reliability ( see supporting results and discussion in S1 Fig , S2 Table , and S1 Text ) . Thus , both the regional BOLD response and the spatial representations encoded in parietal areas and to some extent in auditory areas were influenced by whether the location of the visual or the auditory signal needed to be attended to and reported in line with the principles of Bayesian Causal Inference . As the current paradigm manipulated the factor of task-relevance over sessions , participants knew the sensory modality that needed to be reported prior to stimulus presentation . Thus , the regional BOLD-response in higher auditory cortices is likely to be modulated by attentional top-down effects [32–36] . Future studies may investigate Bayesian Causal Inference when auditory and visual report trials are presented in a randomized fashion to minimize attention- and expectation-related effects . Alternatively , studies could factorially manipulate ( i ) the attended and ( ii ) the reported sensory modality . For instance , participants may be cued to attend to the auditory modality prior to stimulus presentation and yet be instructed to report the visual modality after stimulus presentation . Yet , despite these attempts Bayesian Causal Inference may inherently entail processes associated with “attentional modulation” in a wider sense , as it computationally requires combining the multisensory forced-fusion estimate with the “task-relevant” unisensory estimate . Critically , however , the effects of attentional modulation or task-relevance invoked by Bayesian Causal Inference should interact with the spatial discrepancy between the sensory signals . Effects of task-relevance should be most pronounced for large spatial discrepancies . In conclusion , the multivariate analysis based on Bayesian Causal Inference moves significantly beyond identifying multisensory interactions , towards characterizing their computational operations that prove to differ across cortical levels . This methodological approach provides a novel hierarchical perspective on multisensory integration in human neocortex . We demonstrate that the brain simultaneously encodes multiple spatial estimates based on segregation , forced fusion , and model averaging along the cortical hierarchy . Only at the top of the hierarchy , higher-order anterior IPS3–4 takes into account the uncertainty about the causal structure of the world and combines sensory signals as predicted by Bayesian Causal Inference . To our knowledge , this study is the first compelling demonstration of how the brain performs Bayesian Causal Inference , a statistical operation fundamental for perception and cognition .
The study was approved by the human research review committee of the University of Tuebingen ( approval number 432 2007 BO1 ) . After giving written informed consent , six healthy volunteers without a history of neurological or psychiatric disorders ( all university students or graduates; 2 female; mean age 28 . 8 years , range 22–36 years ) participated in the fMRI study . All participants had normal or corrected-to-normal vision and reported normal hearing . One participant was excluded because of excessive head motion ( 4 . 21/3 . 52 standard deviations above the mean of the translational/rotational volume-wise head motion based on the included five participants ) . The visual stimulus was a cloud of 20 white dots ( diameter: 0 . 43° visual angle ) sampled from a bivariate Gaussian with a vertical standard deviation of 2 . 5° and a horizontal standard deviation of 2° or 14° presented on a black background ( i . e . , 100% contrast ) . Participants were told that the 20 dots were generated by one underlying source in the center of the cloud . The auditory stimulus was a burst of white noise with a 5 ms on/off ramp . To create a virtual auditory spatial signal , the noise was convolved with spatially specific head-related transfer functions ( HRTFs ) thereby providing binaural ( interaural time and amplitude differences ) and monoaural spatial filtering signals . The HRTFs were pseudo-individualized by matching participants’ head width , height , depth , and circumference to the anthropometry of participants in the CIPIC database [37] . HRTFs from the available locations in the database were interpolated to the desired location of the auditory signal . The behavioral responses from the auditory localizer session ( see below ) indicated that participants were able to localize the virtual auditory spatial signals in the magnetic resonance ( MR ) scanner . They were significantly better than chance at discriminating whether two subsequent auditory signals were presented from the same or different locations ( mean accuracy = 0 . 88; mean d’ = 3 . 14 , p = 0 . 001 in a one sample t-test against zero ) . In a spatial ventriloquist paradigm , participants were presented with synchronous , yet spatially congruent or disparate visual and auditory signals ( Fig 1A ) . On each trial , visual and auditory locations were independently sampled from four possible locations along the azimuth ( i . e . , −10° , −3 . 3° , 3 . 3° , or 10° ) leading to four levels of spatial discrepancy ( i . e . , 0° , 6 . 6° , 13 . 3° , or 20° ) . In addition , we manipulated the reliability of the visual signal by setting the horizontal standard deviation of the Gaussian cloud to 2° ( high reliability ) or 14° ( low reliability ) visual angle . In an inter-sensory selective-attention paradigm , participants reported their auditory or visual perceived signal location and ignored signals in the other modality . For the visual modality , they were asked to determine the location of the center of the visual cloud of dots . Hence , the 4 × 4 × 2 × 2 factorial design manipulated ( i ) the location of the visual stimulus ( {−10° , −3 . 3° , 3 . 3° , 10°} , i . e . , the mean of the Gaussian ) ; ( ii ) the location of the auditory stimulus ( {−10° , −3 . 3° , 3 . 3° , 10°} ) ; ( iii ) the reliability of the visual signal ( {2° , 14°} , standard deviation of the Gaussian ) ; and ( iv ) task-relevance ( auditory-/visual-selective report ) resulting in 64 conditions ( Fig 1B ) . Please note that in contrast to our inter-sensory attention paradigm , Koerding and colleagues [2] employed a dual task paradigm where participants reported auditory and visual locations on each trial . Thus , the two paradigms differ in terms of attentional and task-induced processes . On each trial , synchronous audiovisual spatial signals were presented for 50 ms followed by a variable inter-stimulus fixation interval from 1 . 75–2 . 75 s . Participants localized the signal in the task-relevant sensory modality as accurately as possible by pushing one of four spatially corresponding buttons . Throughout the experiment , they fixated a central cross ( 1 . 6° diameter ) . To maximize design efficiency , stimuli and conditions were presented in a pseudorandomized fashion . Only the factor task-relevance was held constant within a session and counterbalanced across sessions . In each session , each of the 32 audiovisual spatial stimuli was presented exactly 11 times either under auditory- or visual-selective report . On average , 5 . 9% of the trials were interspersed as null-events in the sequence of 352 stimuli per session . Each participant completed 20 sessions ( ten auditory and ten visual localization reports; apart from one participant who performed nine auditory and 11 visual localization sessions ) . Before the fMRI study , participants completed one practice session outside the scanner . Audiovisual stimuli were presented using Psychtoolbox 3 . 09 ( www . psychtoolbox . org ) [38] running under MATLAB R2010a ( MathWorks ) . Auditory stimuli were presented at ~75 dB SPL using MR-compatible headphones ( MR Confon ) . Visual stimuli were back-projected onto a Plexiglas screen using an LCoS projector ( JVC DLA-SX21 ) . Participants viewed the screen through an extra-wide mirror mounted on the MR head-coil resulting in a horizontal visual field of approximately 76° at a viewing distance of 26 cm . Participants performed the localization task using an MR-compatible custom-built button device . Participants’ eye movements and fixation were monitored by recording participants’ pupil location using an MR-compatible custom-build infrared camera ( sampling rate 50 Hz ) mounted in front of the participants’ right eye and iView software 2 . 2 . 4 ( SensoMotoric Instruments ) . To address potential concerns that our results may be confounded by eye movements , we evaluated participants’ eye movements based on eye tracking data recorded concurrently during fMRI acquisition . Eye recordings were calibrated with standard eccentricities between ±3° and ±10° to determine the deviation from the fixation cross . Fixation position was post-hoc offset corrected . Eye position data were automatically corrected for blinks and converted to radial velocity . For each condition , the number of saccades ( defined by a radial eye-velocity threshold of 15° s−1 for a minimum of 60 ms duration and radial amplitude larger than 1° ) were quantified ( 0–875 ms after stimulus onset ) . Fixation was well maintained throughout the experiment with post-stimulus saccades detected in only 2 . 293% ± 1 . 043% ( mean ± SEM ) of the trials . Moreover , 4 ( visual location ) × 4 ( auditory location ) × 2 ( visual reliability ) × 2 ( visual versus auditory report ) repeated measure ANOVAs performed separately for ( i ) % saccades or ( ii ) % eye blinks revealed no significant main effects or interactions . To characterize how participants integrate auditory and visual signals into spatial representations , we computed the deviation between the responded location and the mean responded location in the corresponding congruent condition for each trial and in each subject . For instance , for trial i ( e . g . , auditory location = 3 . 3° , visual location = −3 . 3° , visual reliability = low , visual report ) we computed the response deviation by comparing the responded visual location in trial i to the mean responded visual location for the corresponding congruent condition ( e . g . , auditory location = −3 . 3° , visual location = −3 . 3° , visual reliability = low , visual report ) . We then averaged the individual histograms of response deviations across subjects ( Fig 2 , for an additional analysis of response accuracy see supporting results in S1 Table and S1 Text ) . Fig 2 shows the histograms of the response deviations as a function of task-relevance , visual reliability and audiovisual disparity ( i . e . , disparity = visual location − auditory location ) . Please note that we flipped the histograms for negative spatial disparities and auditory report and the histograms for positive spatial disparities and visual report , so that for both types of reports increasing disparity corresponded to a rightward shift of the task-irrelevant signal in Fig 2 . We then combined the histograms for positive and negative spatial disparities to reduce the number of conditions and the complexity of Fig 2 . Details of the Bayesian Causal Inference model of audiovisual perception can be found in Koerding and colleagues [2] . The generative model ( Fig 3B ) assumes that common ( C = 1 ) or independent ( C = 2 ) sources are determined by sampling from a binomial distribution with the common-source prior P ( C = 1 ) = pcommon . For a common source , the “true” location SAV is drawn from the spatial prior distribution N ( μP , σP ) . For two independent causes , the “true” auditory ( SA ) and visual ( SV ) locations are drawn independently from this spatial prior distribution . For the spatial prior distribution , we assumed a central bias ( i . e . , μP = 0 ) . We introduced sensory noise by drawing xA and xV independently from normal distributions centered on the true auditory ( respectively , visual ) locations with parameters σA ( respectively , σV ) . Thus , the generative model included the following free parameters: the common-source prior pcommon , the spatial prior variance σP2 , the auditory variance σA2 , and the two visual variances σV2 corresponding to the two visual reliability levels . Under the assumption of a squared loss function , the posterior probability of the underlying causal structure can be inferred by combining the common-source prior with the sensory evidence according to Bayes rule ( cf . S5 Table ) : p ( C= 1∣xA , xV ) = p ( xA , xV∣C=1 ) pcommon p ( xA , xV ) ( 1 ) In the case of a common source ( C = 1 ) ( Fig 3B left ) , the optimal estimate of the audiovisual location is a reliability-weighted average of the auditory and visual percepts and the spatial prior . In the case of independent sources ( C = 2 ) ( Fig 3B right ) , the optimal estimates of the auditory and visual signal locations ( for the auditory and visual location report , respectively ) are independent from each other . To provide a final estimate of the auditory and visual locations , the brain can combine the estimates under the two causal structures using various decision functions such as “model averaging , ” “model selection , ” and “probability matching” [39] . In the main paper , we present results using “model averaging” as the decision function that was associated with the highest model evidence and exceedance probability at the group level ( see S4 Table; please note that at the within-subject level , model averaging was the most likely decision strategy in only three subjects , see S3 Table , and Wozny and colleagues [39] ) . According to the “model averaging” strategy , the brain combines the integrated forced-fusion spatial estimate with the segregated , task-relevant unisensory ( i . e . , either auditory or visual ) spatial estimates weighted in proportion to the posterior probability of the underlying causal structures . Thus , Bayesian Causal Inference formally requires three spatial estimates ( ŜAV , C = 1 , ŜA , C = 2 , ŜV , C = 2 ) which are combined weighted by the posterior probability of each causal structure into a final estimate ( ŜA / ŜV , depending on which sensory modality is task-relevant ) . We evaluated whether and how participants integrate auditory and visual signals based on their behavioral localization responses by comparing three models: ( i ) The observers may process and report auditory and visual signals independently ( i . e . , the full-segregation model , Equation 3 ) . ( ii ) They may integrate auditory and visual signals in a mandatory fashion irrespective of spatial disparity ( i . e . , the forced-fusion model , Equation 2 ) . ( iii ) The observer may perform Bayesian Causal Inference , i . e . , combine estimates from the forced-fusion and the task-relevant estimate from the full-segregation model weighted by the probability of the underlying causal structures ( Equations 4 and 5 , i . e . , model averaging , for other decision functions see S3 Table and S4 Table ) . To arbitrate between full segregation , forced fusion , and Bayesian Causal Inference , we fitted each model to participants’ localization responses ( Table 1 ) based on the predicted distributions of the auditory spatial estimates ( i . e . , p ( ŜA|SA , SV ) ) and the visual spatial estimates ( i . e . , p ( ŜV|SA , SV ) ) . These distributions were obtained by marginalizing over the internal variables xA and xV that are not accessible to the experimenter ( for further details of the fitting procedure see Koerding and colleagues [2] ) . These distributions were generated by simulating xA and xV 5 , 000 times for each of the 64 conditions and inferring ŜA and ŜV from Equations 1–5 . To link p ( ŜA|SA , SV ) and p ( ŜV|SA , SV ) to participants’ auditory or visual discrete localization responses , we assumed that participants selected the button that is closest to ŜA or ŜV and binned the ŜA and ŜV accordingly into a histogram ( with four bins corresponding to the four buttons ) . Thus , we obtained a histogram of predicted auditory or visual localization responses for each condition and participant . Based on these histograms we computed the probability of a participant’s counts of localization responses using the multinomial distribution ( see Koerding and colleagues [2] ) . This gives the likelihood of the model given participants’ response data . Assuming independence of experimental conditions , we summed the log likelihoods across conditions . To obtain maximum likelihood estimates for the parameters of the models ( pcommon , σP , σA , σV1 − σV2 for the two levels of visual reliability; formally , the forced-fusion and full-segregation models assume pcommon = 1 or = 0 , respectively ) , we used a non-linear simplex optimization algorithm as implemented in MATLAB’s fmin search function ( MATLAB R2010b ) . This optimization algorithm was initialized with 200 different parameter settings that were defined based on a prior grid search . We report the results ( across-subjects' mean and standard error ) from the parameter setting with the highest log likelihood across the 200 initializations ( Table 1 ) . This fitting procedure was applied individually to each participant’s data set for the Bayesian Causal Inference , the forced-fusion , and the full-segregation models . The model fit was assessed by the coefficient of determination R2 [40] defined as R2=1−exp ( −2n ( l ( ß^ ) −l ( 0 ) ) ) where l ( ß^ ) and l ( 0 ) denote the log likelihoods of the fitted and the null model , respectively , and n is the number of data points . For the null model , we assumed that an observer randomly chooses one of the four response options , i . e . , we assumed a discrete uniform distribution with a probability of 0 . 25 . As in our case the Bayesian Causal Inference model’s responses were discretized to relate them to the four discrete response options , the coefficient of determination was scaled ( i . e . , divided ) by the maximum coefficient ( cf . [40] ) defined as max ( R2 ) =1−exp ( 2nl ( 0 ) ) To identify the optimal model for explaining participants’ data , we compared the candidate models using the Bayesian information criterion ( BIC ) as an approximation to the model evidence [41] . The BIC depends on both model complexity and model fit . We performed Bayesian model selection [42] at the group level as implemented in SPM8 [43] to obtain the exceedance probability for the candidate models ( i . e . , the probability that a given model is more likely than any other model given the data ) . A 3T Siemens Magnetom Trio MR scanner was used to acquire both T1-weighted anatomical images and T2*-weighted axial echoplanar images with BOLD contrast ( gradient echo , parallel imaging using GRAPPA with an acceleration factor of 2 , TR = 2 , 480 ms , TE = 40 ms , flip angle = 90° , FOV = 192 × 192 mm2 , image matrix 78 × 78 , 42 transversal slices acquired interleaved in ascending direction , voxel size = 2 . 5 × 2 . 5 × 2 . 5 mm3 + 0 . 25 mm interslice gap ) . In total , 353 volumes times 20 sessions were acquired for the ventriloquist paradigm , 161 volumes times 2–4 sessions for the auditory localizer and 159 volumes times 10–16 sessions for the visual retinotopic localizer resulting in approximately 18 hours of scanning in total per participant assigned over 7–11 days . The first three volumes of each session were discarded to allow for T1 equilibration effects . Ventriloquist paradigm . The fMRI data were analyzed with SPM8 ( http://www . fil . ion . ucl . ac . uk/spm ) [43] . Scans from each participant were corrected for slice timing , were realigned and unwarped to correct for head motion and spatially smoothed with a Gaussian kernel of 3 mm FWHM . The time series in each voxel was high-pass filtered to 1/128 Hz . All data were analyzed in native participant space . The fMRI experiment was modelled in an event-related fashion with regressors entering into the design matrix after convolving each event-related unit impulse with a canonical hemodynamic response function and its first temporal derivative . In addition to modelling the 32 conditions in our 4 ( auditory locations ) × 4 ( visual locations ) × 2 ( visual reliability ) factorial design , the general linear model included the realignment parameters as nuisance covariates to account for residual motion artefacts . The factor task-relevance ( visual versus auditory report ) was modelled across sessions . The parameter estimates pertaining to the canonical hemodynamic response function defined the magnitude of the BOLD response to the audiovisual stimuli in each voxel . For the multivariate decoding analysis , we extracted the parameter estimates of the canonical hemodynamic response function for each condition and session from voxels of the regions of interest ( = fMRI voxel response patterns ) defined in separate auditory and retinotopic localizer experiments ( see below ) . Each fMRI voxel response pattern for the 64 conditions in our 4 × 4 × 2 × 2 factorial design was based on 11 trials within a particular session . To avoid the effects of image-wide activity changes , each fMRI voxel response pattern was normalized to have mean zero and standard deviation one . Decoding of spatial estimates . To investigate whether and how regions along the auditory and visual spatial processing hierarchy ( defined below; cf . Fig 3C ) represent spatial estimates of the Causal Inference model , we used a multivariate decoding approach where we decoded each of the four spatial estimates from the regions of interest: ( i ) the full-segregation visual estimate: ŜV , C = 2 , ( ii ) the full-segregation auditory estimate: ŜA , C = 2 , ( iii ) the forced-fusion audiovisual estimate: ŜAV , C = 1 , and ( iv ) the Bayesian Causal Inference ( i . e . , model averaging ) estimate: ŜA & ŜV , pooled over auditory and visual report ( i . e . , for each condition we selected the model averaging estimate that needs to be reported in a particular task context ) . Thus , our decoding approach implicitly assumed that the forced-fusion as well as the auditory and visual estimates under full segregation are computed automatically irrespective of task-context . By contrast , the final auditory or visual Bayesian Causal Inference estimates are flexibly computed depending on the particular task-context according to a decision function such as model averaging . After fitting the Causal Inference model individually to behavioral localization responses ( see above ) , the fitted model predicted these four spatial estimates’ values in 10 , 000 simulated trials for each of the 64 conditions . The spatial estimates’ values as an index of participants’ perceived location are of a continuous nature . Finally , we summarized the posterior distribution of spatial estimates ( i . e . , participant’s perceived location ) by averaging the values across those 10 , 000 simulated trials for each of the four spatial estimates separately for each condition and participant . Please note that using the maximum a posteriori estimate as a summary index for the posterior distribution provided nearly equivalent results . For decoding , we trained a linear support vector regression model ( SVR , as implemented in LIBSVM 3 . 14 [44] ) to accommodate the continuous nature of these mean spatial estimates that reflect the perceived signal location for a particular condition and subject . More specifically , we employed a leave “one session” out cross-validation scheme: First , we extracted the voxel response patterns in a particular region of interest ( e . g . , V1 ) from the parameter estimate images pertaining to the magnitude of the BOLD response for each condition and session ( i . e . , 32 conditions × 10 sessions for auditory report + 32 conditions × 10 sessions for visual report = 640 voxel response patterns ) . For each of the four spatial estimates ( e . g . , ŜV , C = 2 ) , we trained one SVR model to learn the mapping from the condition-specific fMRI voxel response patterns ( i . e . , examples ) to the condition-specific spatial estimate’s values ( i . e . , labels ) from all but one session ( i . e . , 640 − 32 = 608 voxel responses patterns ) . The model then used this learnt mapping to decode the spatial estimates from the 32 voxel response patterns from the single remaining session . In a leave-one-session-out cross-validation scheme , the training-test procedure was repeated for all sessions . The SVRs’ parameters ( C and ν ) were optimized using a grid search within each cross-validation fold ( i . e . , nested cross-validation ) . We quantified the decoding accuracies for each of these four spatial estimates in terms of the correlation coefficient between ( i ) the spatial estimates obtained from the Causal Inference model fitted individually to a participant’s localization responses ( i . e . , these spatial estimates were used as training labels for fMRI decoding , e . g . , ŜV , C = 2 ) and ( ii ) the spatial estimates decoded from fMRI voxel response patterns using SVR . To determine whether the spatial estimates ( i . e . , labels ) can be decoded from the voxel response patterns , we entered the Fisher z-transformed correlation coefficients for each participant into a between-subject one-sample t-test and tested whether the across-participants mean correlation coefficient was significantly different from zero separately for the ( i ) segregated auditory or ( ii ) visual , ( iii ) forced-fusion audiovisual , or ( iv ) auditory and visual Bayesian Causal Inference estimate . As these four spatial estimates were inherently correlated , most regions showed significant positive correlation coefficients for several or even all spatial estimates ( see S6 Table ) . Thus , to determine which of the four spatial estimates was predominantly represented in a region , we computed the exceedance probabilities ( i . e . , the probability that the correlation coefficient of one spatial estimate is greater than the correlation coefficient of any other spatial estimate ) using non-parametric bootstrapping across participants ( N = 1 , 000 times ) . For each bootstrap , we resampled the 5 ( = number of participants ) individual Fisher z-transformed correlation coefficients with replacement from the set of participants for each of the four spatial estimates and formed the across participants’ mean correlation coefficient for each of the four spatial estimates [45] . In each bootstrap , we then determined which of the four spatial estimates obtained the largest mean correlation coefficient . We repeated this procedure for 1 , 000 bootstraps . The fraction of bootstraps in which a decoded spatial estimate ( e . g . , the segregated auditory estimate ) had the largest mean correlation coefficient ( indexing decoding accuracy ) was defined as a spatial estimate’s exceedance probability ( Fig 3D ) . Please note that under the null hypothesis , we would expect that none of the four spatial estimates is related to the voxel response pattern resulting in a uniform distribution of exceedance probabilities for all four spatial estimates ( i . e . , exceedance probability of 0 . 25 ) . Auditory and visual retinotopic localizer . Auditory and visual retinotopic localizers were used to define regions of interest along the auditory and visual processing hierarchies in a participant-specific fashion . In the auditory localizer , participants were presented with brief bursts of white noise at −10° or 10° visual angle ( duration 500 ms , stimulus onset asynchrony 1 s ) . In a one-back task , participants indicated via a key press when the spatial location of the current trial was different from the previous trial . 20 s blocks of auditory conditions ( i . e . , 20 trials ) alternated with 13 s fixation periods . The auditory locations were presented in a pseudorandomized fashion to optimize design efficiency . Similar to the main experiment , the auditory localizer sessions were modelled in an event-related fashion with the onset vectors of left and right auditory stimuli being entered into the design matrix after convolution with the hemodynamic response function and its first temporal derivative . Auditory responsive regions were defined as voxels in superior temporal and Heschl’s gyrus showing significant activations for auditory stimulation relative to fixation ( p < 0 . 05 , family-wise error corrected ) . Within these regions , we defined primary auditory cortex ( A1 ) based on cytoarchitectonic probability maps [46] and referred to the remainder ( i . e . , planum temporale and posterior superior temporal gyrus ) as higher-order auditory area ( hA , see Fig 3C ) . Standard phase-encoded retinotopic mapping [47] was used to define visual regions of interest ( http://sampendu . wordpress . com/retinotopy-tutorial/ ) . Participants viewed a checkerboard background flickering at 7 . 5 Hz through a rotating wedge aperture of 70° width ( polar angle mapping ) or an expanding/contracting ring ( eccentricity mapping ) . The periodicity of the apertures was 42 s . Visual responses were modelled by entering a sine and cosine convolved with the hemodynamic response function as regressors in a general linear model . The preferred polar angle was determined as the phase lag for each voxel , which is the angle between the parameter estimates for the sine and the cosine . The preferred phase lags for each voxel were projected on the reconstructed , inflated cortical surface using Freesurfer 5 . 1 . 0 [48] . Visual regions V1–V3 , V3AB , and IPS0-IPS4 were defined as phase reversal in angular retinotopic maps . IPS0–4 were defined as contiguous , approximately rectangular regions based on phase reversals along the anatomical IPS [49] . For the decoding analyses , the auditory and visual regions were combined from the left and right hemispheres . | How can the brain integrate signals into a veridical percept of the environment without knowing whether they pertain to same or different events ? For example , I can hear a bird and I can see a bird , but is it one bird singing on the branch , or is it two birds ( one sitting on the branch and the other singing in the bush ) ? Recent studies demonstrate that human observers solve this problem optimally as predicted by Bayesian Causal Inference; yet , the neural mechanisms remain unclear . By combining psychophysics , Bayesian modelling , functional magnetic resonance imaging ( fMRI ) , and multivariate decoding in an audiovisual localization task , we show that Bayesian Causal Inference is performed by a neural hierarchy of multisensory processes . At the bottom of the hierarchy , in auditory and visual areas , location is represented on the basis that the two signals are generated by independent sources ( = segregation ) . At the next stage , in posterior intraparietal sulcus , location is estimated under the assumption that the two signals are from a common source ( = forced fusion ) . Only at the top of the hierarchy , in anterior intraparietal sulcus , the uncertainty about the world’s causal structure is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception |
Classical approaches to estimate vaccine efficacy are based on the assumption that a person's risk of infection does not depend on the infection status of others . This assumption is untenable for infectious disease data where such dependencies abound . We present a novel approach to estimating vaccine efficacy in a Bayesian framework using disease transmission models . The methodology is applied to outbreaks of mumps in primary schools in the Netherlands . The total study population consisted of 2 , 493 children in ten primary schools , of which 510 ( 20% ) were known to have been infected , and 832 ( 33% ) had unknown infection status . The apparent vaccination coverage ranged from 12% to 93% , and the apparent infection attack rate varied from 1% to 76% . Our analyses show that vaccination reduces the probability of infection per contact substantially but not perfectly ( = 0 . 933; 95CrI: 0 . 908–0 . 954 ) . Mumps virus appears to be moderately transmissible in the school setting , with each case yielding an estimated 2 . 5 secondary cases in an unvaccinated population ( = 2 . 49; 95%CrI: 2 . 36–2 . 63 ) , resulting in moderate estimates of the critical vaccination coverage ( 64 . 2%; 95%CrI: 61 . 7–66 . 7% ) . The indirect benefits of vaccination are highest in populations with vaccination coverage just below the critical vaccination coverage . In these populations , it is estimated that almost two infections can be prevented per vaccination . We discuss the implications for the optimal control of mumps in heterogeneously vaccinated populations .
Mass vaccination programs for childhood diseases have been highly successful in reducing the incidence and public health impact of the targeted diseases . Nevertheless , with the exception of smallpox , eradication has not been achieved , and outbreaks continue to occur even in highly vaccinated populations [1]–[4] . A prominent example is that of mumps , which has re-emerged in the past decade in highly vaccinated populations throughout the world [5]–[7] . The question arises as to whether this re-emergence is due to current vaccines becoming less effective , or to reduced vaccine coverage which allows the virus to spread in partially vaccinated populations [8] . In the Netherlands , large outbreaks of mumps genotypes D and G have occurred in recent years [9]–[11] . Since 1987 , a combined MMR ( measles-mumps-rubella ) vaccine containing live attenuated virus is routinely given at 14 months and 9 years of age . Vaccination coverage has been high ever since introduction of the vaccine in 1987 ( 90–95% ) . Nevertheless , there are municipalities in which vaccination coverage is substantially lower [12] , [13] . To determine whether the outbreaks of mumps are the result of low vaccination coverage or insufficient protection conferred by the vaccine , we estimate vaccine efficacy using outbreak data from ten primary schools in the Netherlands [9] , [11] . The total number of children included in our study is 2 , 493 , of whom 510 had a reported mumps infection . Vaccination coverage in these schools ranged from 12%–93% , and infection attack rates ranged from 4% to 76% , with highest attack rates occurring in schools with the lowest vaccination coverage and lowest attack rates in schools with high vaccination coverage ( Table 1 ) . Notably , the attack rates in unvaccinated individuals varied from more than 80% in schools with low vaccination coverage ( <15% ) to lower than 25% in schools with high vaccination coverage ( ≥75% ) , indicating substantial differences in the infection pressure between schools . Classical methods to estimate vaccine efficacy from outbreak data compare the infection attack rates in the vaccinated versus unvaccinated groups ( i . e . the cohort method ) [14] , [15] . This method , however , has significant drawbacks . First , it is not straightforward to take account of missing data on vaccination and infection status . This is unfortunate as outbreak data are almost never complete , and judicious choices will have to be made to avoid introducing systematic bias in the parameter estimates . Even more importantly , the cohort method fails to acknowledge that the probability of infection of an individual is dependent on the number of infections in the population , i . e . on the infection status of others . To take account of the dependencies between individuals that arise naturally in infectious disease outbreaks we base the statistical analyses on a Bayesian inferential framework using infectious disease transmission models . In this framework , missing vaccination and infection information is imputed in a consistent manner , thereby making efficient use of the available information , and enabling precise estimation of vaccine efficacy and the critical vaccination coverage needed to prevent epidemic outbreaks [16] , [17] . The basis of our statistical analyses is the contact process that specifies how often and with which person-types each person makes infectious contacts , i . e . contacts that are sufficient for transmission if the sender is infected and the receiver as yet uninfected [18]–[20] . The contact process specifies a directed graph , of which the connected component with the initial infective as the root determines which individuals are ultimately infected . Estimation of the epidemiological parameters ( basic reproduction number , vaccine efficacy ) is based on the likelihood of directed graphs that are compatible with the data . The analyses reveal that mumps vaccine effectively prevents infection , and that herd immunity against mumps is achieved with moderate vaccination coverages . We argue that resource-limited catch-up vaccination efforts should be focused at communities with intermediate vaccination coverages , thereby maximizing both the direct and indirect benefits of vaccination .
Our baseline scenario assumes a common transmissibility and vaccine efficacy across schools . The analysis indicates that mumps is moderately transmissible ( = 2 . 49; 95%CrI: 2 . 36–2 . 63 ) , and that the vaccine reduces the probability of transmission by more than 90% per contact that would have resulted in transmission to an unvaccinated person ( = 0 . 933; 95CrI: 0 . 908–0 . 954 ) ( Figure 1 ) . The differences between the apparent and estimated vaccination coverages and attack rates are small ( <2% and <5% , respectively; Tables 1–2 ) . We use estimates of transmissibility and vaccine efficacy to obtain estimates of the critical vaccination coverage . The analyses yield an estimated critical vaccination coverage of 0 . 642 ( 95%CrI: 0 . 617–0 . 666 ) , indicating that herd immunity in the school setting can be obtained with moderate vaccination coverages . Estimates of transmissibility and vaccine efficacy are used to obtain an estimate of the number of infections prevented per vaccination . This number is highest for vaccination coverages just below the critical vaccination coverage , as at these values the slope of attack rate versus vaccination coverage is steepest ( Figure 2 ) . The number of infections prevented per vaccination near the threshold coverage is well approximated ( using a Taylor series expansion ) by . Hence , it is expected that the ( direct and indirect ) benefits of vaccination are such that infections can be prevented per vaccination if the initial vaccination coverage is just below the threshold value , which is estimated by . Schools in our study population span a large range of possible vaccination coverages , and it is of interest to evaluate the consistency of the estimates of vaccine efficacy and pathogen transmissibility . Figure 2 shows the relation between vaccination coverage and infection attack rate in the ten schools , together with the theoretical relation between vaccination coverage and attack rate in a large population , and simulations of a finite population . Overall , the correspondence between the observed and simulated data is excellent for schools with low vaccination coverage and high attack rates , while there is a tendency for higher attack rates than expected in schools with high vaccination coverage and a small number of infections . To investigate the information contained in the data by school we perform analyses in which each school is equipped with its own transmissibility and vaccine efficacy . It appears that precise estimates of transmissibility and vaccine efficacy can be obtained in schools with high attack rates ( schools 1–4 ) , but not in schools with only a handful of infections ( schools 7–10 ) . In fact , in schools with less than 10 confirmed infections credible intervals of the reproduction number range from well below 1 to more than 3 , while vaccine efficacy estimates can range from less than 0 . 20 ( schools 8–10 ) to almost 1 ( schools 7–10; Table 3 , Figure 3 ) . Further , the analyses show that in schools with high attack rates ( schools 1–4 ) the parameter estimates are quite close to those of the baseline scenario , indicating that estimates of transmissibility and vaccine efficacy in the baseline scenario are dominated by schools with large numbers of infections and low vaccination coverages . In comparison with our estimates of vaccine efficacy as the reduction in the probability of infection ( Table 3 ) , estimates of vaccine efficacy by the cohort method tend to be somewhat lower in schools with low vaccination coverage and high infection attack rates ( schools 1–4; Table 4 , Table S3 ) . Moreover , in these schools credible intervals tend to be slightly broader when using the cohort method . The most conspicuous difference , however , is that in populations with high vaccination coverage ( schools 7–10 ) , vaccine efficacy is sometimes estimated with fair precision when using the cohort method , even though the number of infections is very small ( ≤6 ) .
Our analyses have shown that mumps is moderately transmissible in the setting of primary schools , and that the vaccine used in these populations is highly effective in preventing infection . These results are largely in line with earlier studies [5] , [6] , but contrast with a recent study that suggested that outbreaks of mumps in populations with large-scale vaccination programs may be due to the vaccine having become less effective in preventing infection [4] . The younger average age of our study population and the fact that these outbreaks have been caused by viruses of different genotypes ( genotype D versus genotype G ) may help explain these contrasting findings . Since genotype D viruses are genetically distant from the current vaccine virus ( Jeryl Lynn strain , genotype A ) our results indicate that the Jeryl Lynn-based vaccine is highly effective in curbing transmission to vaccinated persons , even if genetic differences between the vaccine and outbreaks strains are substantial [8] . Estimates of the transmissbility of mumps are most precise in schools 1–4 , i . e . in schools with low vaccination coverage and large numbers of infections . In these schools , the basic reproduction number is estimated at 2 . 5 , 2 . 3 , 2 . 8 , and 2 . 5 , with credible intervals ranging from 1 . 9 to 3 . 2 . Vaccine efficacy , on the other hand , is estimated most precisely in schools 1 , 3 , 4 , and 5 ( Table 3 , Figure 3 ) . In these schools , estimates of vaccine efficacy are 0 . 97 , 0 . 99 , 0 . 94 , and 0 . 98 , with credible intervals ranging from 0 . 84 to 1 . These schools have low vaccination coverage and high levels of exposure ( i . e . high attack rates ) but still more than 30 vaccinated persons . In school 2 the exposure level has been high but the number of vaccinated persons is too small for precise estimation of vaccine efficacy . In schools with high coverage , vaccine efficacy cannot be estimated with any precision , as in these schools it is uncertain whether escape from infection is caused by the vaccine or by a lack of exposure . The schools included in this study differ greatly with respect to vaccination coverages ( range: 12%–93% ) and infection attack rates ( range: 4%–76% ) . Nevertheless , estimates of vaccine efficacy are remarkably consistent across schools ( Table 3 , Figure 3 ) . In fact , only in schools with just a handful of infections ( ≤6 ) ( schools 8–10 ) does the estimated vaccine efficacy drop below 0 . 88 . In these schools , credible intervals of vaccine efficacy are wide , and estimates are less determined by the information contained in the data than by the prior distribution of vaccine efficacy . This is also the reason that estimates of vaccine efficacy in the baseline scenario are dominated by schools with low vaccination coverages and high attack rates , as these schools contain much more information than schools with high vaccination coverages and low infection attack rates ( Figure 2 ) . Schools in our study were included based on confirmed mumps infections . It is therefore possible that large outbreaks are more likely to be detected and included than small outbreaks . In other words , it is conceivable that the inclusion process systematically favours inclusion of schools with uncharacteristically high attack rates , thereby leading to selection bias . For schools with low vaccination coverage ( and high attack rates ) this is arguably not a problem as variation in outbreak sizes is expected to be minor , given the sizes of the schools included ( Figure 2 ) . For schools with high vaccination coverage , however , selection bias may well have played a role , and may explain the relatively high attack rates in some of these schools ( school 6 and to a lesser extend schools 9–10 ) ( Figure 2 ) . Fortunately , one could argue that our statistical methodology provides a natural weighting of schools , in which schools with small number of infections have lower weight than schools with high number of infections . If specific details were available on the inclusion process , one could envisage extension of the analyses in which the selection process is modelled explicitly . This , however , would introduce more model options , additional parameters to be estimated , and would certainly lead to a more complicated analysis . We have assumed throughout that infections outside the school played a marginal role . Again , this assumption is probably less problematic in schools with low vaccination coverage and high infection attack rates than in schools with high vaccination coverage and lower attack rates , as variation in the expected number of infections is expected to be small in schools with low vaccination coverage . Moreover , there was no sustained community transmission during the study period , suggesting that the impact of infection outside the schools may have been small . Nevertheless , it would be interesting to extend the current analyses , e . g . , along the lines of [21] , [22] by inclusion of other major transmission settings . Classical estimates of mumps transmissibility have been based on the mean age at infection in the pre-vaccination era ( [23] and references therein ) , or on seroprevalence data from the pre-vaccination era [24] , [25] . These analyses yielded estimates of the basic reproduction number in fully unvaccinated populations that are substantially higher ( ∼7–20 ) than our estimates ( ∼2–3 ) . It should be noted that these population-based estimates cannot directly be translated to our school-based estimates . Still , should those early estimates be indicative of the current transmissibility of mumps at the population level , then not only are schools an important transmission route but other settings also have the potential to contribute significantly to overall transmission . Again , to assess the contribution of different settings to the overall transmission dynamics , it would be desirable to extend the current studies beyond the school setting , by including household information and , in the specific case of this study , information on the churches attended by the participants [9] . This , however , is only possible if detailed information were available on these settings , not only with respect to their composition but also with respect to vaccination and infection status of a sizeable part of the population . Vaccine efficacy and transmissibility together determine the critical vaccination coverage needed to prevent epidemic outbreaks . In our study , estimates of the critical vaccination coverage are 64% ( 95%CrI: 62%–67% ) in the baseline scenario , and range from 63% ( 95%CrI: 58%–68%; school 1 ) to 76% ( 95%CrI: 66%–83%; school 6 ) in schools with more than 10 confirmed infections ( schools 1–6 ) . This indicates that the critical vaccination coverage does not need to be as high as suggested by early population-based estimates , which are in the range of 86%–95% . In none of the analyses presented here have we made a distinction between children who had been vaccinated once and those that had been vaccinated twice . This was done because preliminary analyses and previous results [9] , [11] could not find any evidence for differences in vaccine efficacy between the two groups . In view of the data this is not unexpected , as the total number of infections in vaccinated children was small , and as attack rates in the two subpopulations were identical ( 15 infections among the 582 children who had been vaccinated once; 10 infections among the 370 who had been vaccinated twice ) . The fact that attack rates were identical is somewhat surprising , as one could have expected more infections in the group that had been vaccinated only once , more than five years ago . For completeness , we have presented the full data in Table S2 . Further , in our analyses we assume that the vaccine works by reducing the probability of transmission ( i . e . we assume a leaky vaccine ) , rather than by providing all-or-nothing immunity . This was done for simplicity , and since the current data do not allow us to distinguish between the different workings of the vaccine . If additional data was available , e . g . , on the pre-outbreak antibody titres , one could consider extension of the method by using pre-outbreak antibody titres as an indicator for the ‘level of immunity’ , and use this indicator to estimate how the level of pre-existing immunity relates to the probability of infection . In most situations , however , such information will be hard to get , as this would necessitate a large prospective study . Our definition of vaccine efficacy has a clear-cut biological interpretation ( reduction of the probability of infection per contact ) . This makes it possible to meaningfully average over populations with varying vaccination coverages and exposure levels , and also to extrapolate beyond the study population . This contrasts with traditional estimates of vaccine efficacy that are based on a comparison of attack rates in vaccinated and unvaccinated individuals ( the cohort method ) , or that simply use the vaccination status of the infected individuals together with the population vaccination coverage ( the screening method ) [14] , [26] . Vaccine efficacy estimated by these methods lack a clear biological interpretation , and in essence assumes that a person's risk of infection is independent of whether or not others in the population are infected . This makes interpretation of the estimates problematic , and forbids estimation of the critical vaccination coverage [15] , [27]–[29] . Even though our definition of vaccine efficacy differs fundamentally from vaccine efficacy measured by the cohort method , the results are quantitatively in fair agreement with traditional estimates , especially in populations with low vaccination coverage and large number of infections ( Table 3 versus Table 4 and Table S3 ) . In schools with high vaccination coverage and small numbers of infections the reverse tends to be true , and estimates of vaccine efficacy generally are both higher and more precise when using the cohort method . For instance , in school 10 there are 6 confirmed infections , and vaccine efficacy is poorly estimated in our analysis ( 95%CrI: 0 . 16–0 . 96 ) but with fair precision by the cohort method ( 95%CrI: 0 . 68–0 . 98 ) . This is arguably an artefact of the latter method's assumption that all 139 uninfected vaccinated persons have been exposed to an infected person , thereby artificially increasing the precision of the estimates of vaccine efficacy . Our results point to strategies to efficiently allocate catch-up vaccination efforts in heterogeneously vaccinated populations . No additional vaccination is needed in schools with high vaccination coverage ( >75% , say ) as these are already protected against epidemic outbreaks affecting a large fraction of students . Similarly , allocating vaccines to schools with low vaccination coverage ( <50% , say ) is inefficient as it does not markedly reduce the probability of infection for those who are not vaccinated , i . e . the indirect benefits of vaccination are small in these populations . Our analyses suggest that vaccination of populations in the range between these two extremes is most efficient , and that in these populations a single vaccination can potentially prevent almost two infections . Of course , in practice other considerations , for instance on ethical issues , communication , and cost-effectiveness would also come into play .
In the Netherlands , several large outbreaks of mumps virus ( genotype D ) occurred in 2007–2009 . We collected data from children attending primary schools with evidence of mumps virus transmission ( report of at least one laboratory confirmed mumps case or more than one clinical mumps case ) [9] , [11] . Children's parents were asked to fill out a questionnaire asking for information on the child's vaccination status and occurrence of mumps . Individual data on vaccination status were also retrieved from the national Dutch vaccination register . When these were not available , we used the self-reported vaccination status ( vaccinated/unvaccinated ) . Children who were vaccinated more than twice ( one case ) , and who were reported to have had mumps before September 2007 ( three cases ) were excluded . The study was approved by the medical ethics committee of the University Medical Centre Utrecht and the Radboud University Nijmegen Medical Centre . The data are presented in Table 1 and Tables S1 , S2 . To explore the correspondence between the parameter estimates with the data , we simulated outbreaks in schools of size 200 using the digraph construction described above . To prevent early extinction we introduced three infectious persons with random vaccination status in each simulation . For each vaccination composition , we generated 5 , 000 random digraphs with the values of the basic reproduction number and vaccine efficacy sampled without replacement from the posterior distribution . Subsequently , for each graph we calculated the attack rate among those that were initially susceptible , and present the median and 2 . 5% and 97 . 5% percentiles of the resulting distributions ( the black line and grey area in Figure 2 ) . To compare our results with estimates of vaccine efficacy using the cohort method [14] , we have calculated vaccine efficacy as 1 minus the relative risk of infection in vaccinated versus unvaccinated persons . In these analyses only information of persons with known vaccination and infection status was taken into account ( Table S1 ) . As in the above we employ a Bayesian framework in which the probabilities in the unvaccinated and vaccinated groups are assigned uniform prior distributions , yielding beta-binomial posterior distributions for the infection probabilities . Estimates are obtained using Markov chain Monte Carlo ( MCMC ) methods , specifically by taking a thinned sample of 10 , 000 from a converged chain of length 500 , 000 . Table S3 reports classical ( frequentist ) estimates of vaccine efficacy using the cohort method [14] . | Less than two decades ago , it was generally believed that in developed countries infectious diseases such as measles , mumps , and pertussis were under firm control via vaccination . Nowadays , it is increasingly recognized that this picture has been overly optimistic . A central question is whether recurrent disease outbreaks are caused by vaccination coverage having dropped below safe levels , or by vaccines having become less effective . To answer this question , the authors study outbreaks of mumps in primary schools in the Netherlands . Using disease transmission models , the authors estimate vaccine efficacy and the critical vaccination coverage needed to prevent large outbreaks . The analyses show that the vaccine has been highly effective in preventing infection , but that vaccination coverage has been insufficient in some schools . The authors argue that catch-up vaccination campaigns aimed at populations with intermediate vaccination coverage will be most efficient , as these would maximize the ( direct and indirect ) benefits of vaccination . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"infectious",
"disease",
"epidemiology",
"epidemiology",
"disease",
"dynamics",
"population",
"dynamics",
"population",
"biology",
"biology",
"epidemiological",
"methods",
"infectious",
"disease",
"control",
"infectious",
"disease",
"modeling"
] | 2013 | Estimation of Vaccine Efficacy and Critical Vaccination Coverage in Partially Observed Outbreaks |
Aspergillus fumigatus is the most common cause of invasive mold disease in humans . The mechanisms underlying the adherence of this mold to host cells and macromolecules have remained elusive . Using mutants with different adhesive properties and comparative transcriptomics , we discovered that the gene uge3 , encoding a fungal epimerase , is required for adherence through mediating the synthesis of galactosaminogalactan . Galactosaminogalactan functions as the dominant adhesin of A . fumigatus and mediates adherence to plastic , fibronectin , and epithelial cells . In addition , galactosaminogalactan suppresses host inflammatory responses in vitro and in vivo , in part through masking cell wall β-glucans from recognition by dectin-1 . Finally , galactosaminogalactan is essential for full virulence in two murine models of invasive aspergillosis . Collectively these data establish a role for galactosaminogalactan as a pivotal bifunctional virulence factor in the pathogenesis of invasive aspergillosis .
The incidence of invasive mold infections due to the fungus Aspergillus fumigatus has increased dramatically in hematology patients receiving intensive cytotoxic chemotherapy or undergoing hematopoietic stem cell transplantation [1] . Despite the advent of new antifungal therapies , the mortality of invasive aspergillosis ( IA ) remains 60–80% [2] . There is therefore a pressing need for novel therapeutic strategies to treat or prevent IA . A better understanding of the pathogenesis of IA is one approach that may inform the development of new therapeutic targets . Adherence of A . fumigatus to host constituents is thought to be an early and critical step in the initiation of colonization and infection [3] . Upon inhalation , A . fumigatus conidia rapidly adhere to pulmonary epithelial cells and resident macrophages before being internalized and germinating within host cells [4] , [5] , [6] . Following germination , filamentous hyphae remain in intimate contact with host epithelial , endothelial and immune cells and can induce tissue injury and inflammatory responses . Inhibition of these adherence events may provide a useful therapeutic strategy to reduce morbidity and mortality of A . fumigatus mediated disease . Despite the fact that hyphae play such a critical role in the pathogenesis of invasive aspergillosis , the fungal ligands governing adherence of A . fumigatus hyphae to host constituents are largely unknown . A bioinformatic analysis of potential adhesins of A . fumigatus has identified several candidate proteins involved in mediating adhesion to host constituents [7] , but the adherence of mutant strains that lack these proteins has not been reported . Carbohydrate constituents of the cell wall have been recently implicated in adherence events [8] although their role in mediating adherence to host constituents has not been studied . We recently identified a fungal regulatory protein , MedA , which governs fungal adhesion to host cells and basement membrane constituents and biofilm formation [9] . In addition , we found that a strain deficient in StuA , a previously described developmental transcription factor [10] , was similarly deficient in the formation of adherent biofilms . In contrast deletion of ugm1 , which encodes a UDP-galactose mutase required for the production of galactofuranose , has been reported to result in a strain of A . fumigatus with increased adherence to epithelial cells [11] . Here , we report that carbohydrate analysis of these mutants revealed that the ΔmedA and ΔstuA mutants were defective in galactosaminogalactan ( GAG ) production whereas the Δugm1 mutant hyperproduced GAG . A comparative transcriptome analysis of the ΔmedA and ΔstuA regulatory mutants identified a gene encoding a putative UDP-glucose-epimerase , designated uge3 , which was dysregulated in both the ΔstuA and ΔmedA mutants . Disruption of uge3 resulted in a complete block in GAG synthesis , and markedly decreased adhesion to host cells and biofilm formation . The Uge3 deficient strain was also attenuated in virulence and induced a hyperinflammatory response in a corticosteroid treated mouse model . The absence of GAG in hyphae resulted in an increased exposure of cell wall β-glucan and in higher levels of dectin-1 binding , in association with the release of higher levels of pro-inflammatory cytokine by dendritic cells . Blocking dectin-1 with an anti-dectin-1 antibody , or pre-incubating hyphae with Fc-dectin-1 blocked this increased cytokine production . Suppression of inflammation in mice treated with cyclophosphamide and cortisone acetate resulted in further attenuation of virulence of the Δuge3 mutant , although the degree of reduction in fungal burden was similar in neutropenic and corticosteroid treated mice . Collectively these data identify GAG as a multifunctional virulence factor that mediates adherence of A . fumigatus , cloaks β-glucan and suppresses host inflammatory responses in vivo .
Previously , a mutant deficient in UDP-galactofuranose mutase ( ugm1 ) was reported to have increased adherence to host cells and abiotic substrates , while an A . fumigatus mutant deficient in the regulatory factor MedA had markedly impaired adherence to multiple substrates and was defective in biofilm formation [9] . To identify other mutants with alterations in adhesion , we screened a collection of regulatory mutants including mutants deficient in StuA , BrlA , AcuM and DvrA [10] , [12] , [13] , [14] for their ability to form biofilms on plastic surfaces . Using this approach , we found that the ΔstuA mutant previously described by our group [10] was also markedly impaired in the formation of adherent biofilms on plastic ( Fig . 1A , 1B ) . We hypothesized that these differences in the adherence properties of these three strains might stem from alterations in expression of a single adhesion factor . To test this hypothesis , we performed an analysis of the cell wall carbohydrate composition of these mutant strains in comparison to their respective complemented strains and wild-type A . fumigatus . The cell walls of the hypoadherent ΔmedA and ΔstuA strains , but not the hyperadherent Δugm1 strain , were found to contain a significant reduction in N-acetyl galactosamine ( GalNAc ) ( Fig . 1C ) . Since N-acetyl galactosamine is a key component of galactosaminogalactan ( GAG ) , a glycan found within the amorphous cell wall and extracellular matrix of A . fumigatus during infection [15] , [16] , these results suggested that GAG is involved in the differential adhesive properties of these mutants . Consistent with this hypothesis , culture supernatants from the ΔmedA mutant contained no detectable GAG , while only trace amounts of GAG were found in supernatants from the ΔstuA mutants ( Fig . 1D ) . In contrast , culture supernatants from the Δugm1 mutant contained markedly increased GAG as compared to the wild-type and ugm1 complemented strains . To test the hypothesis that GAG mediates A . fumigatus adherence , we examined the ability of a suspension of extracellular GAG harvested from wild-type hyphae to rescue the adherence defects of the ΔstuA and ΔmedA hyphae . The addition of supplemental GAG resulted in a dose dependent increase in adherence to tissue culture treated plates ( Fig . 1E ) . The addition of exogenous GAG also increased the adherence of wild-type A . fumigatus , although to a lesser extent than was seen with the adhesion deficient mutants treated with GAG . Collectively these results suggested that GAG was responsible for the adherence of A . fumigatus to plastic and other substrates . Since MedA and StuA control the expression of hundreds of genes , to remove any pleiotrophic effect and to test the specific role of GAG production in adherence , we sought to identify the specific genes required for GAG synthesis . Whole genome microarray analysis of the ΔmedA strain was performed during hyphal growth and development , and compared with the wild-type and medA complemented strain . Genes that were significantly dysregulated in the ΔmedA strain were then compared with the list of previously identified ΔstuA dependent genes [10] . Ten genes were identified as being significantly dysregulated in both mutant strains ( Fig . 2A ) . Among these genes was Afu3g07910 , predicted to encode a UDP-glucose epimerase . Given the role of glucose epimerases in the biosynthesis of galactose and galactosamine , this gene , designated uge3 , was selected for further study . Real-time RT-PCR confirmed that uge3 expression was reduced in both the ΔstuA and ΔmedA strains ( Fig . 2B ) . To test the role of Uge3 in the synthesis of GAG , a Δuge3 mutant strain was constructed . Deletion of uge3 had no observable effects on growth or morphology including conidiation , conidia size , germination , and radial hyphal growth on solid media in a wide variety of conditions including: minimal media , nutrient rich media ( YPD ) , pH range 4 . 5 to 8 . 5 , varying iron concentrations ( from 0 to 30 µM ) , and microaerophilic or normoxic conditions ( Fig . S1 ) . Scanning electron microscopy of Δuge3 mutant hyphae revealed a complete loss of surface decoration and of intercellular matrix ( Fig . 3A ) . Cell wall analysis of the Δuge3 mutant strain demonstrated an undetectable level of N-acetyl galactosamine ( Fig . 3B ) , and no GAG was detected in culture filtrates from this strain ( Fig . 3C ) . The production of soluble galactofuranose was unaffected ( Fig . 3D ) . When compared with wild-type A . fumigatus , a slight increase in cell wall GlcNAc content was observed in the Δuge3 mutant ( Fig . 3B ) , as well as a minimally increased resistance to the anti-chitin agent nikkomycin but not to the chitin binding agent calcofluor white ( Table 1 ) . However , complementation of the Δuge3 mutant with an intact allele of uge3 had no effect on these observations , despite completely restoring N-acetyl galactosamine and GAG synthesis . Collectively these results suggest that Uge3 is necessary for the production of GAG . To test the hypothesis that GAG was required to mediate A . fumigatus adherence to substrates , we compared the adherence of the Δuge3 mutant with the wild-type and uge3 complemented strains to a variety of substrates . The Δuge3 mutant strain exhibited a near complete absence of adherence to all substrates tested , including plastic , pulmonary epithelial cells and fibronectin ( Fig . 4A , 4B ) . As it was observed with the ΔstuA and ΔmedA mutant strains , preincubation of either plastic plates or hyphae of the Δuge3 mutant with a suspension of wild-type GAG produced a dose dependent increase in adherence to plastic ( Fig . 4C , 4D ) . This increased adherence was not observed when the wells or hyphae were supplemented with a suspension of zymosan , a β-glucan-rich fungal cell wall preparation , suggesting that the increased adherence is specific to GAG . Scanning electron microscopy of Δuge3 hyphae incubated with a suspension of GAG demonstrated a partial restoration of the cell wall decoration seen in wild-type hyphae , suggesting that the Δuge3 mutant was able to bind extracellular GAG ( Fig . 4E ) . To confirm that GAG directly binds to epithelial cells , we tested the ability of a suspension of GAG isolated from wild-type A . fumigatus to bind directly to A549 cells . FITC conjugated Soy Bean Agglutinin ( SBA ) was used to quantify GAG binding . This lectin is specific for terminal GalNAc residues , and does not bind to GAG deficient uge3 mutant hyphae ( Fig . 5A ) . Using this approach , purified GAG was observed to bind to A549 epithelial cells in a dose dependent manner ( Fig . 5B ) . Collectively these results demonstrate that GAG is required for adherence to , and injury of epithelial cells , and suggest that GAG is an important adhesin of A . fumigatus . Deletion of uge3 also completely blocked the ability of A . fumigatus to induce pulmonary epithelial cell injury as measured by a chromium release assay ( Fig . 6 ) . Restoration of uge3 expression in the Δuge3 mutant completely restored the ability of A . fumigatus to adhere to host constituents and damage epithelial cells , confirming the specificity of these observations and suggesting that GAG is necessary for adherence to host constituents and subsequent induction of epithelial cell injury . To determine if blocking GAG synthesis and fungal adherence alters virulence , we compared the virulence of the Δuge3 , wild-type A . fumigatus and the uge3 complemented strain in a corticosteroid treated mouse model of invasive aspergillosis . Mice infected with the Δuge3 mutant strain survived significantly longer than mice infected with either the wild type of uge3-complemented strain ( Fig . 7A ) , although this effect was modest . Consistent with the increased survival of mice infected with the Δuge3 mutant , these mice were found to have a significantly reduced pulmonary fungal burden after four days of infection as compared with mice infected with wild-type A . fumigatus ( Fig . 7B ) . Histopathologic examination confirmed that infection with the Δuge3 mutant strain produced fewer and much smaller fungal lesions than did the wild-type A . fumigatus ( Fig . 7C ) . Surprisingly , despite the lower abundance of hyphae in pulmonary lesions of mice infected with the Δuge3 mutant , these lesions contained more neutrophils than did those of mice infected with wild-type A . fumigatus . These results suggest that infection with the Δuge3 mutant strain induced an increased host inflammatory response . GAG has been localized to the amorphous outer layer of the fungal cell wall [15] . We therefore hypothesized that extracellular GAG might mask the surface exposure of other fungal pathogen-associated molecular pattern ( PAMP ) molecules such as β-1 , 3 glucan , and as a result the increased inflammatory response seen during infection with the Δuge3 mutant might be a consequence of unmasking of these PAMPs . To test this hypothesis , we performed immunofluorescent microscopy to compare the binding of recombinant Fc-dectin-1 [17] to the Δuge3 mutant and wild-type A . fumigatus . Consistent with previous reports [18] , we found that binding of Fc-dectin-1 to swollen conidia could be detected in both strains ( Fig . 8A ) . However , during germination and hyphal growth , there was much more intense staining of Δuge3 mutant hyphae as compared with the wild-type parent strain , in which Fc-dectin-1 binding decreased over time . In contrast , total β-1 , 3 glucan content , as assessed by aniline blue staining and release of soluble β-1 , 3 glucan in the culture supernatant , was not different between the wild-type and the Δuge3 mutant strains ( Fig . 8B , 8C ) . Similarly , sensitivity of the Δuge3 mutant to the β-1 , 3 glucan synthase inhibitor casofungin was unchanged from the wild-type parent strain ( Table 1 ) . Therefore , the increased binding of Fc-dectin-1 to the Δuge3 cells was due to greater surface exposure of β-1 , 3 glucan rather than increased synthesis of this glycan . To test if the increased exposure of β-glucan , or other fungal cell wall PAMPs , on the surface of Δuge3 hyphae might induce an increased inflammatory response by immune cells , we determined the cytokine response of bone marrow derived dendritic cells ( BMDDCs ) upon co-culture with wild-type or Δuge3 hyphae . After 6 hours of co-incubation , BMDDCs infected with the Δuge3 mutant strain produced significantly higher levels of pro-inflammatory cytokines , including TNF-α , KC , MIP-1α , IL-6 , and a trend to higher IL-12 levels , as compared to BMDDCs infected with the wild-type strain ( Fig . 9 ) . In addition , a trend to lower levels of the anti-inflammatory cytokine IL-10 produced by BMDDCs infected with the Δuge3 mutant as compared with hyphae of wild-type A . fumigatus was observed , although this difference was not statistically significant . To confirm these results and determine if this increased pro-inflammatory response induced by the Δuge3 mutant was mediated by increased binding to dectin-1 , we examined the ability of an anti-dectin-1 neutralizing antibody and Fc-dectin-1 to block the increase in TNF-α production by BMDDCs in response to hyphae of the Δuge3 mutant strain . Pre-incubation of BMDDCs with a monoclonal anti-dectin-1 antibody completely blocked the increased TNF-α production by BMDDCs in response to hyphae of the Δuge3 mutant strain ( Fig . 10 ) . Similarly , pre-incubating hyphae of the Δuge3 mutant strain with Fc-dectin-1 completely blocked the increased TNF-α production by BMDDCs . Collectively these results support the hypothesis that GAG inhibits host inflammatory responses in part by masking of PAMPs such as β-glucan . The results of these in vitro and in vivo studies suggest that the unmasking of fungal PAMPs in the absence of GAG induces an increased inflammatory response to hyphae that is detrimental to the host . To test this hypothesis , the virulence of the Δuge3 mutant and wild-type strain was compared for their virulence in highly immunosuppressed mice treated with both corticosteroids and cyclophosphamide . In this model , A . fumigatus infection does not induce a detectable cellular or cytokine inflammatory response during the neutropenic period [19] . In these highly immunosuppressed mice , the Δuge3 mutant strain exhibited markedly attenuated virulence as compared with the wild-type parent strain ( Fig . 11A ) . This difference in virulence was unlikely related to differences in the initial infectious inoculum , since the fungal burden was similar between mice infected with the wild-type and Δuge3 mutant and sacrificed one hour after infection ( a median of 1900 vs . 1850 colony forming units per animal , respectively ) . Mice infected with the Δuge3 mutant strain had a reduction in pulmonary fungal burden that was similar in magnitude to that seen in the non-neutropenic mouse model ( Fig . 11B ) . Histopathologic examination of lungs after 5 days of infection confirmed an absence of infiltrating leukocytes surrounding the sites of wild-type A . fumigatus infection ( Fig . 11C ) . These data suggest that the increased inflammatory response induced by the Δuge3 strain in non-neutropenic mice is non-protective and increases mortality , because inhibiting inflammation in the highly immunosuppressed mouse model was associated with improved survival . To confirm this hypothesis , we compared the inflammatory response during infection with the wild-type and the Δuge3 mutant in both the non-neutropenic model and the highly immunosuppressed models . To minimize the effects of differences in fungal burden between strains , mice were studied earlier in the course of disease , after three days of infection . In non-neutropenic immunosuppressed mice , a significantly lower fungal burden was again observed in mice infected with the Δuge3 mutant strain as compared with those infected with wild-type A . fumigatus ( Fig . 12A ) . Relative to this lower fungal burden , Δuge3 mutant strain was found to induce significantly higher pulmonary myeloperoxidase levels ( MPO ) suggesting that this mutant has a higher capacity to mediate pulmonary leukocyte recruitment as compared with wild-type A . fumigatus ( Fig . 12A ) . Similarly , the relative induction of pulmonary TNF-α , as well as the ability to induce pulmonary injury , as measured by LDH levels in BAL fluid , was significantly greater with the Δuge3 mutant than with wild-type A . fumigatus ( Fig . 12A ) . In contrast , in the highly immunosuppressed mouse model there was no significant difference in pulmonary fungal burden , myeloperoxidase content , TNF-α levels or LDH release between mice infected with the wild-type and with the Δuge3 mutant strain at this earlier time point ( Fig . 12B ) . Further , these measures of inflammation were significantly lower in these highly immunosuppressed mice as compared with non-neutropenic animals . Collectively these data suggest that in non-neutropenic mice , infection with the Δuge3 mutant stimulates a non-protective hyper-inflammatory response .
In A . fumigatus , GAG is a heterogeneous linear polymer consisting of α1–4 linked galactose and N-acetylgalactosamine residues in variable combination [16] . GAG is secreted and also a component of both the amorphous cell wall and extracellular matrix . In addition , GAG has been detected in lung lesions of experimentally infected animals [15] . The present study adds significantly to our understanding of the biosynthesis and function of this fungal polysaccharide . First , the results of our in vitro studies strongly suggest that GAG is the principal mediator of A . fumigatus adherence and plays a key role in biofilm formation . The mechanism by which this carbohydrate mediates adherence to substrates and binds to hyphae remains undefined . Although specific host or fungal lectins may mediate binding of Aspergillus GAG , binding to plastic is clearly independent of host receptors and must be mediated by physicochemical interactions such as charge or hydrophobicity . Further , the lack of competition observed when GAG in suspension was added to wild-type hyphae would argue against a receptor-ligand interaction . Overall , these data are most consistent with a model in which GAG functions as a glue that mediates attachment between hyphae and substrates in a highly promiscuous fashion . The findings of this study add to the growing body of evidence implicating polyhexosamine glycans as key adhesion factors for microorganisms . Work from the 1970's identified polygalactosamine compounds from Neurospora crassa and Bipolaris sorokiniana and suggested that they could potentially play a role in the adherence of fungal spores to glass surfaces 20 , 21 . Similarly , a large number of gram positive and gram negative bacterial biofilms contain polysaccharide intercellular adhesin ( PIA ) , a homopolymer of N-acetylglucosamine , which mediates adherence between bacteria and the surfaces they colonize [22] . Although composed of a different amine sugar , the similarities between these mechanisms of adherence are striking . The adhesive characteristics of PIA are in large part governed by de-acetylation of N-acetyl glucosamine residues . PIA differs from A . fumigatus GAG in which the galactosamine residues have been reported to be uniformly acetylated [16] . Nevertheless , these data suggest that the use of polyhexosamine glycans is a widespread microbial adherence strategy , and could potentially serve as a useful target for the development of antimicrobial strategies with broad applicability . The present results suggest that Uge3 is a key enzyme in the GAG biosynthetic pathway . The absence of N-acetyl galactosamine in the cell wall of the uge3 mutant strain and the absence of effects on galactofuranose synthesis suggest that this enzyme functions in the production of N-acetyl galactosamine , although experimental validation of this hypothesis is required . A minimal increase in the GlcNAc content of the cell wall of the Δuge3 mutant was also noted . Although this finding could suggest accumulation of substrate in the absence of conversion to GalNAc , it is unclear if this is a significant finding . A similar increase in GlcNAc content was also seen in the uge3 complemented strain despite a restoration of GalNAc and GAG synthesis . Similarly , we observed no difference in susceptibility to classic cell wall perturbing agents between the Δuge3 mutant and complemented strains , suggesting that the increased GlcNAC seen in both strains does not contribute to the marked reduction of adherence and virulence that was seen only in the Δuge3 mutant . Although these data suggests Uge3 therefore mediates synthesis of the N-acetyl galactosamine component of GAG , the pathways responsible for the production of galactose for the synthesis of GAG remain unknown . It is possible that Uge3 also mediates the interconversion of UDP-glucose to UDP-galactose , as epimerases with dual substrate affinity have been described [23] , however the normal levels of galactose in the Δuge3 mutant strain argue against this hypothesis . Alternately , galactose synthesis may be dependent on one of the other two putative epimerases identified within the A . fumigatus genome . The results of this and previous studies strongly suggest that GAG modulates immune responses in vivo . Previous work has suggested that GAG may be recognized by the host as a PAMP and mediate immunosuppression . Fontaine et . al . observed that a urea-soluble fraction of GAG induced neutrophil apoptosis in vitro , and that vaccination of mice with a soluble fraction of GAG enhanced the progression of invasive aspergillosis in immunocompetent and immunosuppressed mice in association with increasing Th2 and Th17 responses [16] . The experiments described here add substantially to these findings by testing the effects of live organisms deficient in GAG production in both non-neutropenic and highly immunosuppressed leukopenic mice . Our findings of increased local inflammation surrounding the Δuge3 strain support the role of GAG as an immunosuppressive molecule . The increased inflammatory response to GAG-deficient hyphae was not protective , but rather attenuated the survival advantage in mice infected with this strain when compared with highly immunosuppressed animals . These findings add support to the growing body of literature suggesting that non-protective inflammatory responses can increase mortality during infection with A . fumigatus [24] , [25] , [26] . In addition to its direct effects on the immune system , GAG likely also modulates host immune responses through cloaking β-glucan and possibly other PAMPs on the surface of hyphae . Masking of β-glucan and other cell wall PAMPs by the hydrophobin RodA has been previously demonstrated in conidia , and is thought to play an important role in immune evasion [27] . These cell wall β-glucans and other PAMPs are then exposed when the hydrophobin layer is shed during germination . However , studies examining β-glucan exposure during the growth and development of A . fumigatus hyphae have found that , as hyphae mature , the recognition of β-glucan exposure by dectin-1 decreases when compared with swollen conidia and early germinated hyphae [18] . Our results suggest that the production of GAG by maturing hyphae may account for this reduced exposure of β-glucan , and as in the case of conidia , results in an attenuation of inflammatory responses . A similar immune evasion strategy has been reported in the dimorphic fungus Histoplasma capsulatum , in which surface expression of α- ( 1 , 3 ) -glucan has been shown to mask exposure of β-glucan and reduce inflammatory responses [28] . This modulation of β-glucan exposure is the natural converse to the effects of the echinocandin antifungals , in which increasing the exposure of β-glucan is postulated to increase host inflammatory responses and improve fungal killing [29] , [30] . The findings of this study also suggest that GAG-mediated adherence may play a role in virulence . Suppression of inflammation in mice infected with the Δuge3 mutant resulted in a reduced pulmonary fungal burden and increased survival of the mice infected with the Δuge3 mutant as compared to mice infected with the wild-type A . fumigatus . It is therefore possible that this attenuated virulence and reduced fungal burden reflects the impaired ability of this mutant to adhere to , and form colonies in the lung , rather than alterations in immune mediated fungal killing . Alternately , loss of GAG may render hyphae more susceptible to host killing by microbicidal peptide or other neutrophil-independent host defences , or result in a unique growth defect seen under in vivo conditions . These studies suggest that anti-GAG strategies could be useful in the therapy of invasive aspergillosis . Importantly , however , our data would suggest that blocking GAG function would likely be a superior approach to inhibiting the synthesis of GAG , in order to avoid potentially increasing the inflammatory response , and potentially mortality , attributable to unmasking β-glucan or other PAMPs .
A . fumigatus strain Af293 ( a generous gift from P . Magee , University of Minnesota , St . Paul , MN ) was used as the wild-type strain for all molecular manipulations . The ΔmedA , Δugm1 and ΔstuA mutants and their corresponding parent and complemented strains were described previously [10] . Except where indicated , strains were propagated on YPD agar and at 37°C while exposed to light as previously described [9] . Liquid growth media were synthetic Brian medium [31] , Aspergillus Minimum Medium ( AspMM ) [32] , and RPMI 1640 medium ( Sigma-Aldrich ) buffered with 34 . 53 g of MOPS ( 3- ( N-morpholino ) propanesulfonic acid , Sigma-Aldrich ) per liter , pH 7 . 0 as indicated . When noted , pH and/or iron concentration were modified in AspMM , in order to generate a pH from 4 . 5 to 8 . 5 , and a [Fe2+] from 0 to 30 µM . Microaerophilic growth was performed using YPD or AspMM , incubated in a candle jar . The type II pneumocyte cell line CCL-185 ( lung epithelial cells A549 ) was obtained from the American Type Culture Collection , and was grown in DF12K medium containing 10% foetal bovine serum , streptomycin ( 100 mg/litre ) and penicillin ( 16 mg/litre ) ( Wisent ) . Bone marrow derived dendritic cells ( BMDDCs ) were prepared by flushing femurs and tibias of 6–8 week old C57BL/6 mice . Bone marrow cells were then cultured with culture media supplemented with either J558L culture supernatants or rGM-CSF , as previously described [33] , [34] . Marrow cells were plated at a density of 4×105 cells/ml in petri dishes containing 10 ml of culture media . For J558L supernatant-supplemented cultures , on days 3 and 6 , cells were fed an additional 1 ml J558L culture supernatant per dish , and on day 8 with 4 ml of culture media with 30% J558L culture supernatant per dish . BMDDCs were used in fungal interaction experiments after 11 days of culture . For rmGM-CSF-supplemented cultures , on day 3 marrow cells were fed an additional 4 ml of culture media , and used in experiments on day 9 . BMDDC differentiation was confirmed by flow cytometry via CD11c expression ( data not shown ) . In vitro , expression of the genes of interest was quantified by relative real-time RT-PCR analysis as previously described [38] . The primers used for each gene are shown in Table S1 . First strand synthesis was performed from total RNA with Quantitec Reverse Transcription kit ( Qiagen ) using random primers . Real-time PCR was then performed using an ABI 7000 thermocycler ( Applied Biosystems ) Amplification products were detected with Maxima® SYBR Green qPCR system ( Fermentas ) . Fungal gene expression was normalized to A . fumigatus TEF1 expression , and relative expression was estimated using the formula 2−ΔΔCt , where ΔΔCt = [ ( Cttarget gene ) sample− ( CtTEF1 ) sample]/[ ( Cttarget gene ) reference− ( CtTEF1 ) reference] . To verify the absence of genomic DNA contamination , negative controls were used for each gene set in which reverse transcriptase was omitted from the mix . Cell wall extraction was performed as previously described [39] . Alkali soluble ( AS ) and alkali insoluble ( AI ) fractions were extracted as previously described [40] . Monosaccharides were determined by gas chromatography after hydrolysis , reduction and peracetylation of the AI and AS fractions [41] with meso-inositol as internal standard . Both hexose and hexosamine concentrations were expressed as percentages of the total cell wall . The ability of strains to form biofilms was tested by inoculating 6- well culture plates with 105 conidia in 1 mL of Brian broth . After incubation at 37°C for 24 h , the plates were washed , fixed and stained as previously described [9] . The capacity of the various strains of A . fumigatus to adhere to plastic , fibronectin and epithelial cells was analyzed using our previously described method [9] . Six-well culture plates were prepared with confluent monolayers of A549 epithelial cells , adsorbed with 0 . 01 mg/ml of fibronectin overnight , or left untreated and then infected with 200 germlings of the strain of interest in each well and incubated for 30 minutes . Following incubation , wells were washed 3 times with 4 mL of PBS in a standardized manner , and overlaid with YPD agar for quantitative culture . The adherence assays were performed in triplicate on at least three separate occasions . For carbohydrate supplementation experiments , either the plastic plate or the germlings were coated with the carbohydrate of interest . Briefly , the appropriate amount of extracellular GAG isolated as above , or of zymosan ( Sigma-Aldrich ) , was resuspended in 2 mL of PBS by sonication , added to the wells of a 6-well non-tissue culture plate and incubated overnight before being washed and used in the adherence assay described above . For adherence assay with glycan treated fungus , germlings were incubated for 1 hour , at room temperature , in a dilution of GAG or zymozan in PBS; then rinsed three times to remove non-adherent carbohydrate before being tested for adherence as described above . To measure the adherence of purified GAG to A549 epithelial cells , monolayers of A549 cells were grown to confluence in 96-well plate ( Nunclone , Inc ) then fixed in 4% paraformaldehyde . Cells were incubated with varying concentrations of GAG suspended in PBS , washed and then stained with fluorescein conjugated Soybean Agglutinin ( Vector Labs ) . Binding was quantified by measuring fluorescence at 495 nm using Spectramax ( Molecular Devices ) . To confirm the specificity of Soybean Agglutinin ( SBA ) for GalNAc residues of GAG , hyphae of Af293 wild-type , Δuge3 , and Δuge3::uge3 were grown on poly-d-lysine coated glass coverslip for 12 hours , fixed with 4% paraformaldehyde , co-incubated with fluorescein conjugated SBA , and imaged by confocal microscope at 488 nm ( Olympus ) . Caspofungin ( Merck ) and nikkomycin X ( Sigma-Aldrich ) were diluted in sterile deionized H2O . Calcofluor white ( Sigma-Aldrich ) was diluted in a solution of 0 . 8% KOH and 83% glycerol . Antifungal susceptibility testing was performed in accordance with the CLSI M38-A document for broth dilution antifungal susceptibility testing of filamentous fungi [44] as previously described [45] . Final dilutions of antifungals were prepared in RPMI 1640 buffered with MOPS . 100 µL of drug stock was added to 100 µL of 105 conidia/mL solution per well . Plates were examined after 24 and 48 hours of incubation and the minimal inhibitory concentration ( MIC ) was determined by visual and microscopic inspection resulting in 100% growth inhibition while the minimal effective concentration ( MEC ) was determined by visual and microscopic inspection resulting in abnormal growth . The extent of damage to epithelial cells caused by the various strains of A . fumigatus was determined using a minor modification of our previously described method [46] . Briefly , A549 cells were loaded with chromium by incubating monolayers grown in 24-well tissue culture plates with 3 µCi of 51Cr at 37°C in 5% CO2 for 24 hours . Excess chromium was removed by washing with HBSS . The labelled A549 cells were then infected with 5×105 conidia in 1 ml serum free DF12K medium . After a 16 h incubation , the medium above the cells was retrieved . The cells were then lysed with 6 N NaOH and the lysate collected . The 51Cr content of the medium and lysates was then measured in a gamma counter and the extent of epithelial cell damage was calculated . Each strain was tested in triplicate on three separate occasions , and all results were corrected for spontaneous chromium release by uninfected epithelial cells . A . fumigatus conidia were germinated for 9 h in non-tissue culture treated six-well plates in 2 ml phenol red-free RPMI 1640 medium at a concentration of 1 . 5×106 conidia/well and allowed to germinate . Next , 1 . 5×106 BMDDCs were then added to each well in 1 ml of RPMI 1640 medium . As a positive control BMDDCs were incubated with 3 µg/ml lipopolysaccharide ( purified from S . minnesota , Invitrogen ) . Following 6 hrs co-incubation , culture supernatants were collected . Total cytokine analysis in culture supernatants was performed using the Mouse Cytokine 20-Plex Panel ( Invitrogen ) , as per manufacturer's instructions , and analyzed using xPONENT analysis software . To investigate neutralization of either dectin-1 or β-glucan , BMDDCs or fungi were co-incubated for 1 h with 10 µg/ml mouse anti-dectin-1 ( Invivogen ) or 10 µg/ml Fc-dectin-1 recombinant protein ( a generous gift from G . D . Brown ) , respectively . BMDDCs were added to fungi at a MOI of 1∶2 . As controls , BMDDC or 5 µg/ml zymosan ( Sigma-Aldrich ) were co-incubated with fungi or BMDDC , respectively . TNF-α analysis in culture supernatant was performed using the Mouse TNF alpha ELISA Ready-SET-Go kit ( eBiosciences ) . The virulence of the indicated A . fumigatus strains was tested in two different murine models of invasive pulmonary aspergillosis . In the first model , male BALB/C mice were immunosuppressed by administering 10 mg of cortisone acetate ( Sigma-Aldrich ) subcutaneously every other day , starting on day −4 relative to infection and finishing on day +4 , for a total of 5 doses [47] . In the second model , the mice were immunosuppressed with cortisone acetate , 250 mg/kg subcutaneously on days −2 and +3 , and cyclophosphamide ( Western Medical Supply ) , 250 mg/kg intraperitoneally on day −2 and 200 mg/kg on day +3 [48] , [49] . For each fungal strain tested , groups of 11–13 mice were infected using an aerosol chamber as previously described [48] . An additional 8 mice were immunosuppressed but not infected . To prevent bacterial infections , enrofloxacin was added to the drinking water while the mice were immunosuppressed . Mice were monitored for signs of illness and moribund animals were euthanized . All procedures involving mice were approved by the Los Angeles Biomedical Research Institute Animal Use and Care Committee , and followed the National Institutes of Health guidelines for animal housing and care . In both models , differences in survival between experimental groups were compared using the log-rank test . The mouse studies were carried out in accordance with the National Institutes of Health guidelines for the ethical treatment of animals . This protocol was approved by the Institutional Animal Care and Use Committee ( IACUC ) of the Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center ( Animal Welfare Assurance Number A3330-01 ) . | Invasive aspergillosis is the most common mold infection in humans , predominately affecting immunocompromised patients . The mechanisms by which the mold Aspergillus fumigatus adheres to host tissues and causes disease are poorly understood . In this report , we compared mutants of Aspergillus with different adhesive properties to identify fungal factors involved in adherence to host cells . This approach identified a cell wall associated polysaccharide , galactosaminogalactan , which is required for adherence to a wide variety of substrates . Galactosaminogalactan was also observed to suppress inflammation by concealing β-glucans , key pattern associated microbial pattern molecules in Aspergillus hyphae , from recognition by the innate immune system . Mutants that were deficient in galactosaminogalactan were less virulent in mouse models of invasive aspergillosis . These data identify a bifunctional role for galactosaminogalactan in the pathogenesis of invasive aspergillosis , and suggest that it may serve as a useful target for antifungal therapy . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"fungal",
"diseases",
"aspergillosis"
] | 2013 | Aspergillus Galactosaminogalactan Mediates Adherence to Host Constituents and Conceals Hyphal β-Glucan from the Immune System |
The development of next-generation sequencing platforms is set to reveal an unprecedented level of detail on short-term molecular evolutionary processes in bacteria . Here we re-analyse genome-wide single nucleotide polymorphism ( SNP ) datasets for recently emerged clones of methicillin resistant Staphylococcus aureus ( MRSA ) and Clostridium difficile . We note a highly significant enrichment of synonymous SNPs in those genes which have been affected by recombination , i . e . those genes on mobile elements designated “non-core” ( in the case of S . aureus ) , or those core genes which have been affected by homologous replacements ( S . aureus and C . difficile ) . This observation suggests that the previously documented decrease in dN/dS over time in bacteria applies not only to genomes of differing levels of divergence overall , but also to horizontally acquired genes of differing levels of divergence within a single genome . We also consider the role of increased drift acting on recently emerged , highly specialised clones , and the impact of recombination on selection at linked sites . This work has implications for a wide range of genomic analyses .
The populations of many pathogenic bacterial species exist as a collection of discrete clonal complexes , many of which have emerged recently and exhibit specific resistance or virulence attributes . For example , molecular techniques such as multilocus sequence typing ( MLST ) [1] have identified a small number of widely disseminated methicillin resistant S . aureus ( MRSA ) clonal lineages [2] , [3] . Of these , the MLST haplotype Sequence Type ( ST ) 239 is the most common globally [4] , [5] , [6] , [7] , [8] , [9] , [10] . This clone is multiple antibiotic resistant , shows increased virulence [11] , [12] , and is known to have emerged through a very large ( 635 kb ) homologous replacement via an unknown mechanism [13] , [14] , [15] . Harris et al recently sequenced a global sample of 63 isolates of this clone using the Illumina Genome Analyser ( IGA ) platform [16] . Read mapping was carried out against a completely sequenced ST239 isolate ( TW20 ) which caused an outbreak in a London hospital [11] [14] , and this approach yielded powerful evidence concerning the global dissemination of this clone . Comparable studies using the Illumina platform have subsequently been carried out on single clones in other pathogenic species , including Streptococcus pneumoniae [17] and Clostridium difficile [18] . Robust phylogenetic and evolutionary analysis on many bacterial genomes , and S . aureus in particular , relies on drawing a clear distinction between the “core” and “non-core” genomes [19] , [20] , [21] , [22] , [23] . The S . aureus core genome is stable , with modest levels of sequence divergence ( typically ∼1 . 5% ) , low rates of homologous recombination , and a high degree of synteny [24] , [25] , [26] . In contrast , the non-core genome is highly diverse and dynamic due to high rates of horizontal transfer of accessory elements , including a number of genomic islands , various “types” of the SCCmec chromosomal antibiotic resistance cassette , and prophages encoding various toxins and other virulence attributes [25] , [27] , [28] . Harris et al defined core SNPs conservatively as those affecting contiguous regions ( >1 kb ) that were universally present within all the ST239 isolates sequenced [16] . A very low rate of homoplasy confirmed that the vast majority of the core SNPs arose by recent de novo mutation , rather than recombination . Analysis of the core data also suggested that ST239 first emerged in the mid 1960s , shortly after the first administration of methicillin in 1959 . Intuitively , it might be expected that non-core genes will exhibit a higher proportion of non-synonymous change than core genes . As non-core genes are not ubiquitous and therefore - by definition - non-essential , they might be under weaker selective constraints . Additionally , non-core genes encoding virulence factors may be subject to positive selection from the immune response [29] . However , non-core genes are typically acquired by horizontal transfer , and tend to exhibit striking mosaic structure , implicating a long history of recombination [23] , [30] , [31] , [32] , [33] . It follows that the de novo ( mutational ) emergence of many SNPs in the non-core may pre-date the emergence of the clonal lineage in which they are observed , particularly so if the clone has emerged very recently and spread very rapidly , as is the case with ST239 . The age of the SNPs is important because very recently emerged de novo mutations tend to contain a high proportion of non-synonymous changes , not as a result of positive selection but because purifying selection has had insufficient time to purge slightly deleterious mutations [34] , [35] , [36] , [37] , [38] . Whilst this effect might be pronounced for recently emerged SNPs in the core genes , many of the non-core SNPs will have already passed through a selective filter in the wider population prior to transfer and so should show a lower proportion of slightly deleterious ( non-synonymous ) changes . An important caveat of this time-dependence model is that the relative enrichment of non-synonymous changes within mutational SNPs should only be apparent when comparisons are made between very closely related isolates belonging to the same recently emerged clone , and recombination should have little impact on dN/dS when more diverged strains of the species are considered . Furthermore , core genes which have experienced homologous replacement originating from an external lineage should , like non-core genes , also show an enrichment of synonymous change . This provides the means to test alternative explanations which assume differing selective pressures acting on core and non-core genes . In addition to the age of the SNPs , it is also necessary to consider variation in the efficiency of selection , at both a genome-wide level and at linked sites . For example , the adoption of a highly specialised niche would decrease the effective population size leading to greater drift ( higher dN/dS ) . This would be more pronounced in the core relative to the mobile non-core , as the latter has been acquired horizontally from the wider population . Local rates of recombination also impact on the degree to which selection removes linked neutral variation [39] , [40] . Regardless of whether selection is positive ( hitch hiking [41] ) or negative ( background selection [42] ) a greater degree of linked neutral variation will be removed in lowly recombining regions . This might also account for differences in dN/dS between core ( non-recombining ) and non-core ( recombining ) components of the genome . Unlike the time dependence model , there is no obvious reason why these effects should be restricted to very short evolutionary time-scales ( within single clones ) . Here we revisit the genome-wide ST239 SNP data for S . aureus [16] and C . difficile [18] to examine the impact of recombination on the proportion of synonymous and non-syonymous SNPs . For S . aureus , we confirm a higher rate of recombination and a highly significant enrichment of synonymous changes in the non-core . Further , we note a striking time dependence of dN/dS in non-core genes , whereby the more diverged elements ( older SNPs ) correspond to lower dN/dS ratios . However , our results also point to a moderation of the efficiency of purifying selection ( i . e . increased drift ) in core genes of S . aureus ST239 , possibly owing to ecological specialisation and a reduction in the effective population size . This difference between core and non-core is not evident when more diverged ( interclonal ) comparisons are made , which argues against a strong effect from selection at linked sites . We note a similar enrichment of synonymous SNPs in core genes which have experienced homologous replacement , both in S . aureus and in Clostridium difficile . This confirms that the effect is not limited to S . aureus , and controls for the possibility of differing selection pressures acting on the core and non-core .
Based on the core definition of Harris et al [16] ( described in Methods ) , and including intergenic SNPs , we note a total of 4250 core SNPs ( affecting 1492 core genes ) , and 2459 non-core SNPs ( affecting 257 non-core genes ) within 63 isolates composing the ST239 dataset . In total , there are 2562750 bp within the core region and 454915 bp within the non-core region , hence the overall SNP densities are 1 . 658×10−3 SNPs per site for the core , and 5 . 405×10−3 SNPs per site for the non-core . The higher SNP density in the non-core reflects the high proportion of mobile ( “extra-chromosomal” ) elements , including prophage , genomic islands and other MGEs ( Figure 1 ) [14] . Note that the “extrachromosal” category in Figure 1 refers to the likely horizontal origin of these genes , and all SNPs are physically located on the chromosome . Short IS elements may also be assigned as non-core , as will regions that have encountered small deletions ( Figure 1 ) . In order to check that the data is not overly biased by the presence of a large number of non-core genes in a small number of isolates , we checked how many non-core genes were present in only one strain , any two strains , three strains and so on . As many non-core genes are partially mapped , we took both the most inclusive definition of gene “presence” ( at least one mapped read within the CDS ) , and the most exclusive definition ( 100% of the CDS is mapped ) ( supplementary Table S1 ) . Plotting the cumulative proportions of non-core genes present in 1 , 2 …63 isolates based on these two definitions revealed that essentially all non-core genes were present in at least 13 isolates , and >50% of non-core genes were present in at least 40/63 isolates ( supplementary Figure S1 ) . Harris et al noted that recombination has been rare in the core , a view supported by the paucity of homoplasies [16] . In contrast , the frequent horizontal transfer , mosaic structure and modular nature of mobile elements in the non-core is consistent with frequent recombination [43] . To directly compare levels of recombination in the core and non-core , we constructed neighbour-net networks , as implemented in Splitstree 4 [44] , based on core and non-core SNPs separately ( Figure 2 , A and B ) . Whereas no reticulation is apparent in the core genome ( Figure 2A ) , confirming low rates of recombination , extensive reticulation is noted for the non-core , consistent with high rates of recombination ( Figure 2B ) . This impression is borne out by the phi test which provided significant evidence of recombination for the non-core ( P<0 . 001 ) , but no significance evidence for recombination for the core ( P>0 . 1 ) . Table 1 gives the total numbers and percentages of synonymous , non-synonymous and intergenic SNPs in the core and non-core . Whereas there are more than twice as many non-synonymous than synonymous SNPs in the core genome , for the non-core the reverse is true . A chi-squared test confirms that the core is highly significantly enriched for non-synonymous changes , relative to the non-core ( χ2 = 719 . 325 , p<0 . 00001 ) . Given higher rates of recombination in the non-core , this observation provides a simple explanation as to why there is more reticulation when networks are reconstructed from all synonymous SNPs as compared to all non-synonymous SNPs ( Figure 2 , C and D ) . The non-core therefore differs from the core in three respects: i ) a higher SNP density , ii ) a higher rate of recombination and iii ) a higher proportion of synonymous change . We expanded this analysis by dividing the non-core SNPs into two approximately equal subsets; dispersed ( <5 SNPs per 100 bp; n = 1469 ) and clustered ( >5 SNPs per 100 bp; n = 986 ) . We note over twice as many non-synonymous SNPs in the dispersed set ( n = 476 ) compared to the clustered set ( n = 207 ) . In contrast , the numbers of synonymous SNPs are almost identical in the two subsets ( 694 and 695 respectively ) . This demonstrates that a higher proportion of synonymous SNPs are noted within regions of increased SNP density , even when only considering different non-core genes ( χ2 = 72 , p<0 . 005; Table 1; Supplementary Figure S2 ) . We note that the non-core regions of high SNP density tend to correspond to the mobile elements ( e . g . categorised as “extrachromosomal” in Figure 1 ) . Non-core genes not included in this category are less SNP dense and show approximately equal numbers of non-synonymous ( n = 148 ) and synonymous ( n = 144 ) SNPs . To complement the analyses described above we used BEAST to examine the degree of rate variation for the core and non-core SNPs . This approach provides an additional means to examine the relative strengths of selection on the core and non-core genome . In the absence of strong selection we would not expect significant rate variation between synonymous and non-synonymous SNPs . In contrast , strong purifying selection would decrease the rate of change for non-synonymous SNPs , resulting in site variation . Model selection was carried out as described in Methods , and the Akaike Information Criterion ( AIC ) and Bayesian Information Criterion gave essentially identical results . The best DNA substitution scheme for the core data set was TVMef , whereas the non-core selected the TVM scheme ( these schemes are identical except the former assumes equal base frequencies ) . Far greater rate variation among sites was found in the non-core data than in the core , which is expected as non-core SNPs represent imports from multiple donor lineages . None of the robustly supported models required correction for rate variation among sites in the core genes , whereas all the robustly supported models for the non-core data required a correction for rate variation among sites . Furthermore estimates of the alpha parameter were far lower for the non-core data ( ∼3 . 7 ) than for the core data ( approaching 100 ) . Whereas neither synonymous or non-synonymous SNPs ( when considered separately ) required correction for rate variation in the core data , both required this correction for the non-core , and in this case non-synonymous SNPs showed more rate variation ( alpha parameter = 2 . 86 ) than synonymous SNPs ( alpha parameter = 4 . 73 ) . Figure 3A shows the relative rates of change for synonymous and non-synonymous SNPs in the core . Although the distributions substantially overlap , the mean rate is marginally faster for the synonymous SNPs and a larger variance is evident for the non-synonymous SNPs . This is consistent with a wider range of selective consequences of non-synonymous SNPs , and the initial purging of the most deleterious class . Figure 3B shows the relative rate distributions for the synonymous and non-synonymous SNPs in the non-core . In this case the distributions are significantly non-overlapping , consistent with the selective removal of a far higher proportion of slightly deleterious non-synonymous SNPs ( the mean rate and confidence intervals are given in the figure legend ) . The above analyses demonstrate that core SNPs are the least densely clustered , and show a much lower proportion of synonymous change than the non-core . As core SNPs are likely to have emerged more recently ( on average ) than non-core SNPs , this is consistent with the time dependency of dN/dS noted previously between genomes of differing levels of divergence [34] . However , it is not clear whether this time dependency is sufficient to explain the difference between the core and non-core or whether other factors are playing a role . For example , it is possible that a reduction in the genome-wide effective population size may have resulted in increased drift on core genes . Low rates of recombination also decrease the local population size by increasing background selection , and this could contribute to the relative paucity of neutral variation in core genes . In order to disentangle these different effects we first controlled for time dependence by comparing core and non-core genes at similar levels of divergence . We calculated dN/dS for all 1953 pairwise comparisons of the 63 isolates , separately for the core and the non-core regions . For each pair we estimated divergence time by calculating the divergence at synonymous sites ( again for the core and non-core separately ) . Figure 4 , main panel , plots the average synonymous site divergence against the average dN/dS for 39 bins , each of 50 pairwise comparisons . This plot confirms the high dN/dS ratio in the core , relative to the non-core , and reveals that the maximum synonymous SNP density in the core is over an order of magnitude lower than that of the non-core . Figure 4 , bottom left , rescales the plot in order to clarify the patterns in the core data and the most conserved 5 bins of the non-core , where levels of divergence overlap . We note that the most conserved bins for both the core and non-core correspond to a dN/dS ratio approaching parity , thus the most recent mutations have been subject to very little purifying selection , regardless of whether they emerged in core or non-core genes . However , whereas the subsequent decrease in dN/dS for the non-core is striking , and closely fits a power law ( R2 = 0 . 96 ) ( Figure 4 , bottom right ) , the plot for core genome follows a very shallow trajectory . This strongly suggests that time dependence alone cannot explain the differences in dN/dS between the core and non-core genomes . A decrease in the efficiency of purifying selection owing to a reduction of the effective population size might explain the relatively slow purging of non-synonymous mutations in the core [34] . This weakened selection may have resulted from the rapid global spread of ST239 , combined with specialised adaptation to the hospital environment . Such an ecological shift would disproportionately affect the core SNPs as these are more likely to have emerged de novo since the emergence of the clone . The high rates of recombination experienced by non-core genes might also act to buffer against increased drift acting elsewhere on the genome by maintaining a higher local effective population size . It is well documented that in eukaryotic genomes low rates of recombination are associated with low levels of neutral variation [45] , [46] , [47] , [48] , [49] , and that recombination facilitates protein adaptation [50] . An important mechanism underlying this is background selection , whereby the selective purging of deleterious mutations results in the loss of neutral variation at linked sites [42] . The emergence of an adaptive mutation has a similar effect through hitch hiking [41] , and in both cases the size of the genomic region affected is determined by the local rate of recombination [40] . The effect will be stronger in lowly recombining regions of the genome where more neutral variation remains linked to the site under selection . The role of positive selection on core genes should also be considered [29] , along with the possibility that recombination is mutagenic [51] ( although it is unclear in this latter case how this can explain the strong enrichment of synonymous change in recombining genes ) . When considering these alternative hypotheses , it is important to emphasize that the analyses described thus far considers isolates within a single clone , which probably emerged under 50 years ago [16] and thus corresponds to a tiny fraction of the diversity in the species as a whole . Our hypothesis of time dependence is broadly distinct from the alternative explanations listed above , in that it predicts that the high proportion of non-synonymous change in core genes should be substantially moderated over greater levels of divergence , such that the differences in dN/dS between core and non-core genes should diminish . Comparing a more representative sample of S . aureus genomes therefore provides a simple means to test our model of time dependence against these alternative hypotheses . To this end , we calculated the dN/dS ratio between core orthologs in TW20 ( see Methods ) and three other S . aureus genomes representing a range of divergence times: i ) within the same clonal complex ( TW20 vs USA300 ) , ii ) within different clonal complexes ( TW20 vs MRSA252 ) , and iii ) highly divergent ( TW20 vs MSHR1132 ) . This latter genome corresponds to the unusually diverged S . aureus genotype ST75 which has been recorded in Northern Australia [52] , Cambodia [53] and French Guiana [54] . For each of the three pairwise comparisons we calculated the mean dS and dN/dS , along with a standard error calculated by re-sampling the data as described in Methods . Note that we omitted those core genes corresponding to the large replacement region as described below . Figure 5 confirms that the dN/dS between core genes decreases with increasing synonymous site divergence . The mean dN/dS ratio for the intermediate comparison ( TW20 vs MRSA252 ) , which provides the most representative comparison for the species as whole , is below 0 . 1 . We note this is lower than the average dN/dS observed for the non-core regions within the ST239 clone ( 0 . 18 ) . This suggests that the difference between core and non-core genes has not only been diminished , but has reversed , in that non-core genes show a higher dN/dS than core genes when greater divergence times are considered . To confirm this we also calculated dN/dS separately for 57 orthologous non-core genes , identified on the basis of reciprocal Fasta best hits ( as described previously [14] ) , between the most divergent comparison ( TW20 vs MSHR1132 ) ( Supplementary Table S2 ) . The average dN/dS for these non-core genes is ∼0 . 1 , which is over double the average of ∼0 . 04 noted in the core genes for the same comparison . Thus the relative inflation of neutral variation associated with non-core genes within the ST239 clone is only apparent over very short phylogenetic distances , and this argues against a strong role for background selection , positive selection on core genes or the mutagenic effects of recombination as alternative explanations . The unique hybrid structure of the TW20 genome presents an opportunity to test whether the differences we observe between the core and non-core genes result from differing selection pressures on these two sets of genes . This large ( 635 kb ) homologous replacement in the ST239 genome has affected many core and non-core genes , but for this analysis we only consider core genes where orthologs can be robustly identified in both parental genomes ( see Methods ) . We compared patterns of dS and dN/dS within and outside the replacement region using the full genome sequences corresponding to the recipient clone ( USA300; ST8 ) [55] , the donor clone ( MRSA252; ST36 ) [23] and the resultant hybrid ( TW20; ST239 ) [14] . In order to gauge significance , we used a re-sampling procedure as described in Methods . Overall patterns of divergence ( dS ) confirm that the replacement region within TW20 ( hybrid ) is much more similar to MRSA252 ( the donor ) than USA300 ( the recipient ) , whilst the reverse is true for the rest of the genome ( Supplementary Figure S3 ) . When TW20 and USA300 are compared , the dN/dS ratios are much lower within the replacement ( ∼0 . 12 ) than the rest of the genome ( ∼0 . 33 ) ( Figure 6 ) . This is entirely expected , as the replacement represents an import from a diverged lineage so , similar to the non-core , should be relatively enriched for synonymous changes . The link between inflated neutral diversity and recombination therefore holds similarly for core genes which have been replaced by diverged homologous imports as it does for non core genes , and is therefore unlikely to be related to gene specific selection pressures . As a further check we confirmed that when the TW20 ( hybrid ) and MRSA252 ( donor ) genomes are compared the reverse is true; the dN/dS ratio within the replacement is higher ( 0 . 23 ) than the rest of the genome ( 0 . 08 ) ( Figure 6 ) . The analysis above demonstrates that diverged homologous replacements may result in a relative local enrichment of synonymous change . In order to check whether a similar pattern could be observed in other species , we used the genome-wide SNP data for 25 isolates belonging to a single hypervirulent clone of Clostridium difficile presented by He et al [18] . These data are well suited to this analysis , as two of these isolates ( bi11 and bi4 ) exhibit regions of high SNP density consistent with large-scale homologous recombination from outside of the clone . Following He et al . [18] we assign these blocks of high density SNPs as having arisen by homologous recombination . These blocks of recombination account for the vast majority ( 89 . 4% ) of all the SNPs detected within all the 25 isolates of this clone . Whereas the average SNP density across all strains outside of these blocks was only 6×10−5 SNPs per site , all the blocks correspond to a SNP density at least an order of magnitude higher than this , and the average SNP density within the blocks was 1 . 4×10−3 SNPs per site . Because the recombination blocks within strains bi11 and bi4 correspond to such striking peaks of SNP density , we simply identified all the 1553 SNPs within these two strains likely to have arisen by recombination by visual inspection of the SNP alignment ( Supplementary Figure S4 ) . We then compared the number of synonymous , non-synonymous and intergenic SNPs within the blocks with the remaining 184 SNPs ( Table 2 ) . Consistent with expectation , a significant enrichment of synonymous SNPs was observed for those changes corresponding to the regions of densely clustered SNPs , and therefore assigned as having arisen by recombination ( χ2 = 34 , p<0 . 005 ) .
Here we have exploited four complete S . aureus genome sequences , and revisited the genome-wide SNP datasets of S . aureus [16] , and Clostridium difficile [18] to examine how recombination impacts on the level of neutral variation within recently emerged bacterial clones . A key starting point is the high level of non-synonymous change among very recently emerged mutations , and a commensurate decrease in dN/dS over divergence time [18] , [34] , [37] , [38] , [56] , [57] , [58] , [59] . For S . aureus , which is a highly structured ( clonal ) population , this is evident as a preponderance of non-synonymous change within clones ( dN/dS∼0 . 7 ) compared to between clones ( dN/dS∼0 . 1 ) [23] , [24] . Given this framework , it is clear that the importation of DNA into a bacterial clone from elsewhere is predicted to introduce a relative preponderance of synonymous change , and this is strongly supported by our data . Furthermore , we note that the proportion of synonymous change increases as the donor lineage is more divergent . This confirms that the relative enrichment of synonymous SNPs over time equally applies to genes acquired from disparate lineages within a single genome as it does to overall levels of divergence between pairs of genomes . The relationship of dN/dS to synonymous site divergence in the non-core fits closely to a power law ( R2 = 0 . 96 ) . Although more work is required to elucidate the properties of these plots for different datasets , we note that the decrease in dN/dS over greater scales of divergence ( between clones; Figure 5 ) appears to show a broadly similar relationship . Whilst it is clearly necessary to account for the time-dependence of dN/dS when comparing the strength of selection between genes , genomes or populations over very short time-scales , this model does not fully explain the difference we observe between the core and non-core genes in S . aureus . Even when controlling for this effect ( i . e . comparing genes at the same level of divergence ) the core genes show an enrichment of non-synonymous SNPs relative to the non-core . Rocha et al showed that differences in the trajectory of the decrease in dN/dS over time can be explained by changes in the effective population size [34] . This effect has been examined in detail by comparing trajectories of dN/dS over time in E . coli and highly specialised Shigella clones [36] , and the impact of increased drift through bottlenecking has also been discussed in Mycobacterium bovis [60] . It is possible that the epidemiological characteristics of ST239 may reduce the effective population size , thus weakening the efficiency of purifying selection . Since its emergence in the mid 1960s this clone has disseminated globally , and it has been estimated that it currently causes >90% of all cases of hospital-acquired MRSA within regions which together account for >60% of the global population [7] . In contrast to methicillin sensitive S . aureus ( MSSA ) , which are typically carried asymptomatically in the community [61] , ST239 is very rarely noted outside of the hospital environment . Direct transmission between hospitals must therefore play a large role in the global dissemination of this clone , and this mode of dissemination will incur substantial bottlenecking if very limited variation is introduced into any given hospital . Commenting on the large number of impressively reinforced , yet extinct , species in the fossil record , Haldane remarked that “… in some cases the species literally sank under the weight of their own armaments” [62] . In the case of S . aureus ST239 , which has already been replaced in Western Europe and is currently being replaced in other parts of the world [63] , [64] , the “armament” of multiple drug resistance may be costly in terms of cell function and resources , but also in terms of restricting the competitiveness of the clone to those health care settings where antibiotics are most aggressively deployed . The argument above has two important implications . First , it raises the possibility that rapidly emerging clinically important clones which are exclusively maintained in health-care settings are inevitably self-limiting . This may help to account for the cycles of clonal expansion and replacement commonly noted within S . aureus and other pathogen populations [65] . It will be interesting to examine this further by comparing the trajectories of dN/dS over time in samples representing different ecological constraints and effective population sizes . Second , this analysis provides a novel approach for detecting increased drift through comparisons between core and non-core genes within a single genome . This contrasts with whole genome comparisons of different lineages as carried out previously for E . coli and Shigella [36] . The current study also illustrates the importance , and potential , of considering evolutionary processes within the context of the age and history of different genomic regions , rather than purely in terms of direct selective effects . Such a perspective underpins studies on the likely fate of acquired genes [66] , [67] , [68] or the effects of age on the functional and selective stability of proteins [69] . To what extent can selection at linked sites , leading to variation in local effective population size ( as determined by the rate of recombination ) , explain our data ? Although background selection [42] has been discussed extensively for eukaryotes , its role in bacterial evolution remains almost completely unknown . Touchon et al demonstrated a lower rate of recombination , a lower value of Tajima's D and a higher dN/dS ratio around the terminus of replication in E . coli [67] . These authors interpreted the co-occurrence of low rates of recombination and an enrichment of slightly deleterious change as evidence of background selection . Whereas we have argued that recombination introduces neutral variation into the genome from a diverged lineage , an advocate of background selection would argue that recombination has saved much of the neutral variation that has emerged de novo within the genome which would otherwise have been lost . As illustrated by Figure 4 , main panel , this would need to be a very powerful force because the maximum level of neutral variation in the non-core of the ST239 data is at least an order of magnitude greater than that in the core . We note in this context a broad distinction between recombination in eukaryotes , where the evolutionary consequences tend to be viewed in terms of the process itself ( e . g . background selection , biased gene conversion or the Hill-Robertson effect ) , and recombination in bacteria , which ( because it is less common , but can occur over large phylogenetic distances ) is typically more simply viewed in terms of how the incoming SNPs ( or genes ) directly impact on the genome . We argue that our primary observation –the relative enrichment of synonymous SNPs within recombined regions at intra-clonal ( but not inter-clonal ) scales– is most parsimoniously explained by this latter perspective . Although the efficiency of purifying selection may have been compromised within the ST239 clone , our analysis confirms the decrease of dN/dS on the core genome over longer evolutionary time-scales which encompass the variation within the species . This means that the difference in the proportion of neutral variation between the non-recombining core and the recombining non-core disappears , or is even reversed , after the very initial ( intra-clonal ) stages of diversification . It is not obvious how to reconcile this observation with background selection , although we do concede the possibility that expansion of the “population” in this way may have unpredictable consequences concerning the relative effective population sizes of highly and lowly recombining regions . If positive selection played a major role in the emergence of high levels of non-synonymous changes in the core , or if the putative mutagenic effect of recombination enhanced neutral variation in the non-core , then we would expect these patterns to be maintained over greater levels of divergence . The analysis of the large homologous replacement in TW20 suggests that it is not the process of recombination per se which acts to inflate neutral variation , as this effect is apparent only in those cases where the imported region ( MRSA252-like ) is more divergent than the two comparator genomes ( USA300 and TW20 ) are to each other . If this is not the case , then recombination will have the opposite effect and remove neutral variation that has accumulated between the parental lineages ( this applies when comparing TW20 with MSSA252 ) . Finally , as this analysis is based on the core genes , the differences we observe in the SNP data are unlikely to be due to differing selective constraints acting on core and non-core regions . One curious observation remains . For both the S . aureus and C . difficile data , the percentage of intergenic SNPs is lower when the changes are assigned as having been acquired horizontally from outside the clone ( non-core or recombined; Tables 1 and 2 ) . In the case of the non-core S . aureus data , this might be partly explained by difficulties in mapping very short and diverged intergenic sites in phage . However , this cannot explain the C . difficile data , where we note there is no significant difference between the numbers of intergenic and non-syonymous SNPs in the recombined and non-recombined datasets ( in contrast , the difference between intergenic and synonymous SNPs is highly significant ) . Assuming that intergenic and synonymous SNPs are approximate selective equivalents , at least compared to non-synonymous SNPs , then this is the opposite trend to that expected . The reasons for the relative paucity of intergenic ( compared to synonymous ) SNPs within the non-core ( S . aureus ) or recombined ( C . difficile ) datasets may hint at selection on intergenic sites , and this observation clearly warrants further attention . In conclusion , here we demonstrate that the dN/dS ratio varies according to the level of divergence and the past history of recombination between different genes , as well as due to population level effects associated with ecological specialisation . Whilst further work is required to elucidate the possible effects of selection at linked sites in natural bacterial populations , it is clear that variation imported into recently emerged bacterial clones from diverged lineages will contain a relatively high proportion of synonymous SNPs . This effect is not restricted to non-core genes , and the analysis on the C . difficile data demonstrates it extends to species other than S . aureus . As imported SNPs have been pre-filtered by purifying selection , so one might expect to see a general increase in the relative rate of recombination moving backwards in the tree as de novo mutations will tend to be purged more rapidly than recombination events . Recent studies have discussed the rapid rate of mutation at the very tips of the trees , which decreases over time as mutations are purged [35] , [70] , but it is not yet clear whether the rate of recombination shows a similar decrease . More practically , we propose that the enrichment of local synonymous change within bacterial clones might be used as an additional diagnostic to identify recombination events , thus facilitating detailed studies into their size and frequency or , through their subsequent removal , more robust phylogenetic analysis . Finally , although this work has focussed on the selective removal of slightly deleterious non-synonymous changes , future studies might also consider possible selective costs of SNPs at synonymous and intergenic sites [36] , [71] , [72] , [73] .
We used the core and non-core SNP datasets as previously defined by Harris et al [16] . The core genome was identified conservatively and objectively , as all sequences >1 kb that are present in all 63 isolates . Note that non-core regions present in the query strains , but absent in the reference sequence ( TW20 ) , are excluded . Rather than representing novel elements , the non-core SNP data in this study corresponds to the allelic variation of the accessory elements present in TW20 ( e . g . polymorphisms present between closely related variants of the same non-core element ) . We computed the proportion of synonymous , non-synonymous , and intergenic SNPs for both core and non-core data sets using scripts written in Python . We used four alignments in these analyses: i ) all the synonymous and non-synonymous SNPs located in the core regions , ii ) all the synonymous and non-synonymous present in the non-core regions , iii ) all the synonymous SNPs irrespective of their presence in core or non-core regions , iv ) all the non-synonymous SNPs regardless of their presence in core or non-core regions . For each alignment neighbour-nets were constructed using uncorrected p distances and were drawn using the Equal Angle method . Additionally , the Phi test for detecting recombination was conducted on all 4 alignments . All these analyses were carried out using SplitsTree 4 [44] . Statistical selection of models of nucleotide substitution was carried out using jModelTest [74] on an alignment of all SNPs for each data set , and on separate partitions ( see below ) . Likelihood scores were computed for the different models . Corrections for unequal base frequencies and rate variation among sites were allowed , but we did not consider the correction for invariable sites as all SNPs are , by definition , variable . The Akaike Information Criterion ( AIC ) and Bayesian Information Criterion were used to perform model selection . The models used were: [TVMef SYM TVM GTR TVMef+G SYM+G TVM+G GTR+G TPM1 TIM1ef TPM1uf TIM1 K80 TrNef TPM1+G HKY TIM1ef+G TrN TPM1uf+G TIM1+G K80+G TrNef+G HKY+G TrN+G F81 JC F81+G JC+G] . We used the Bayesian methods implemented in BEAST [37] to estimate the rate of evolution for core and non-core SNPs . As the date of isolation was known for each single strain , we could calibrate the inferred phylogenies . In order to estimate relative rates , two data partitions ( one consisting of the synonymous SNPs and other made up of non-synonymous SNPs ) were used for the core and non-core data . These partitions were set up by editing the BEAST XML input files , as described in the BEAST manual ( beast-mcmc . googlecode . com/files/BEAST14_Manual_6July2007 . pdf ) . Since biological data sets will best fit a relaxed molecular model , which assumes independent rates on different branches , rather than a strict clock model , we used such an approach . The uncorrelated log-normal relaxed clock model was employed , using the GTR model with a gamma distribution for rate variation among sites . Substitution rate , rate heterogeneity , and base frequencies parameters were unlinked across partitions , otherwise default priors were used . For each analysis , one chain was run for 20 , 000 , 000 steps , and samples were taken every 2 , 000 steps . The first 2 , 000 steps were discarded as burn-in , and convergence was evaluated through TRACER , by examining the effective sample size ( ESS ) of the mean substitution rates and by examining trace plots of the likelihood scores . The genomes used were those of S . aureus TW20 ( hybrid ) , S . aureus MRSA252 ( donor ) , and S . aureus USA300 ( recipient ) . We identified 2207 orthologues present in all three genomes by a reciprocal Fasta analysis performed previously [14] . Of these , 36 were discarded as pseudogenes leaving 2171 genes . The co-ordinates of the large homologous replacement ( with respect to the TW20 genome ) were taken as position 1… 427725 and position 2848037 … 3043210 ( note there is only one contiguous block , but this passes through the origin of replication so is located at either end of the linear sequence ) . All orthologous genes which fell completely within these boundaries were assigned as REC ( 357 genes ) , whilst those orthologues which fell outside were assigned as NON-REC ( 1755 genes ) . Note that many of the genes falling within these boundaries in TW20 belong to non-core elements ( e . g . SCCmec , SCCmer , prophage φSa1 , Tn552 and ICE6013 ) , and as orthologues of many of these CDSs could not be identified in both MRSA252 and USA300 they were excluded . The exclusion of these non-core genes also allows us to test for gene specific effects between core and non-core genes . Individual alignments were made for each of the remaining genes as follows: first , using the protein sequences which are the translations of the genes we created protein alignments through MUSCLE [75] and , then , we used the program TRANALING , from The European Molecular Biology Open Software Suite [76] , to generate alignments of the nucleic coding regions from the protein alignments ( this was done in order to have DNA alignments in frame ) . To gauge the significance of differences in dS and dN/dS between the recombinant region and the rest of the genome , and to control for the different sizes of the datasets , we used a re-sampling procedure . We randomly sampled ( with replacement ) gene alignments until their concatenated length exceeded 378747 bp ( the concatenated length of the 372 core genes of the recombinant region ) . This was repeated 200 times for the recombinant region and 200 times for the rest of the genome . For each replicate , pairwise dS and dN/dS values were computed from the concatenated alignments using the codeml program in PAML [77] to estimate synonymous substitution rates ( dS ) , non-synonymous substitution rates ( dN ) , and the ratio of the two ( dN/dS ) . We specified “runmode = −2” in the control file to set pairwise calculations . In addition to the 3 genomes used for the analysis of the large replacement , we included the genome of S . aureus MSHR1132 that is distantly related to the other 3 genomes . In this analysis we kept only those orthologues located in the non-recombinant region ( NON-REC orthologues , n = 1755 genes , see above ) . Using these orthologues , we generated 200 replicates of concatenated alignments following the re-sampling procedure mentioned above . Pairwise dS and dN/dS values were then computed for each replicate through PAML as previously described . | As bacteria diversify , many of the nucleotide changes that emerge will render the cell slightly less competitive , and these mutations will tend to be removed by natural selection . However , this purging process does not happen instantaneously , and this delay allows deleterious mutations to survive in the population long enough to be sampled . Genomes at the very initial stages of diversification therefore exhibit a relatively high proportion of slightly deleterious mutations and , as most of these are non-synonymous mutations , this is manifest as a high dN/dS ratio . However , the effective population size will also impact on this ratio , as will selection operating on neighbouring SNPs . Here we examine the distribution of synonymous and non-synonymous SNPs within recently emerged clones of two important nosocomial pathogens , methicillin resistant Staphylococcus aureus ( MRSA ) and Clostridium difficile . In both species , we note a much higher proportion of synonymous changes in those single nucleotide polymorphisms ( SNPs ) likely to have emerged through recombination compared to de novo mutations . We argue that this effect is explained by the very recent emergence of the mutational SNPs combined with a reduction in the efficiency of selection due to niche specialisation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"bacteriology",
"population",
"genetics",
"evolutionary",
"selection",
"microbiology",
"genome",
"sequencing",
"mutation",
"effective",
"population",
"size",
"microbial",
"evolution",
"staphylococci",
"bacterial",
"pathogens",
"forms",
"of",
"evolution",
"genetic",
"polymorphism",
"microbial",
"pathogens",
"comparative",
"genomics",
"biology",
"evolutionary",
"genetics",
"genetic",
"drift",
"gram",
"positive",
"microevolution",
"natural",
"selection",
"genomics",
"evolutionary",
"biology",
"genomic",
"evolution",
"bacterial",
"evolution",
"evolutionary",
"processes",
"genetics",
"and",
"genomics"
] | 2011 | The Impact of Recombination on dN/dS within Recently Emerged Bacterial Clones |
Viral diseases transmitted via Aedes mosquitoes are on the rise , such as Zika , dengue , and chikungunya . Novel tools to mitigate Aedes mosquitoes-transmitted diseases are urgently needed . We tested whether commercially insecticide-impregnated school uniforms can reduce dengue incidence in school children . We designed a cluster-randomised controlled trial in Thailand . The primary endpoint was laboratory-confirmed dengue infections . Secondary endpoints were school absenteeism; and impregnated uniforms’ 1-hour knock-down and 24 hour mosquito mortality as measured by standardised WHOPES bioassay cone tests at baseline and after repeated washing . Furthermore , entomological assessments inside classrooms and in outside areas of schools were conducted . We enrolled 1 , 811 pupils aged 6–17 from 5 intervention and 5 control schools . Paired serum samples were obtained from 1 , 655 pupils . In the control schools , 24/641 ( 3 . 7% ) and in the intervention schools 33/1 , 014 ( 3 . 3% ) students had evidence of new dengue infections during one school term ( 5 months ) . There was no significant difference in proportions of students having incident dengue infections between the intervention and control schools , with adjustment for clustering by school . WHOPES cone tests showed a 100% knock down and mortality of Aedes aegypti mosquitoes exposed to impregnated clothing at baseline and up to 4 washes , but this efficacy rapidly declined to below 20% after 20 washes , corresponding to a weekly reduction in knock-down and mosquito mortality by 4 . 7% and 4 . 4% respectively . Results of the entomological assessments showed that the mean number of Aedes aegypti mosquitoes caught inside the classrooms of the intervention schools was significantly reduced in the month following the introduction of the impregnated uniforms , compared to those collected in classrooms of the control schools ( p = 0 . 04 ) Entomological assessments showed that the intervention had some impact on the number of Aedes mosquitoes inside treatment schools immediately after impregnation and before insecticidal activity declined . However , there was no serological evidence of protection against dengue infections over the five months school term , best explained by the rapid washing-out of permethrin after 4 washes . If rapid washing-out of permethrin could be overcome by novel technological approaches , insecticide-treated clothes might become a potentially cost-effective and scalable intervention to protect against diseases transmitted by Aedes mosquitoes such as dengue , Zika , and chikungunya . ClinicalTrials . gov NCT01563640
Aedes is a genus of mosquitoes originally found in tropical and subtropical zones , but which is now widespread in most continents . The current geographical distribution is the widest ever recorded and there is potential for further spread [1] . Over 50% of the world’s population live in areas where they are at risk of Aedes transmitted infections [2] . Aedes mosquitoes transmit a large array of viral diseases , notably dengue , Zika , yellow fever , West Nile encephalitis , chikungunya viruses . Aedes mosquitoes are predominantly active during daylight hours , well adapted to urban living , and are difficult to control with currently available control strategies [3] . Although novel approaches , such as Wolbachia-infected mosquitoes or genetically modified male mosquitoes , appear promising [4 , 5] , their use remains controversial and they are unlikely to be implemented at a large scale in the near future . The current Zika virus outbreak in Latin America , associated with severe complications such as microcephaly and neurological complications [6] , accentuates the urgent need to develop additional novel approaches that are simple and rapidly scalable; in particular personal protection approaches to protect individuals at high risk such as pregnant women . Since Aedes aegypti is a day-biting mosquito , developing technologies that can be applied during the day to offer protection against mosquito bites should be top priority . Permethrin is a pyrethroid-based insecticide registered by the US Environmental Protection Agency ( EPA ) since 1977 that has been extensively used as insect repellent and insecticide with a documented safety record [7] . Permethrin can be bound to fabric fibres in clothing via different techniques such as micro-encapsulation and polymer coating [8 , 9] . Insecticide-treated clothing has been used for many years by the military and in recreational activities as personal protection against bites from a variety of arthropods including ticks , chigger mites , sandflies and mosquitoes and is thought to be safe [7] . Insecticide-treated clothing has been reported to give between 0% and 75% protection against malaria and between 0% and 79% protection against leishmaniasis [7] . No field trials have been conducted to demonstrate the efficacy against Aedes-transmitted diseases . However , one study showed that wearing permethrin-treated clothing resulted in a reduction in the number of Aedes bites by 90% [10] suggesting that it could potentially be a promising intervention for Aedes-transmitted diseases such as Zika , dengue , and chikungunya . Of all Aedes-transmitted diseases , dengue is the most frequent arboviral disease globally , with an estimated 390 million infections annually [11] . Children carry a significant burden of morbidity and mortality for dengue with a higher rate of more severe disease than adults [12] . Because children spend most of their day at school at a time of peak biting behaviour of Aedes mosquitoes , personal protective measures such as impregnated clothing should be investigated . As school children in most dengue endemic countries wear school uniforms as a social norm , impregnated school-uniforms could be easily scaled up as a national programme , if such a strategy were proven to be impactful . InsectShield manufactures permethrin-impregnated apparel for recreational and military purposes . InsectShield Repellent Apparel is registered by the US EPA [13]; their approach is a polymer-coating technique which is claimed to withstand up to 70 washings [13] . InsectShield clothing was successfully used for tick bite prevention [14]and also protective against mosquito bites by measuring changes in antibody titers to mosquito salivary gland extracts [15] . We conducted a school based field trial to assess the efficacy of InsectShield school uniforms on reducing dengue infections in children .
The study area is located about 150 km east of Bangkok , in Plaeng Yao District , Chachoengsao Province , Eastern Thailand . The region covers an area of 237 km2 with a population of 36 , 607 , with a total of 24 schools . Official permission from the Office of Basic Education Commission was obtained . School directors were informed about the trial , and 10 school directors agreed to participate . In these 10 schools , students in grades one to nine , aged 6 to 17 years , were eligible to participate if parental consent was given . Awareness seminars for the school senior management , teachers and parents were held prior to the trial to ensure high recruitment rates and compliance . The rainy season associated with the highest dengue incidence runs from June to October , and the school term ( corresponding to the study period ) was from mid-June to mid-November . The protocol of this school-based trial was reviewed and approved by the Mahidol University Institutional Review Board ( MU-IRB 2009/357 . 1512 ) . The intervention was permethrin-impregnated school uniforms . The impregnation process involved washing and then coating the uniforms in a proprietary process resulting in 0 . 054 mg/m2 permethrin ( InsectShield USEPA2009 ) . As , hypothetically , individuals wearing insecticide-impregnated clothing could also provide indirect protection to others not wearing impregnated clothing , we randomised the intervention by school , rather than by individual . To ensure acceptability by the school senior management and to ensure real-life scenarios , we used locally used school uniforms , comprising the standard school uniform , Scouts uniform , sports uniform and cultural uniform . Uniforms were typically short-sleeved and only covered the legs down to the knees ( shorts or skirts ) . Computer randomisation into intervention versus control group was by school , randomised into equal groups . Only the investigator who carried out the randomisation in Sweden and the overseas impregnating factory ( InsectShield ) were aware of the allocation; schools , children and on-site investigators were blinded . To maintain blinding for the on-site investigators and schools , all uniforms ( from both intervention and control schools ) were collected and sent to the InsectShield impregnating factory , but only the uniforms of the intervention schools were impregnated . To ensure that the correct uniforms were returned to the correct owners , all uniforms received labels indicating the child’s name , class , and school . We collected blood samples via finger-prick ( <0 . 2ml ) from study subjects at the beginning and end of the school term ( June and November ) . Dengue IgG ELISA was first performed for all paired samples . A primary infection was defined as a seroconversion from baseline negative IgG to positive IgG at follow-up . If the baseline sample was IgG positive , we did an additional analysis to identify new dengue infections by using the monoclonal antibody ( MAb ) -based capture enzyme-linked immunosorbent assays ( MAb-ELISA ) to measure the increase in IgG , whereby Dengue IgG indirect ELISA was performed using purified 2H2 monoclonal antibody for coating plates on paired samples and cut-off values used as described by Johnson et al [16] . Impregnated standard uniforms were evaluated for their effect on mosquito knock-down and 24-hour mortality at baseline and after repeated washing , measured by standardised WHOPES bioassay cone tests [17] . A WHO plastic cone was secured onto the cloth using rubber bands . Batches of five nulliparous starved female Aedes aegypti mosquitoes ( 3–5 days old ) were placed in the cone via a mouth aspirator , and a small cotton plug was used to close the aperture . Bioassays were carried out at 25 ± 2°C and 65 ± 10% relative humidity . Mosquitoes were exposed to the materials for three minutes and removed using a mouth aspirator . The mosquitoes were then placed in a holding cup inside an insectary ( 25 ± 2°C and 65 ± 10% ) with a net secured over the top with two elastic bands and cotton wool soaked in 10% sugar solution . Knock-down was recorded one hour post exposure , and mortality was recorded after 24 hours . Ten replicates were carried out for each sample of 10 shirts and 10 skirts or trousers . Testing was done at baseline and repeated after weekly laundering where the uniforms were hand-washed and dried in the shade for 24 hours to simulate field conditions . Portable vacuum aspirators were used to collect adult mosquitoes indoors in the school areas at baseline and monthly following the introduction of treated uniforms . The collectors aspirated mosquitoes in 5 classrooms per school and spent 15 minutes per classroom for aspiration . BG sentinel traps , one trap per school , were placed in the corner of the corridor of each school building outside the classrooms . They were left in both treatment and control schools in the morning and were collected in the evening of the same day . The collectors collected mosquitoes from these traps in all schools on two consecutive days . Mosquitoes collected were transferred to plastic tubes and were stored on ice during the transport to the laboratory at the Center of Excellence for Vectors and Vector-Borne Diseases in Salaya Campus of Mahidol University for further processing . The collected mosquitoes were then sorted , counted and identified to genus and species according to each school before storage at -80°C . School class teachers recorded all children who were absent for at least one day . Children or their parents were contacted by phone to obtain the reason of absenteeism . Absenteeism for sick leave ( for any cause , with or without fever ) for at least one day was recorded , and those who were absent from school for at least 2 days due to a febrile illness were also documented . The primary outcome was the incidence of laboratory confirmed dengue infections during the school term in individuals wearing impregnated uniforms versus non-impregnated uniforms . Secondary outcomes were ( 1 ) the number of Aedes aegypti mosquitoes in intervention and control schools ( in classrooms , and school corridors ) and ( 2 ) the 1 hour knock-down and 24 hour mortality of Aedes aegypti mosquitoes exposed to our impregnated school uniforms as measured by WHOPES cone tests at baseline and after weekly washing . We also recorded school absenteeism for 1 day or more , and also absenteeism for 2 days or more because of a febrile illness . The sample size calculation based on the primary endpoint has been described in detail in the trial protocol [18] . We originally planned a cross-over design spanning two transmission seasons because dengue transmission can vary greatly between schools and seasons , which can to some extent be controlled for by a cross-over design . The original assumptions underlying the sample size for the trial were an incidence rate ( symptomatic plus asymptomatic ) averaging 5% during a transmission season [19] , i . e . 10% over two seasons; an effect size of halving incidence by using impregnated uniforms , and a drop-out rate ( children leaving the school or withdrawing from the trial , etc . ) of 20% . Taking into account a conservative cluster design effect of 3 , the total sample size was calculated to be 2 , 012 ( i . e . 1 , 006 in each study arm ) . Retaining the design effect of 3 ( since we did not gain appreciable understanding of the local geographic and temporal variability in dengue incidence ) , a sample size of 270 x 3 in each arm of the curtailed trial would have given 80% power to detect a significant difference of 7% versus 2% incidence between the two groups ( alpha = 0 . 05 ) . The total documented enrolment of 1 , 655 approximated to this sample size of 270 x 3 x 2 = 1 , 620 . The 7% versus 2% difference corresponds to our original design assumption that a difference of at least 5% would be necessary in order to have policy implications . Statistical analyses used STATA 12 . The effectiveness of the intervention was determined by comparing proportions of students in intervention and control schools who had confirmed incident dengue infections during the trial , with adjustment for clustering by school as the unit of randomisation , using the clchi2 command . Differences in mean monthly numbers of Aedes mosquitoes trapped at intervention and control schools during the month in which the intervention was efficacious were assessed using the ttest command on ln ( n+1 ) transformed values .
The flow chart ( Fig 1 ) summarizes the numbers of participants in both intervention and control groups at all stages of the trial . The ten participating schools had 2 , 314 students at the start of the trial; consent was given by 1 , 811 students’ parents . Out of the 1 , 811 enrolled students ( mean age 10 . 1 years; range 6–17; 908 males , 903 females ) , 11 were withdrawn because of skin irritation , which were all mild and transient ( 7 in intervention; 4 in control schools ) . Of the 1 , 800 , 1 , 655 provided complete sets of paired blood samples ( Table 1 ) . The IgG baseline dengue seropositivity rate was 53 . 0% ( 878/1 , 655 ) . Of the 1 , 655 students with paired samples , 57 had evidence for a dengue infection during the study period , and 16 had equivocal results . Between schools , the number of dengue infections varied considerably from 0 to 9 . 9% ( Table 1 ) . In the control schools , 24/641 ( 3 . 7% ) and in the intervention schools 33/1 , 014 ( 3 . 3% ) students had evidence of new dengue infection . For our primary outcome , there was no significant difference in proportions of students having incident dengue infections between the intervention and control schools , with adjustment for clustering by school ( χ2 = 0 . 02 , p = 0 . 89 ) . The proportion of absenteeism ( for any reason ) was relatively high in all schools and ranged from an average of 23 . 0% ( SD = 15 . 9% ) over the school term , with a range of 9 . 6% to 49 . 9% in the treatment schools and 0 . 9% to 32 . 0% in the control schools . The proportion of pupils not going to school for at least 2 days due to a febrile illness ranged from an average of 1 . 4% ( SD = 1 . 17% ) over the school term from 0 . 6 . % to 4 . 0% in the treatment schools and from 0 . 1% to 2 . 8% in the control schools . The overall proportion of absenteeism due to fever of at least 2 days was relatively stable over the school term . There were no statistical differences between the treatment and control groups for any of the school months . Fig 2 shows both 1 h knock-down and 24 h mortality of Aedes mosquitoes exposed to the impregnated school uniforms . This started close to 100% and remained at high levels for up to 4 washes . After 4 washes , both knock-down and mortality declined rapidly . After 20 washes , the efficacy was below 20% . Knock-down decreased at an approximately linear rate of 4 . 7% per week , mortality at 4 . 4% per week . Due to this unexpectedly rapid waning of intervention efficacy , although the study was originally planned as a cross-over trial covering two dengue transmission seasons , we decided to abandon the second phase of the cross-over trial design . Results of the entomological assessments showed that the mean number of Aedes aegypti mosquitoes caught inside the classrooms of the intervention schools was significantly lower in the month following the introduction of the impregnated uniforms , when compared to those collected in classrooms of the control schools ( back-transformed mean of ln ( n+1 ) transform in control schools 4 . 9 , in intervention schools 1 . 4; ratio = 3 . 5 , 95% CI 1 . 1 to 5 . 5 ) . There was no significant difference in the mean numbers of Aedes aegypti mosquitoes collected at other times ( control 4 . 0 , intervention 3 . 8; ratio = 1 . 1 , 95% CI 0 . 7 to 1 . 7 ) .
Given the day-biting behaviour of Aedes mosquitoes , impregnated school uniforms could potentially be a simple novel tool to reduce mosquito-borne diseases and local vector populations . Our results from the WHOPES cone tests at the start of the trial underpin the potential for insecticide-treated uniforms to protect against dengue by reducing the populations of Aedes mosquitoes and hence mosquito-bites: knock-down effect and mortality immediately after impregnation of uniforms with permethrin by the InsectShield proprietary method were close to 100% , consistent with results obtained under laboratory conditions at the London School of Hygiene and Tropical Medicine . [20] Furthermore , we documented a significant reduction in Aedes mosquito numbers in the classrooms of the intervention schools in the first month after the start of the trial at the time when the insecticidal activity of impregnated uniforms was still 100% . However , our cluster- randomised controlled trial in ten Thai schools involving 1 , 811 children did not show serological evidence of a protective effect over the 5-month study period of one school term . Given the theoretical support for this strategy , we need to carefully examine plausible reasons for the apparent failure to protect in our trial , so that this intervention is not discarded as an ineffective strategy for control against Aedes-transmitted diseases such as dengue , Zika or chikungunya . The main reason for the negative result was the rapidly waning efficacy of InsectShield permethrin-impregnated clothes under field conditions . We chose InsectShield factory-impregnation over hand-dipping with permethrin because InsectShield impregnation ( unlike hand-dipping with permethrin ) results in odourless , well tolerated apparel—a fact that is important for a double-blind randomised trial where odour could otherwise had given away the allocation group . We had not anticipated rapid washing-out of permethrin prior to the study as InsectShield have consistently claimed that the insecticidal efficacy of their proprietary method of permethrin impregnation withstands up to 70 washes [13] . However , we documented rapid declines in insecticidal activity after the first 4 washes . After 20 washes , the knock-down and mortality effects on mosquitoes were well below 20% . What might be the reasons for such rapid waning of insecticidal activity ? Maybe the quality of already used cotton Thai school uniforms was inferior to clothing materials used by InsectShield for commercial purposes . With suboptimal cloth quality , the coating technique might have been less durable ? Or maybe the washing conditions of the tropics , drying in the open air and ironing decreased the insecticidal effect more rapidly ? However , we believe it was not just the potentially suboptimal cloth material of local Thai school uniforms , as a very recent study on laundering resistance of five commercially available , factory-treated permethrin-impregnated fabrics also found that permethrin content fell by 58 . 1 to 98 . 5% after 100 defined machine launderings , with InsectShield showing the fastest loss [21] . There is an urgent need for a standardised testing and licensing procedure for insecticide-impregnated commercial clothing to avoid misleading information . We also documented a high heterogeneity in incident infections between the schools and differences in baseline seroprevalence , which may have masked the extent of the efficacy in our trial . Large differences in dengue infection rates between schools within the same year have also been noted by other groups [22 , 23] , and underpin the difficulty in sample size calculations and subject selection of a disease that appears to be not only highly focal but also often exhibits a cyclical pattern with high and low epidemic years . We need to consider other potential causes for the lack of efficacy in our trial . Although acceptability and compliance with the trial uniforms was high , [24] school uniforms are not worn after school and over weekends . We did some simulation modelling and estimated a reduction of dengue infections by 47% if 60% of all mosquito bites occurred during school hours and 70% of the children wore treated uniforms , assuming that mosquito knock-down and mortality levels remained at baseline ( without washing-out effect ) [25] . A reduction of dengue infections by 47% would indeed be a major public health victory . Because we used paired blood samples at baseline and at the end of the study period ( 5 months ) , we are unable to tease out the impact of impregnated clothing on dengue incidence in the first month of wearing the intervention uniforms , at a time when the efficacy in terms of knock-down and mortality effect on mosquitoes was still close to 100% . As a proxy marker for symptomatic dengue disease , we recorded absenteeism of 2 days due to a febrile illness , but found no differences between the treatment and control groups , nor did we find statistically significant differences from month to month . Nevertheless , the results of entomological assessments showed that the intervention had an impact on the number of Aedes mosquitoes inside intervention schools in the first month of the intervention before insecticidal activity declined . This is an important finding that encourages continued research on the use of insecticide-treated clothing as a potential strategy for dengue prevention in school children . Permethrin-treated clothes were also shown to be effective in reducing tick bites and mosquito bites in general in other studies , however , similarly as in our study , this effect waned off as time passed on , possibly also due to laundering effect [14 , 15] . Long-lasting insecticide-treated bednets were a major breakthrough in the control of malaria . However , bednets do not get washed so frequently . For insecticide-treated clothing to be a viable public health intervention it should withstand regular washing . If the rapid washing-out of permethrin can be overcome by novel technological approaches , insecticide-treated clothes would deserve to be re-evaluated as a potentially cost-effective and scalable intervention . Despite the urgency of the current Zika outbreak associated with serious pregnancy outcomes , we should not rush into recommending factory-impregnated clothing to pregnant women in Zika affected areas , until standardised testing and licensing procedures for insecticide-treated materials are implemented , with defined cut-off values for initial maximum and post-laundering minimum concentrations of permethrin as well as data on toxicity , homogeneity on fabrics , residual activity , and laundering resistance [21] . Failing to do this could generate a dangerous sense of security among Zika-exposed pregnant women using impregnated clothing , since the wearer has no means of judging insecticidal efficacy . Given the increasingly epidemic proportions of Aedes-transmitted viral infections , we hope that the findings from this trial will provide strong impetus to fund research to develop appropriate and safe technologies for long-lasting insecticide-treated clothing materials that can be used for school uniforms , work place uniforms and maternity clothing alike . | Viral diseases transmitted via Aedes mosquitoes are on the rise , such as Zika , dengue , and chikungunya . Novel tools to mitigate Aedes mosquitoes-transmitted diseases are urgently needed . We tested whether commercially available insecticide-impregnated school uniforms can reduce dengue incidence in school children . To test this hypothesis we designed a school based randomized controlled trial where we enrolled 1 , 811 school children aged 6–17 . For study monitoring , we also measured the effect of the impregnated uniforms on the survival of Aedes mosquitoes based on a standard bioassay test called WHOPES cone test . Furthermore , we counted the number of Aedes mosquitoes in classrooms and outside areas of classrooms . In the control schools , 3 . 7% and in the intervention schools 3 . 3% of the students had evidence of new dengue infections during the 5 month long school term , which indicates that there was no protection against dengue infections despite the fact that the knockdown effect of the impregnated uniforms was very high in the laboratory . We also showed a significant reduction of Aedes mosquitoes in the classrooms of the intervention schools . So why did this not translate into clinical protection against dengue ? We assume the reason was the rapid wash-out effect of permethrin . Despite the company’s claim that impregnated clothing would withstand up to 70 launderings , we found a rapid decline in permethrin efficacy already after 4 washes , with the efficacy to below 20% after 20 washes . If rapid washing-out of permethrin could be overcome by novel technological approaches , insecticide-treated clothes might become a potentially cost-effective and scalable intervention to protect against diseases transmitted by Aedes mosquitoes such as dengue , Zika , and chikungunya . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"education",
"chikungunya",
"infection",
"pathogens",
"sociology",
"tropical",
"diseases",
"microbiology",
"social",
"sciences",
"animals",
"viruses",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"insect",
"vectors",
"zoology",
"infectious",
"diseases",
"agrochemicals",
"aedes",
"aegypti",
"medical",
"microbiology",
"epidemiology",
"schools",
"microbial",
"pathogens",
"disease",
"vectors",
"insects",
"agriculture",
"arthropoda",
"insecticides",
"mosquitoes",
"entomology",
"flaviviruses",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"organisms",
"zika",
"virus"
] | 2017 | Mitigating Diseases Transmitted by Aedes Mosquitoes: A Cluster-Randomised Trial of Permethrin-Impregnated School Uniforms |
Mammalian X chromosomes evolved under various mechanisms including sexual antagonism , the faster-X process , and meiotic sex chromosome inactivation ( MSCI ) . These forces may contribute to nonrandom chromosomal distribution of sex-biased genes . In order to understand the evolution of gene content on the X chromosome and autosome under these forces , we dated human and mouse protein-coding genes and miRNA genes on the vertebrate phylogenetic tree . We found that the X chromosome recently acquired a burst of young male-biased genes , which is consistent with fixation of recessive male-beneficial alleles by sexual antagonism . For genes originating earlier , however , this pattern diminishes and finally reverses with an overrepresentation of the oldest male-biased genes on autosomes . MSCI contributes to this dynamic since it silences X-linked old genes but not X-linked young genes . This demasculinization process seems to be associated with feminization of the X chromosome with more X-linked old genes expressed in ovaries . Moreover , we detected another burst of gene originations after the split of eutherian mammals and opossum , and these genes were quickly incorporated into transcriptional networks of multiple tissues . Preexisting X-linked genes also show significantly higher protein-level evolution during this period compared to autosomal genes , suggesting positive selection accompanied the early evolution of mammalian X chromosomes . These two findings cast new light on the evolutionary history of the mammalian X chromosome in terms of gene gain , sequence , and expressional evolution .
In mammals and Drosophila , the X chromosome usually differs dramatically from autosomes since it is hemizygous in males [1] . Sexual antagonism ( beneficial for one sex , but deleterious for the other ) enriches male-biased genes on the X chromosome , if alleles are generally recessive , and on the autosome if they are generally dominant [2]–[3] . On the other hand , inactivation of the X chromosome during spermatogenesis [4]–[5] drives the accumulation of male-biased genes on the autosomes where they can be expressed in the meiotic or post-meiotic phase [6]–[7] . These two processes can explain the gene traffic between the X and autosomes in Drosophila [8] and mammals [9]–[10] as well as the excess of male-biased genes on the autosomes [11]–[12] . However , recent analyses of male-biased genes identified several X-linked genes that originated in the last 1–3 million years ( myr ) in Drosophila [13]–[15] . Whether or not these data implicate an effect of evolutionary time on the chromosomal location of male-biased genes remains unknown . In our investigation of how the various evolutionary forces impact the chromosomal distribution of sex-biased genes , we focused particularly on how the age of genes affects their chromosomal locations . By dating when genes arose in humans and mouse , we found male-biased genes were distributed at different locations in different phases of mammalian evolution: young male-biased genes are enriched in the X chromosome , but older male-biased genes favor autosomal locations . Interestingly , this redistribution seems to be associated with feminization of the X chromosome with more X-linked old genes expressed in ovaries . Besides the recent gene gain contributed by emergence of male-biased genes on the X chromosome , we found another burst of gene gain on X chromosome immediately after the divergence of opossum and eutherian mammals . Accelerated protein evolution and transcriptional evolution of X-linked genes reveal positive selection occurring in this period . These data support the recent notion [10] , [16] that our X chromosome originated in the therian ancestor instead of the common ancestor of all mammals . These two lines of findings significantly extend our knowledge of the origination and evolution of X chromosomes in mammals .
We tracked the relative gene abundance of individual chromosomes across 450 myr and identified two bursts of genes occurring on the X chromosome ( Figure 2 ) . One burst ( branches 5–7 ) postdated the divergence of eutherian mammals ( human or mouse ) and marsupials ( opossum ) and the other occurred recently after the split of human and chimp and after the split of mouse and rat , respectively . For both peaks , the X chromosome contributes to 8%∼14% of genes , while it only accounts for 3% of genes in the first 300 myr of vertebrate evolution . In contrast , autosomes tend to vary less in their relative contribution to the whole genome ( Figure S1 ) . As the major contributor generating new genes , DNA-level duplication accounts for 73∼95% of genes of these two peaks . If we only use DNA-level duplicates , the pattern remains the same . Considering that many more genes arose in branch 5 compared to branch 6 or 7 ( 1 , 200∼1 , 400 versus 400∼500 , Figure 1 ) , the old peak seems to be best explained by the hypothesis that the X chromosome emerged in the therian ancestor and subsequently recruited many genes in an accelerated evolution of sex-related functions , as found with retrogene-based chromosomal movement studies [26] . In contrast , the recent burst reveals a rapid addition of new genes into the mammalian X chromosome , which may be independent of major chromosomal changes . Based on human body index data ( GSE7307 , Materials and Methods ) and mouse tissue profiling data [27] at the NCBI GEO database [28] , we identified genes with sex-biased expression ( Materials and Methods ) . As shown in Figure 3 , both human and mouse demonstrate a similar pattern regarding the proportion of male-biased genes and the age of the branch in which they arose . For younger branches ( less than 50 myr ) , male-biased genes are enriched in the X chromosome compared to autosomes ( ∼50% versus ∼30% , Chi-square test p<0 . 05 ) , which might be driven by fixation of recessive male-beneficial alleles under sexual antagonism . This pattern decreases for genes originating in earlier branches . Male-biased genes older than 300 myr are overrepresented on the autosomes ( ∼30% versus ∼15% , p = 1×10−9 ) . This pattern was independently supported by an Affymetrix exon array panel with larger coverage of new genes ( Figure S2 ) . Thus , the recent peak observed in Figure 2 could be attributed to a burst of male-biased genes on X chromosome younger than 50 myr . Figure 3 also demonstrates that the X chromosome consists of a similar or even higher proportion of male-biased genes compared to autosomes from 90 myr ago ( branch 7 ) to 130 myr ago ( branch 5 ) . Thus , many of the genes gained in the first , older peak may also have male-biased expression . Notably , the proportion of female-biased genes on branch 5 was greater on the X chromosome compared to autosome ( 39% versus 20% in Table 1 ) . In contrast , for branches 6 and 7 , the proportion of female-biased genes is around 15% for both the X chromosome and autosomes ( Table S4 ) . Again , this suggests that the newly originated X chromosome was subjected to enhanced positive selection and recruited an excess of both male- and female-biased genes . The earlier peak in Figure 2 indicates the mammalian X chromosome emerged before the divergence of eutherian and marsupial [10] . Thus , the nascent X chromosome changed remarkably , gaining an excessive number of genes . If this scenario is true , those preexisting genes on the ancestral X chromosome might have accumulated many evolutionary changes during this period ( branch 5 ) , as did genes linked to the neo-X chromosome in Drosophila [29] . That means we would expect these ancient genes on the X chromosome to show signatures of positive selection . To test this scenario , we investigated the evolutionary path of ancient genes shared by vertebrates by comparing the ratio between non-synonymous substitution rate and synonymous substitution rate ( Ka/Ks ) ( Materials and Methods ) . In other words , we compared the Ka/Ks of X- and autosomal-linked old genes in separate evolutionary periods . Across evolution of 450 myr , the X chromosome did not show significantly higher Ka/Ks except in branch 5 ( Table 2 ) , which strongly corroborates the hypothesis that the X chromosome did not acquire sex-chromosome status until this period . We extended this analysis to genes gained since branch 5 . We directly estimated the proportion of replacement substitutions ( α ) based on polymorphism and divergence data in [30] and a maximum-likelihood method implemented in the DoEF package [31] . As shown in Table S5 , young genes generally show higher α compared to old genes , and X-linked male-biased genes show the highest α , 0 . 501 . This pattern shows that positive selection instead of neutrality drives the evolution of X-linked genes arising since branch 5 , especially those with male-biased expression . However , positive selection of nucleotide substitutions can only suggest that initial fixation may also be driven by positive selection . More direct evidence comes from copy number polymorphism ( CNP ) data in Drosophila , which showed that the X chromosome is subject to stronger purifying selection than autosomes [32] . In human , it was also noted that the X chromosome shows a paucity of CNPs [33] . Together with bursts of adaptive fixations occurred on the neo-X of Drosophila [29] , it is likely that positive selection instead of drift accounts for two bursts of genes on the X chromosome . As we noted before , enrichment of young male-biased genes on the X declines for those originating in earlier evolutionary branches . Using expression data from mouse spermatogenesis , we compared different age groups to investigate which force underlies such a demasculinization process ( Table 3 ) . As previous studies such as [7] found , old genes are expressed more in the pre-meiosis stage ( spermatogonia ) but are silent from meiosis ( pachytene spermatocyte ) to post-meiosis ( round spermatid ) . In terms of whole testes , however , old X-linked genes are underrepresented ( Table 3 ) . New genes show a distinct pattern: while often expressed in spermatogonia , they are not silent in meiosis . Moreover , a much greater proportion of new genes on the X are expressed in the post-meiosis stage compared to genes on the autosome ( 70% versus 27% , Chi-square test p = 5×10−10 ) . This is consistent with a previous observation of X-linked postmeiotic multicopy genes [34] , the vast majority of which we found were very young ( Materials and Methods ) . Such a pattern suggests that the young X-linked genes are not affected by MSCI . An independent microarray dataset of mouse spermatogenesis [35] confirms high expression of X-linked young genes in spermatid ( Figure S3 ) . In addition , we note that the customized array by Khil et al . was comprised mainly of old and conserved genes , with only 1 . 7% of the set being young genes . In contrast , the Affymetrix array data [36] we used covered 14 , 923 Ensembl genes , 3 . 9% of which are young genes . This striking contrast between young and old genes suggests that MSCI plays an important role in determining the age-dependent chromosomal distribution of male-biased genes . In order to investigate how this contrast occurred in such a short time , we analyzed four major cell types including sertoli cells , spermatogonium , spermatocyte , and spermatid between mouse [35] and rat [37] . We used the Euclidean distance of relative abundance ( RA ) to measure how orthologous genes have diverged in their expression ( Materials and Methods ) . Consistent with a previous comparison of human and chimpanzee [38] , the testis expression of genes on the X chromosome diverge more between rat and mouse than genes on autosomes ( Wilcoxon rank sum test p = 4×10−6 , Figure 4 ) . Furthermore , X-linked young genes show significantly higher divergences , compared to all other three groups ( p<0 . 05 ) . While we found that expression in various spermatogenesis stages is generally conserved [35] with only about 3% divergence ( Figure S4 ) , X-linked young genes show the largest expression divergence in spermatid . Specifically , after the split of mouse and rat 37 myr ago [39] , young X-linked genes show 6 . 9% divergence in spermatid , which is much higher than the genomic average for spermatid , 3 . 3% ( Wilcoxon rank sum test p = 0 . 002 ) . This increased divergence suggests that , although these genes seem to escape MSCI and preferentially transcribe in post-meiosis , the expression profile is not conserved . It remains unknown whether these genes get up-regulated or down-regulated in one species . But if the latter case were true , it indicates that the high post-meiotic expression would be silenced by MSCI in later evolution . This could also explain how the different pattern between young and old genes in Table 3 is achieved . We investigated the distribution of female-biased genes on chromosomes and its correlation with gene ages . Interestingly , female-biased genes are distributed in a pattern symmetrical to male-biased genes ( Figure S5 versus Figure 3 ) : the old X-linked genes are more often female-biased , while young genes are not . We characterized ovary expression of genes using the Affymetrix mouse exon array panel data . Consistent with Figure S5 , ovary expression also depends on the age of the gene's origination . Specifically , young autosomal genes show significantly higher expression in ovaries than young X-linked genes ( Wilcoxon rank sum test p = 5×10−12 , Figure 5 ) . However , old X-linked genes generally show higher expression in ovaries ( p = 5×10−7 ) . Thus , as gene age increases , this expressional excess of autosomal genes reverses and older X-linked genes show significantly higher expression in ovaries . It can be argued that such an age-dependent pattern of expression is not a specific property of ovary evolution and other organs might also show a similar pattern . To test this possibility , we investigated gene expression in the major organs: brain , heart , kidney , liver , lung , muscle , spleen , and thymus . All these tissues , except for brain , showed a significant excess of expression for new genes ( branch≥5 ) on autosomes compared to that of X-linked genes ( Wilcoxon rank sum test p<0 . 01 , Figure S5 ) . However , for old genes ( branch≤4 ) , they are evenly distributed ( p>0 . 05 ) . The brain shows a unique pattern . Young genes ( branch>7 ) are relatively abundant on autosomes ( p = 0 . 001 , Figure S5 ) , but old genes ( branch≤7 ) are overrepresented on the X chromosome ( p≤0 . 01 ) . This is consistent with previous findings that X chromosome is enriched with genes expressed in brain [1] , [40] . Notably , different from ovaries , enrichment in the brain did not show clear age dependence , since genes originating from branches 5 to 7 presented the most significant excess ( Figure S6 ) . The coincidence that the X chromosome is enriched with both ovary-expressed and brain-expressed genes occurring in branch 5 ( Table 1; Figure S5 ) motivated us to perform more thorough transcriptional profiling to get a more complete picture of how genes from this evolutionary period are transcribed . We investigated mouse exon atlas data ( GSE15998 ) to ask whether X-linked genes are more frequently expressed in the tissue of interest across different age groups . We clustered tissues by the proportion of X-linked genes expressed versus the proportion of autosomal genes expressed and identified three major groups: nervous system , testes , and all other tissues ( Figure 6 ) . Remarkably , the X-linked genes originating in branch 5 are transcriptionally permissive with a larger proportion of them expressed in many tissues compared to autosomal genes . This excess is most pronounced for brain samples . Consistently , human data revealed that a greater proportion of X-linked genes emerging on branch 5 are expressed more widely than autosomal genes originating in this period , which is strongest for the brain ( Figure S7 ) . Since human and mouse share a similar pattern , parsimony suggests this striking transcriptional pattern of branch 5 derived genes is ancestral . Notably , none of these genes show sex bias in human brain profiling data [41] , which suggests they might be important for both sexes . We have described evolutionary patterns of protein-coding genes , which could be driven by natural selection in various forms like sexual antagonism or MSCI . If , however , such a pattern is a product of some mutational bias of gene origination , we would not detect similar evolutionary patterns in non-coding RNA genes , such as X-linked miRNAs . Therefore , we investigated the chromosomal distribution of miRNA genes annotated in miRBase [42] and found that miRNA duplicates are distributed in a pattern similar to that observed for protein-coding genes ( Table S6 ) . Specifically , both human and mouse show significant miRNA gene gain in branches 5 to 7 compared to the proportion of all miRNA genes ( 18∼22% versus 10∼13% , Fisher's Exact Test p<0 . 05 ) . Moreover , they also show an excess for the youngest branch . Although it is not significant for the human data due to small sample size , it is for mouse ( p = 0 . 02 ) . Like protein-coding genes , a larger proportion of X-linked miRNAs originating in branch 5 are transcribed in nine tissues ( statistically significant for six of them ) surveyed on Agilent chip [43] compared to autosomal genes ( Table S7; Materials and Methods ) . Moreover , semi-quantitative PCR data of X-linked miRNAs in 12 tissues [44] show 9 out of 13 ( 69% ) young genes are expressed higher in testes than at least six non-testis tissues . However , this percentage drops to 23% for old X-linked genes ( 9 out of 39 , Fisher's Exact Test p = 0 . 005 ) . Consistent with protein-coding genes , these data also show that old genes have moderate or high expression in ovaries and the young genes show only trace levels of expression ( Wilcoxon rank sum test p = 0 . 01 ) . The age-dependent locations and expression profiles of miRNAs support that it is evolutionary forces , rather than some mutation bias intrinsic to a certain type of gene , which account for the dynamics of X-linked gene evolution . It is known that the X chromosome can be divided into five evolutionary strata because of step-wise repression of recombination [45]–[47] . The X-conserved region ( XCR ) consists of the oldest strata 1 and 2 , while the X-added region ( XAR ) includes younger strata 3 that is shared by primates and rodents , and much younger 4 and 5 that were derived within primates [46] , [48] . Since sexual antagonism or other sex related forces like the faster-X process ( see Discussion ) depends on hemizygosity of the X chromosome in male , we expect the accordance between bursts of gene gain with the formation of corresponding strata . If these forces shape the evolution of gene content on the X chromosome , we should find that X-linked genes originating at a given time period should accumulate only in the strata already formed at that time . In other words , we should find a correlation between the ages of genes and the strata in which they are located . Consistent with these predictions , Figure 7 shows that the older strata 1 to 3 are associated with relatively older genes , while strata 4 or 5 are enriched with younger genes ( one sided Fisher's Exact Test p = 0 . 03 ) . This finding parallels the temporal correspondence between the occurrence of strata and the out-of-X retrogene traffic [49] .
Our analyses demonstrated that the X chromosome evolved dramatically on both the sequence and expression levels after the split of eutherian mammal and marsupials . Specifically , the X chromosome showed a burst of gene gain during this time , and many of these genes quickly invaded the transcriptional network of various tissues , especially the brain . Furthermore , genes predating the birth of the X showed rapid protein-level evolution . A straightforward interpretation is that the newborn mammalian X was subjected to strong positive selection similar to the neo-X chromosome in Drosophila [29] . Moreover , the X-linked genes arising in branch 5 seem to have played important roles , as shown by their broad expression . Their transcription pattern suggests that the early evolution of placental mammals was associated with rapid changes in the brain . Furthermore , analysis of gene ontology showed that many of these genes mainly played regulatory roles in transcription and metabolism ( Table S8 ) . Thus , regulatory change contributed by gene gain on the X chromosome was extensively involved in the initial evolution of eutherian mammals . The fact that this peak ranges between branches 5 and 7 suggests remodeling of incipient X chromosome might take about 90 myr ( −160∼−70 myr , Figure 2 ) , which is consistent with one report based on retrogene movement [26] . However , the selective pressures driving this dramatic change in branch 5 appear to be smaller in subsequent branches ( Table 2 ) . Our analyses reveal chromosomal redistribution of X-linked male-biased genes . Sexual antagonism may contribute to the initial fixation of X-linked recessive alleles as described previously [2] , [7] . The faster-X hypothesis was initially proposed to fix more mutations on the X chromosome only if they are recessive and beneficial [1] . Recently , it was observed that this force was most pronounced for male-biased genes [50] . This suggests that the faster-X process could also be involved in the emergence of young X-linked male-biased genes , as the hypothesized sexual antagonism might . These young X-linked male-biased genes could be later silenced by MSCI as suggested by Table 3 , Figure 4 , and Figure S4 . At least two processes could be involved in this switch . First , we found a statistically significant excess of male-biased retrogenes generated in the X→A movement process and X-enrichment of the female-biased parental genes for both human and mouse ( Table S9 ) . Thus , the demasculinization and feminization of the X chromosome could be coupled in retrogene traffic . Moreover , our RA analysis ( Figure S6 ) extends the out-of-the-testes hypothesis [51] to non-retroposed new genes . We found that new genes generally acquire transcription in more tissues during evolution although they are initially enriched in testes . With increasing MSCI and expanding expression breadth , X-linked male-biased genes might become unbiased or even female-biased as Figure S6 shows . If new strata on the X chromosome represent regions that did not develop recombination repression until recently , the genes encoded in these regions will often escape MSCI [45] . Thus , it is expected that the X-linked male-biased genes more likely escape MSCI when located on young strata or pseudoautosomal regions ( PARs ) . However , out of 13 young male-biased genes in humans , the relatively young strata 4 and 5 encode only one ( Table S10 ) , which does not significantly differ much from the expected number based on its genomic size . How then did the remaining 12 genes , those situated on older strata , escape from MSCI ? It was proposed that the excess of inverted repeats ( IRs ) encoded by human and mouse X chromosome could protect genes contained by these IRs from MSCI [52] . IRs suppress MSCI through formation of cruciforms or other unusual chromatin structures . Moreover , cancer/testis ( CT ) genes that are often expressed in normal testes and in cancerous tissues frequently overlap with IRs [52] . Given that X-linked CT genes underwent recent expansion [53] , it is not surprising that some of them could form highly homologous IRs . In fact , 8 out of 13 young X-linked male-biased genes are CTs ( Table S10 ) . Thus , the high IR abundance on the mammalian X chromosome might be one reason that these genes can be transcribed in meiosis or postmeiosis . Furthermore , out of 12 genes encoded by PARs and covered by unique probes ( Table S11 ) , there is only one ( 8% ) male-biased gene , PPP2R3B , which is shared by human and mouse . Thus , different from our intuition , PARs do not harbor an excess of male-biased genes compared to the remaining strata ( 18% ) and to autosomes ( 24% ) . Albeit of small sample size , this observation suggests that sex-related forces like sexual antagonism or faster-X process account for the observed excess of young X-linked male-biased genes . There are only limited number of genes with unique probes on strata 4 ( five ) and 5 ( eight ) . For the remaining strata , stratum 3 is enriched with male-biased genes , which is much higher than stratum 1 ( 27% versus 17% , one-sided Fisher's Exact Test p = 0 . 02 ) and stratum 2 ( 27% versus 15% , p = 0 . 03 ) . This pattern suggests that stratum 3 recruits more young male-biased genes and there was not enough evolutionary time to be feminized as occurred in the oldest strata 1 and 2 . As shown in Figure 2 , the emergence of young male-biased genes peaks in recent evolution of human and mouse . However , this peak started 30 myr ago ( before the divergence of mouse and rat ) in the rodent lineage , while the peak appeared in the last 5 myr in human lineage . This difference is consistent with the fact that the mouse X encodes more young male-biased genes than the human X . Specifically , male-biased genes account for 52% and 74% of X-linked young genes in human and mouse , respectively ( Figure 3; one sided Fisher's Exact Test , p = 0 . 07 ) . Exon array data are similar ( Figure S2; 45% versus 76% , one sided Fisher's Exact Test , p = 2×10−8 ) . Origination of significantly more male-biased young genes suggests that stronger positive selection acts on rodents and could explain why the recent peak of gene gain ( Figure 2 ) began earlier in the mouse lineage than in the human .
We developed a genome-alignment based pipeline to infer the origination time of a given genomic region by modifying a previous gene-alignment based method [58] . We analyzed UCSC [17] netted chained file for human ( hg18 ) and mouse ( mm9 ) to verify whether a given human/mouse locus has a reciprocal syntenic alignment in the outgroup genome such as chimpanzee , rat , chicken , and so on . In other words , we investigated whether a best-to-best match could be found between human/mouse loci and outgroup loci regardless of chromosomal linkage . In this way , we can identify orthologous genes; even those with different chromosomal location due to fusions or translocations such as those found in XAR region will be identified as well . Then , in order to handle occasional sequencing gaps , we scanned multiple outgroups and assigned this locus to a specific branch by following a parsimony rule . Compared to the previous method [58] , our strategy is independent of gene annotation of outgroups and robust with gene translocation . Thus , we generated a more stringent young gene dataset ( as described in the Result section ) . And , as Figure S8 shows , we have not assigned most genes encoded by XAR as young genes simply because this region changed the linkage by fusing to X chromosome . Conversely , several genes originated in branch 5 are located in strata 1 and 2 that are not XAR ( Figure S8 ) , also supporting that our pipeline is robust with gene translocations . Notably , for regions without reliable synteny , our method might not work . This situation would be most pronounced for telomeres , which tend to be repetitive and prone to recombine [59] and thus have very limited synteny . For example , we dated 17 genes situated on PARs of the X chromosome ( Table S11 ) . For three genes encoded by PAR2 , repeats contribute less than 16% of the gene loci based on UCSC annotation [17] . Accordingly , our age assignments for these three genes are always consistent with those inferred by tree reconstruction provided by Ensembl [54] . In contrast , for 14 genes linked with PAR1 , repeats are prevalent with a median contribution of 55% to the gene loci . In this case , our results are consistent for only three out of nine cases with Ensembl age information . We slightly modified the previous pipeline [58] , [60]–[61] and classified young genes as DNA-level duplicates , RNA-level duplicates ( retrogenes ) , and de novo genes . Briefly , we performed all-against-all BLASTP search for human and mouse proteins . It was reported previously that retrogenes can recruit other neighboring genome regions with introns after being retroposed [51] . Thus , in order to define a new gene as retrogene , we requested that in the aligned region between the most similar paralog ( candidate parental gene ) and child genes , the former contain at least one intron and the latter to be intronless . Otherwise , it will be classified as DNA-level duplicates . Notably , if there is no hit with BLAST evalue cutoff 10−6 found [58] and no annotated paralog by Ensembl [54] , the gene will be defined as de novo . In order to avoid non-specific probes and to cover more recently annotated genes , we used the customized array annotation files ( released on November , 2008 ) downloaded from University of Michigan [62] , HGU133Plus2_Hs_ENSG ( Affymetrix Human 133 plus 2 ) and Mouse4302_Mm_ENSG ( Affymetrix Mouse Genome 430 2 . 0 Array ) for human and mouse , respectively . For exon array analysis , we used HuEx-1_0-st-v2 , U-Ensembl49 , G-Affy . cdf and MoEx-1_0-st-v1 , U-Ensembl50 , G-Affy , EP . cdf generated by Aroma . affymetrix team [63] . Thus , we excluded some candidate young genes that were too similar to their paralogs and did not have specific probes . Based on R [57] and Bioconductor platform [64] , we used RMA [65] to normalize and generate gene-level intensity for 3′ gene array and Aroma . affymetrix to normalize and summarize gene-level signal for exon arrays . We used MAS5 to call expressional presence and absence for 3′ gene array . In case of exon array , we used Affymetrix dabg ( detection above background ) algorithm to generate chip specific background signal and then compared gene-level signal to this background with Wilcoxon rank sum one-tail test . Considering multiple-testing issues , we converted all p values to q values using the qvalue package [66] . The q value of 0 . 01 was used as the cutoff . For Agilent miRNA array , we used “gIsGeneDetected” column generated by Agilent Feature Extraction software to define presence or absence calls [67] . We required a gene to be present in all replicates to be considered a presence and a gene to be absent in all replicates to be considered an absence . We removed all ambiguous cases from the final statistics . We used the LIMMA package [68] to call expressional difference , with a false discovery rate corrected p of 0 . 05 used as the cutoff . Although we compared testis and ovary , we used the term “male-bias” or “female-bias” rather than “testis-bias” or “ovary-bias . ” The reason is that these two datasets are nearly equivalent . A previous study showed that the proportion of germline male-biased genes is much higher than that of somatic male-biased genes ( 20% versus 2% ) [12] . For meta-analyses of mouse and rat spermatogenic data , we followed the concept of RA and euclidean distance ( d ) to measure the between-species expression divergence [69] . Specifically , we defined RA as the proportion of expression intensity of one tissue out of all tissues and d as the sum of the square of RA difference for all tissues between mouse and rat , i . e . , . We mapped 20 out of 33 representative genes in [34] to our gene age data using unique NCBI gene names . Remarkably , 16 ( 80% ) are rodent-specific , with 11 of them originating after the mouse and rat split . We note here that this dataset does not overlap with what we described in Table 3 , since Table 3 only presents genes with unique probes , which 19 of these 20 genes do not have . We downloaded the vertebrate-wide 44-way coding sequence alignment from UCSC . UCSC known genes mapping to multiple Ensembl genes were discarded . For Ensembl genes mapping to multiple UCSC known genes , we retained only one UCSC gene with the longest coding region . Then , considering that low quality assembly often causes unreliable estimation of Ka/Ks [70] , we extracted 17 species with relatively better quality ( Figure 1 ) and then removed all in-frame stop codons or gaps in the alignment . According to our age dating information , taxa conflicting with the age were removed . Based on the species tree ( Figure 1 ) , we estimated Ka/Ks for each branch using free ratio model in PAML [71] . We downloaded Gene Ontology ( GO ) annotations for Ensembl V51 . We used the program analyze . pl V1 . 9 of TermFinder package [72] to identify those significant terms for new genes , with multiple test corrected p of 0 . 05 as the cutoff and the whole genome as the background . Herein , TermFinder was updated to V0 . 83 , which corrected a mistake in calculating false discovery rate [73] . | Some evolutionary theories predict that the X chromosome will be enriched for genes with male functions . However , recent studies showed there had been gene traffic in which autosomal male-biased genes were retroposed from X-linked parental genes . A question remains about whether this pattern also holds for all types of new genes . Herein , using comparative genomic analysis , we dated all human and mouse genes to the vertebrate phylogenetic tree . We found that the X chromosome evolved with two bursts of gene origination events . The recent burst includes mainly male-biased genes in contrast to older X-linked genes that are often female-biased in expression . Meiotic sex chromosome inactivation contributes to this dynamic since it silences the older but not the younger X-linked genes . The older burst was after the split of eutherian mammals and the marsupial opossum , and the genes from this burst were quickly incorporated into transcriptional networks of multiple tissues , especially in the brain . The transcriptional expansion , together with the rapid protein evolution of the preexisting old X-linked genes , suggests that positive selection was acting in the early evolution of the mammalian X chromosome . These two lines of findings revealed extensive gene evolution in the mammalian X chromosome . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology/human",
"evolution",
"evolutionary",
"biology/bioinformatics",
"evolutionary",
"biology/genomics"
] | 2010 | Chromosomal Redistribution of Male-Biased Genes in Mammalian Evolution with Two Bursts of Gene Gain on the X Chromosome |
Genetic reassortment of H5N1 highly pathogenic avian influenza viruses ( HPAI ) with currently circulating human influenza A strains is one possibility that could lead to efficient human-to-human transmissibility . Domestic pigs which are susceptible to infection with both human and avian influenza A viruses are one of the natural hosts where such reassortment events could occur . Virological , histological and serological features of H5N1 virus infection in pigs were characterized in this study . Two- to three-week-old domestic piglets were intranasally inoculated with 106 EID50 of A/Vietnam/1203/04 ( VN/04 ) , A/chicken/Indonesia/7/03 ( Ck/Indo/03 ) , A/Whooper swan/Mongolia/244/05 ( WS/Mong/05 ) , and A/Muscovy duck/Vietnam/ 209/05 ( MDk/VN/05 ) viruses . Swine H3N2 and H1N1 viruses were studied as a positive control for swine influenza virus infection . The pathogenicity of the H5N1 HPAI viruses was also characterized in mouse and ferret animal models . Intranasal inoculation of pigs with H5N1 viruses or consumption of infected chicken meat did not result in severe disease . Mild weight loss was seen in pigs inoculated with WS/Mong/05 , Ck/Indo/03 H5N1 and H1N1 swine influenza viruses . WS/Mong/05 , Ck/Indo/03 and VN/04 viruses were detected in nasal swabs of inoculated pigs mainly on days 1 and 3 . Titers of H5N1 viruses in nasal swabs were remarkably lower compared with those of swine influenza viruses . Replication of all four H5N1 viruses in pigs was restricted to the respiratory tract , mainly to the lungs . Titers of H5N1 viruses in the lungs were lower than those of swine viruses . WS/Mong/05 virus was isolated from trachea and tonsils , and MDk/VN/05 virus was isolated from nasal turbinate of infected pigs . Histological examination revealed mild to moderate bronchiolitis and multifocal alveolitis in the lungs of pigs infected with H5N1 viruses , while infection with swine influenza viruses resulted in severe tracheobronchitis and bronchointerstitial pneumonia . Pigs had low susceptibility to infection with H5N1 HPAI viruses . Inoculation of pigs with H5N1 viruses resulted in asymptomatic to mild symptomatic infection restricted to the respiratory tract and tonsils in contrast to mouse and ferrets animal models , where some of the viruses studied were highly pathogenic and replicated systemically .
The genus Influenzavirus A ( i . e . influenza A virus ) contains individual virus strains which have infected a broad spectrum of avian and mammalian species . While wild aquatic birds are the primordial reservoirs for all influenza A virus genes and subtypes , distinct genetic lineages have become established in humans , horses , and pigs [1] , [2] . Viruses of 3 different subtypes , H1N1 , H3N2 , and H1N2 , are circulating in swine worldwide ( reviewed in [3] , [4] ) . The origin and nature of swine influenza viruses vary on different continents . Most swine influenza A viruses are reassortants containing various combinations of genes originating from human , avian and swine influenza A viruses [3] , [4] . This emphasizes that pigs are susceptible to both human and avian influenza viruses . Such susceptibility could possibly be explained by the presence of cell surface receptors for both human and avian influenza viruses on the epithelium of pig upper respiratory tract [5] . These features enable pigs to be a possible intermediate host or “mixing vessel” , for the generation of pandemic influenza viruses through reassortment [6] , [7] . The 1957 and 1968 pandemic influenza viruses were reassortants which contained human and avian influenza virus genes [8] , [9] . However , there is no proof for a role of pigs in the generation of these pandemic viruses . The 1918 H1N1 “Spanish” pandemic influenza virus appears to have entered both human and pig populations , although the epidemiological evidence favors humans as the initial host [10] . There are a number of reports of human infection with influenza viruses of swine origin ( reviewed in [4] ) . Thus , it is obvious that pigs are an important link in the ecology of influenza A viruses and could be a possible source of origin for human pandemic influenza . Highly pathogenic avian influenza ( HPAI ) viruses of the H5N1 subtype are zoonotic agents that present a continuing threat to both veterinary and public health ( reviewed in [11] ) . Between 1996 and 2003 , H5N1 HPAI viruses were isolated from poultry in Southern China [12] , [13] and Vietnam [14] , and occasionally caused severe disease in humans [13] , [15] , [16] . The situation changed in late 2003–2004 , when the H5N1 viruses expanded their geographic range , resulting in unprecedented epizootics in poultry and new human cases in eastern and southeastern Asia [17] , [18] . In May 2005 , an H5N1 disease outbreak in migratory waterfowl occurred at Qinghai Lake in Western China , and signaled a possible wild bird component to the spread of H5N1 in the region [19] , [20] . During 2005–2007 , H5N1 viruses spread throughout Asia , Europe , Middle East , North and West Africa [21] . Outbreaks in poultry and cases of human H5N1 disease with a high case fatality rate have continued through 2007 and into the beginning of 2008 [21] , [22] . The endemicity of H5N1 HPAI virus in village poultry of Eurasia and Africa [23] , and the continuing appearance of individual human cases have created a situation that may facilitate pandemic emergence . However , to date , most cases of human infection with H5N1 HPAI viruses have occurred through close contacts with infected village poultry [24] . Human-to-human transmission of H5N1 viruses has been inefficient and limited [11] , [24] , [25] , [26] . The transmissibility of H5N1 viruses in mammalian models , such as pigs and ferrets , has been inefficient [27] , [28] , [29] . There are potentially two ways for H5N1 HPAI viruses to acquire efficient interhuman transmissibility: 1 ) genetic reassortment with circulating human influenza A viruses or 2 ) the accumulation of mutations during adaptation in mammalian hosts [30] , [31] , [32] . Potentially , pigs could be the natural host where either of these events could occur . There are a number of reports of natural H5N1 HPAI virus infection of animals taxonomically belonging to the order Carnivora ( i . e . domestic cats , tigers , leopards , dogs and stone martens ) [33] , [34] , [35] , [36] , [37] . Data on isolation of H5N1 viruses from pigs ( Sus scrofa , family Suidae , order Artiodactyla ) has been very limited [38] , [39] , [40] . Sero-epidemiological studies of Choi and co-authors [27] show that 0 . 25% ( 8 of 3 , 175 ) of pig sera collected at slaughterhouses in Vietnam in 2004 were seropositive from H5N1 virus infections . Studies of serum samples collected from pigs during H5N1 poultry outbreaks in Korea during the winter season of 2003 did not reveal any evidence of H5N1 HPAI virus infection [41] . No virological or serological confirmation of infection was observed in miniature pigs after experimental infection with A/chicken/Yamaguchi/7/04 and A/duck/Yokohama/aq-10/03 ( H5N1 ) viruses [42] . Inoculation of Yorkshire white piglets with two Hong Kong 1997 H5N1 HPAI isolates , and two Vietnamese and two Thai 2004 isolates resulted in mild to moderate infection restricted mainly to the respiratory tract [43] , [27] . Since 2003 , H5N1 viruses has evolved rapidly and formed 2 major clades and multiple subclades based on the HA sequences phylogeny and antigenicity [18] , [44] . In the present study we infected pigs with four H5N1 viruses representing clades 1 and 2 , and subclades 2 . 1 , 2 . 2 and 2 . 3 ( Figure 1 ) . Virological , histological and serological features of H5N1 infection in pigs were characterized and compared with those caused by swine H3N2 and H1N1 viruses .
In order to characterize the variety of H5N1 viruses , 4 strains isolated from human , poultry and wild birds , A/Vietnam/1203/04 ( VN/04 ) , A/Chicken/Indonesia/7/03 ( Ck/Indo/03 ) , A/Whooper swan/Mongolia/244/05 ( WS/Mong/05 ) , and A/Muscovy duck/Vietnam/209/05 ( MDk/VN/05 ) were chosen for this study . Phylogenetic analysis of the HA gene sequences of the H5N1 viruses showed that they represented clades 1 and 2 , and subclades 2 . 1 , 2 . 2 and 2 . 3 ( Figure 1 ) , respectively . Analysis of amino acid sequences of the HA revealed that all four viruses had conserved amino acid residues that retained the receptor binding of 2 , 3-NeuAcGal linkages predicted to confer affinity for avian cell surface receptors [17] , [45] . The growth and infectivity of 3 viruses were comparable in MDCK cells and embryonating chicken eggs while titers of Ck/Indo/03 virus were lower ( Table 1 ) . All four H5N1 viruses killed chickens after intranasal inoculation and intravenous pathogenicity tests [46] indicating these viruses were highly pathogenic for chickens . Pathogenicity of H5N1 viruses was also characterized in mouse and ferret models . Intranasal inoculation of 8-weeks-old female BALB/c mice with 103 50% egg infective dose ( EID50 ) of VN/04 , WS/Mong/05 , and MDk/VN/05 viruses resulted in systemic infection with 90–100% mortality . Ck/Indo/03 virus inoculated at the same dose produced mild lung infection without serious disease and mortality in mice . Only one H5N1 virus , VN/04 , was highly pathogenic in 4–6-month-old female ferrets producing severe systemic disease with 100% fatality after intranasal inoculation of 106 EID50 of virus . Infection of ferrets with 106 EID50 of WS/Mong/05 virus resulted in severe respiratory disease without systemic infection and mortality , and was considered to be of moderate pathogenicity . Viruses , Ck/Indo/03 and MDk/VN/05 were considered as low pathogenicity in ferrets causing mild or asymptomatic respiratory infection in animals intranasally inoculated with 106 EID50 of virus . The data on pathogenicity of H5N1 viruses are summarized in Table 1 . Groups of 2–3-weeks-old piglets were inoculated intranasally with 106 EID50 of H5N1 viruses . Controls that demonstrate the susceptibility of the animals to influenza virus infection , consisted of two groups of piglets that were intranasally infected with 106 EID50 of swine H3N2 , A/Swine/North Carolina/307408/04 ( Sw/NC/04 ) , and H1N1 , A/Swine/Indiana/1726/88 ( Sw/IN/88 ) influenza viruses . Body weight of infected pigs was measured daily and compared with that of mock-infected animals . No changes in food consumption or behavior were observed in inoculated animals . However , infection with swine influenza viruses produced slight lethargy and listlessness on day 1 after inoculation in one animal infected with H3N2 and in two animals infected with H1N1 viruses . Pigs inoculated with H5N1 viruses , MDk/VN/05 and VN/04 , as well as those inoculated with H3N2 virus , Sw/NC/04 , did not demonstrate remarkable differences in body weight compared to control animals ( Figure 2A , B , E ) . However , weight loss of 5–15% was seen in pigs inoculated with WS/Mong/05 and Ck/Indo/03 H5N1 viruses on days 1–4 ( Figure 2C , D ) . The most severe , up to 25% , decrease in weight was observed on day 3 in one animal infected with swine H1N1 influenza virus ( Figure 2F ) . To detect viruses and determine infective titers , nasal and rectal swabs were collected from infected animals . None of the influenza A viruses were detected in rectal swabs . Differences were observed in nasal excretion among the H5N1 viruses: WS/Mong/05 virus was detected in all 4 pigs on days 1 and 3 after inoculation , 3 of 4 pigs shed Ck/Indo/03 virus on days 1 and 3 , VN/04 virus was detected in nasal swabs of 3 pigs on day 1 and only in 1 pig on days 3 and 5 , while MDk/VN/05 virus was not detected in nasal swabs of inoculated pigs ( Figure 3 ) . Swine H3N2 and H1N1 viruses were detected in all inoculated pigs on days 1 , 3 , and 5 ( Figure 3 ) . In general , titers of H5N1 viruses in nasal samples collected on day 1 and 3 were similar , and were 2–3 log10 lower than those of swine H3N2 and H1N1 viruses which were detected at the similarly high titers ( Figure 3 ) . To determine sites of viral replication , samples from 18 organs and tissues ( see Materials and Methods ) were collected from infected pigs on day 5 after virus inoculation . H5N1 influenza viruses as well as swine H3N2 and H1N1 viruses were detected only in tissues from the respiratory organs ( Figure 4 ) . All studied H5N1 viruses were detected in the lungs of inoculated pigs . Lung titers of WS/Mong/05 and MDk/VN/05 ( detected in one of two pigs ) viruses were high and comparable with those of swine H3N2 and H1N1 viruses , while lung titers of Ck/Indo/03 and VN/04 ( detected in one of two pigs ) viruses were remarkably lower . MDk/VN/05 virus was also detected in nasal turbinate of one infected pig . The replication sites and titers of WS/Mong/05 virus , which was detected in lungs , trachea and tonsils , were close to those of swine H3N2 and H1N1 influenza viruses which were detected at high titers in upper and lower respiratory tract ( Figure 4 ) . Gross and microscopic lesions were observed in the respiratory tract of all pigs inoculated with either avian or swine influenza viruses . The extent and character of the lesions were variable between pigs in a group , and among virus treatment groups . When present , lesions were most often observed in the lungs . H5N1-inoculated pigs had minimal to mild gross lesions . Microscopic lung lesions included mild to moderate bronchiolitis and alveolitis found on day 5 post inoculation . In addition , moderate lymphocytic infiltration around peribronchiolar and perivascular areas ( Figure 5A ) , mild degeneration to necrosis of bronchiolar epithelium , and moderate necrotic cell debris in the airways of bronchioles and alveoli ( Figure 5D ) were observed . The upper airways and bronchi were spared lesions . Immunohistochemically , viral antigen was detected in bronchiolar epithelium ( Figure 5B and E ) . On day14 post-inoculation , there was no histological lesion in any visceral organs including lungs . Viral antigens were detected only in the lung of pigs inoculated with VN/04 , WS/Mong/05 and MDk/VN/05 viruses which were also positive on virus isolation . Based on gross and microscopic lesions , the pathogenicity of the H5N1 viruses could be ranked in the following order: WS/Mong/05 , VN/04 , MDk/VN/05 , and Ck/Indo/03 . By comparison , the respiratory lesions from pigs infected with swine viruses ( H3N2 and H1N1 ) were more severe and more extensive than those from pigs infected with H5N1 viruses . The lungs from pigs infected with swine viruses on day 5 had severe bronchointerstitial pneumonia characterized by severe degeneration and necrosis of bronchial epithelium and accumulation of necrotic cellular debris within airway lumens ( Figure 5G ) . Consistently , viral antigen was conspicuously detected to bronchial epithelial linings and cellular debris in the airway ( Figure 5H ) . In addition , the nasal cavities of pigs infected with H3N2 swine virus showed mild vacuolar degeneration and necrosis of mucosal epithelium; also , severe tracheobronchitis was observed in both H3N2- and H1N1-infected pigs . Mild lymphocytic infiltration around peribronchial areas was still evident in the lungs of swine viruses-infected pigs on day 14 post-inoculation . However , no viral antigen was detected in any tissues or organs on day 14 by immunohistochemistry . Recently , human infection with H5N1 viruses was reported to produce apoptosis in alveolar epithelial cells and leucocytes in the lungs [47] . To determine whether H5N1 viruses result in similar lesions in pigs , lung sections adjacent to those confirmed for presence of viral antigen from animals infected with H5N1 and H3N2 influenza viruses were stained by TUNEL assay . Apoptosis was frequently observed in proliferating cells , most likely leukocytes and macrophages in the lungs of pigs infected with all four H5N1 viruses . In general , the amount of cells with apoptosis correlated with the severity of lesions produced by H5N1 viruses in the lungs . The greatest numbers of stained cells were observed in the lung samples from pigs infected with VN/04 ( Figure 5C ) and WS/Mong/05 ( Figure 5F ) viruses . In contrast , very small , almost negligible numbers of cells with apoptosis , comparable with those in uninfected control lung samples , were observed in animals infected with swine H3N2 virus ( Figure 5I ) . To confirm the infection , blood samples collected from pigs prior to and two weeks after virus inoculation were examined in hemagglutination inhibition ( HI ) and virus neutralization ( VN ) tests with the homologous viruses to assess the seroconversion . Pre-infection sera lacked antibodies detectable by HI or VN test with H5N1 viruses , but small , almost negligible antibody titers ( presumably of maternal transfer origin ) were observed only in the HI test when using swine H1N1 and H3N2 viruses as the HI test antigen ( Table 2 ) . By contrast , all pigs challenged with H5N1 viruses Ck/Indo/03 and WS/Mong/05 had specific antibodies in HI and VN tests ( Table 2 ) on day 14 post-inoculation . High antibody titers were also observed in both HI and VN tests in serum from 1 pig ( of 2 ) inoculated with H5N1 VN/04 virus , and very low titers of virus-neutralizing antibodies were detected in 1 pig ( of 2 ) inoculated with H5N1 MDk/VN/05 virus . All animals seroconverted after intranasal inoculation with swine H1N1 and H3N2 viruses as evident by high levels of antibodies in both HI and VN tests using the challenge viruses ( Table 2 ) . The consumption of raw or undercooked infected bird meat or other products is one of potential means of transmission of H5N1 HPAI virus to humans [11] , [24] and several animals belonging to order Carnivora [33]–[37] , [48] ) . To model this potential route of infection , piglets in one group of 4 were fed breast and thigh meat from chickens that died from infection with WS/Mong/05 H5N1 virus . The meat was chopped into small pieces approximately 4 cm×2 cm×0 . 5 cm in size and mixed with a limited amount of pelleted diet . Each animal consumed approximately 100 g of meat with infective virus titer 108 EID50/g . No disease signs such as significant weight loss , changes in food consumption or behavior abnormalities were observed in exposed pigs during the 14 day observation period . Virus was detected in nasal swabs from 2 of 4 pigs on day 3 only ( Table 3 ) . No virus was detected in rectal swabs . Two pigs were euthanatized on day 5 after meat consumption and samples from 18 organs and tissues ( see Materials and Methods ) were harvested to determine virus distribution and histological lesion . Infective virus was detected in nasal turbinate and tonsils of both animals ( Table 3 ) . Microscopically , the organs or tissues lacked histological lesions and viral antigen was not demonstrated . However , virus-neutralizing antibodies to WS/Mong/05 virus were detected in serum samples from both pigs collected on day 14 after consumption of infected meat indicating infection had occurred ( Table 3 ) .
It was proposed that expression of sialic acid receptors for human and avian influenza viruses on epithelial cells of the trachea [5] , renders pigs susceptible to infection with both types of influenza viruses [3] , [4] , [49] , [50] . Influenza viruses from pigs can be transmitted to humans [3] , [4] , [51] , [52] as well as human viruses and human/pig gene reassortant viruses can be isolated from pigs [53] . Recently , a H2N3 swine influenza subtype was reported in the USA . It was an avian/swine reassortant virus that was pathogenic in pigs and mice , and was transmitted among swine and ferrets [54] . Thus , it seems possible to propose that H5N1 highly pathogenic avian influenza viruses , which spread through Eurasia and Africa , could reassort in pigs with currently circulating human influenza viruses and/or adapt to efficient transmission in humans , and acquire a pandemic potential . In this study we characterized in a pig model virological , histological , and serological features of infection with H5N1 HPAI viruses representing major HA phylogenetic and antigenic clades and subclades of currently circulating H5N1 viruses , i . e . clade 1 and clade 2 , subclades 2 . 1 , 2 . 2 and 2 . 3 ( Figure 1 ) . These viruses differed in their pathogenicity in well characterized mammalian models , i . e . mice and ferrets ( Table 1 ) . Three of the H5N1 viruses replicated systemically in mice and caused high mortality , but only one caused high mortality in ferrets . In contrast all four viruses had similar low pathogenicity in intranasally inoculated pigs . In pigs , the H5N1 viruses replicated only in the respiratory tract with no evidence of systemic infection . All four H5N1 viruses replicated in lungs of inoculated pigs and resulted in moderate or mild bronchiolitis and alveolitis . WS/Mong/05 and MDk/VN/05 H5N1 viruses were also detected in upper respiratory tract tissues ( trachea ) and tonsils . In contrast to the other studied H5N1 viruses , titers and organ distribution of WS/Mong/05 ( clade 2 , subclade 2 of H5 HA ) in inoculated pigs were most similar to those seen with the swine H3N2 and H1N1 viruses . With the exception of severity , the type and location of virus-induced lesions in the lower respiratory tract of H5N1-infected pigs were similar to those observed in humans [11] . However , viral antigens in pigs infected with H5N1 viruses were detected immunohistochemically in bronchiolar epithelial cells only , in contrast to reported patterns of H5N1 virus attachment to type II pneumocytes in pig , ferret and human lungs [55] , [56] , and human cases there viral antigens were observed in ciliated and nonciliated tracheal epithelial cells [57] and type II pneumocytes [57] , [58] . Interestingly , lung infection of pigs with H5N1 viruses resulted in apoptosis in proliferating leucocytes and macrophages while infection with swine influenza viruses did not , although greater severity of histological lesions were noted with swine influenza virus infections . As we did not find apoptosis in alveolar epithelial of H5N1-infected pigs , our finding only partially resembles the observations of Uiprasertkul and co-authors [47] where frequent apoptosis was identified in alveolar epithelial as well as in proliferating leucocytes in lungs of humans who died in the course of H5N1 virus infection . Our observation suggests tissue pathogenesis of avian H5N1 and swine H3N2 viruses in pigs might be different and such differences could underlay the lower efficacy of replication of H5N1 HPAI viruses in pigs . Serological studies with pigs showed very low pre-challenge levels of antibodies detectable only in HI test with swine H1N1 and H3N2 influenza A viruses ( Table 2 ) . Such antibodies most likely represented maternal transfer . Studies in a mouse model demonstrated that antibodies to human influenza A viral neuraminidase N1 could partially protect animals from lethal infection with H5N1 viruses [59] . This observation raised a concern that maternal antibodies to N1 could influence the course of H5N1 infection in pigs and was the reason for including H1N1 swine influenza virus challenge . However , the antibodies to H1N1 virus did not interfere with H1N1 swine influenza virus replication in pigs challenged with Sw/IN/88 . Furthermore , the antibodies to H3N2 virus did not inhibit replication of H3N2 swine influenza virus . From the current experiments , the detection of H5N1 virus replication and presence of specific serum antibodies against H5N1 virus implies that the low levels of H1N1 antibodies did not significantly interfere with H5N1 virus replication in the respiratory tract of pigs . Overall , the results of this study indicate that commercial piglets can support replication of H5N1 HPAI viruses , but their susceptibility to infection is low . The course of H5N1 virus infection in pigs was almost asymptomatic which could delay or prevent diagnosis of H5N1 infection in pigs . The infected pigs shed H5N1 virus , but the viral titers were lower and time of shedding was shorter in comparison with H1N1 and H3N2 swine influenza viruses . In addition , there was individual strain variation following infection of pigs with different H5N1 viruses . Intranasal inoculation with MDk/VN/05 ( subclade 2 . 3 ) produced infection detected by a single seroconversion and no virus recovery from nasal cavity , while inoculation with VN/04 virus ( Clade 1 ) produced a seroconversion in one of two pigs and low titers of virus were found in nasal cavity on day 1 in 3 pigs . By contrast , the virus isolated from wild migratory birds , WS/Mong/05 ( subclade 2 . 2 ) infected all pigs in the group , and tissue tropism and titers of this virus were similar to those of swine influenza viruses . However the individual susceptibility of pigs to influenza infection is highly variable . As the number of animals in this study was minimal and not suitable for statistical evaluation , we can not exclude that differences observed among the H5N1 viruses are the result of variations in individual susceptibility of pigs . In addition , consumption of chicken meat infected with high titers of virus ( 1010 EID50/pig ) produced a subclinical infection in pigs . The presence of virus in tonsils and the upper respiratory tract suggests that contact between the infected meat and oropharynx initiated infection , most likely through the tonsil . During the 2003 H7N7 poultry outbreak in the Netherlands , infections were detected in pigs on farms with infected poultry , and in some instances , the pigs had been fed broken eggs from the infected chickens [60] . This suggests consumption of infectious virus in raw or uncooked contaminated product can potentially transmit the virus to mammals . The main question resulting from the current study is why this experimental mammalian host has lower susceptibility to infection as compared to ferrets and mice ? It is possible , that further detailed studies of immunopathogenesis of H5N1 infection in pigs will reveal the mechanism of such resistance . This knowledge could be extremely useful for new approaches for treatment of H5N1-induced disease and for the design of new antivirals .
H5N1 viruses A/Chicken/Indonesia/7/03 ( Ck/Indo/03 ) and A/Whooper swan/Mongolia/244/05 ( WS/Mong/05 ) were isolated at Southeast Poultry Research Laboratory from field samples by passage in 10-day-old embryonating chicken eggs . Human isolate of H5N1 highly pathogenic avian influenza virus , A/Vietnam/1203/04 ( VN/04 ) was obtained from World Health Organization collaborating laboratories in Asia through National Institute of Allergy and Infectious Diseases , National Institutes of Health ( NIAID , NIH ) , Bethesda , MD , USA . H5N1 virus A/Muscovy duck/Vietnam/209/05 ( MDk/VN/05 ) was provided by Dr . Nguyen Van Cam from National Center for Veterinary Diagnosis , Hanoi , Vietnam . Swine H3N2 virus A/Swine/North Carolina/307408/04 ( Sw/NC/04 ) and H1N1 virus A/Swine/Indiana/1726/88 ( Sw/IN/88 ) were obtained respectively from National Veterinary Services Laboratories , Ames , Iowa , USA and the University of Wisconsin , Madison , Wisconsin , USA . Virus stocks were produced by passage in 10-day-old embryonating chicken eggs . H5N1 viruses Ck/Indo/03 , WS/Mong/05 , MDk/VN/05 were the 2nd chicken embryo passage and VN/04 isolate was the 4th chicken embryo passage after isolation . The allantoic fluid from infected eggs was harvested , divided into aliquots , and stored at –70°C until it was used for experiments . The infectivity of stock viruses was determined in 10-day-old embryonating chicken eggs and in Madin-Darby canine kidney ( MDCK ) cells according to standard procedures . The 50% egg infective dose ( EID50 ) and the 50% tissue culture infectious dose ( TCID50 ) values were calculated by the Reed-Muench method [61] . All experiments with live H5N1 viruses were performed in a biosafety level 3 agriculture ( BSL-3AG ) biocontainment facility , and all personnel were required to use respiratory protection when working with live viruses or infected animals . MDCK cells were obtained from the American Type Culture Collection ( Manassas , VA ) and were cultured in Dulbecco's Modified Eagle's Medium supplemented with 5% fetal bovine serum . Two to three weeks-old male castrated piglets ( Landrace×Large White cross ) were purchased from a local commercial farm . The pigs did not receive any vaccines on the production farm . In the BSL-3AG animal laboratory facilities pigs were housed in HEPA-filtered isolation units at a constant 27°C . Three to five days were taken to acclimatize animals to the facility . Piglets were feed with commercially available pelleted diet in amounts prescribed by the manufacturer to fulfill all dietary needs . Animal experiments were conducted according to the protocols approved by the Institutional Animal Care and Use Committee based on the applicable laws and guidelines . Each virus treatment group consisted of 4 pigs that were anesthetized with the intramuscular injection of ketamine ( 20 mg/kg ) and xylazine ( 2 mg/kg ) mixture and inoculated intranasally with virus dose of 106 EID50 in 2 ml of PBS ( 1 ml in each nostril ) . Control pigs ( two separated groups of 2 animals ) were inoculated with 2 ml of sterile PBS . The pigs' body weights , temperatures and feed consumption were monitored daily , starting 1 day before inoculation and ending on day 11 after inoculation . Nasal and rectal swabs were collected 3 or 4 days before the infection and on day 1 , 3 , 5 , 7 , 9 , and 11 after virus inoculation . Swabs were tested in 10 day-old embryonating chicken eggs to detect and titer virus ( lower detection limit , 100 . 5 EID50/ml ) . Before the titration , each sample of allantoic fluid that was positive in a hemagglutination test was confirmed to be influenza A virus positive by solid phase ELISA assay ( BinaxNow , Scarborough , ME ) . Virus titers were expressed as log10 EID50 per 1 ml of swab media . Two pigs from each group were euthanatized on day 5 after virus inoculation and the following organs and tissues were collected during the necropsy: nasal turbinate , tonsils , trachea , lungs , olfactory bulbs , brain ( transverse section through mid-cerebrum , thalamus , cerebellum/pons and medulla oblongata ) , heart , whole blood ( collected in sterile PBS to prevent clotting ) , spleen , liver , stomach , pancreas , small intestine ( upper part of duodenum and middle part of jejunum ) , large intestine ( rectum ) , kidney , adrenal glands , diaphragm , and skeletal muscle . Tissues were weighed and grounded in sterile PBS with antibiotics to prepare 10% homogenates . Samples were injected into 10 day-old embryonating chicken eggs for virus detection and titration as described above . Pigs were bled one day before and on day 14 after virus inoculation . To destroy non-specific inhibitors , serum samples were heat inactivated at 56°C for 30 min and treated with 10% chicken red blood cell ( CRBC ) for 60 min at 4°C . Serum antibody titers were determined in hemagglutination inhibition ( HI ) test with 0 . 5% CRBC and virus neutralization test ( VN ) in MDCK cells according to standard procedures described previously [29] . Virus infective dose of 100 TCID50 was used for VN test; MDCK cells were incubated for 72 h at 37°C . Tissues samples collected at necropsy on day 5 and 14 after virus inoculation were preserved in 10% neutral buffered formalin . After fixation , the tissues were routinely processed and embedded in paraffin . Sections were cut at 5 µm and stained with hematoxylin and eosin . Duplicate sections were cut and immunohistochemically stained using a mouse-derived monoclonal antibody ( P13C11 ) specific for type A influenza virus nucleoprotein antigen as the primary antibody . The procedures used to perform the immunohistochemistry followed those previously described [62] , [63] . Fast red was used as the substrate chromagen , and slides were counterstained with hematoxylin . Two to five sections of each organ was stained with hematoxylin and eosin and their immunohistochemically stained duplicates were analyzed . Lung sections from infected and control animals were analyzed for apoptosis by using the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling ( TUNEL ) assay ( In Situ Cell Death Detection Kit , POD , Roche , Mannheim , Germany ) , according to the protocol provided by the manufacturer , and slides were counterstained with hematoxylin . Viral RNAs were extracted from the allantoic fluid by the use of Trizol LS reagent ( Invitrogen Inc . , Carlsbad , CA ) . Standard reverse transcription-PCR was performed by use of a One-Step RT-PCR kit ( QIAGEN , Valencia , CA ) with primers specific for influenza virus HA of H5 subtype . The primer sequences and amplification conditions used are available upon request . The PCR products were separated in an agarose gel by electrophoresis , and amplicons of the appropriate sizes were subsequently excised from the gel and extracted by use of a QIAGEN gel extraction kit . Sequencing was performed with a PRISM Ready Reaction DyeDeoxy Terminator cycle sequencing kit ( Perkin-Elmer , Foster City , CA ) run on a 3730 automated sequencer ( Perkin-Elmer ) . DNA sequences were completed by using the Lasergene sequence analysis software package ( DNAStar , Madison , WI ) . The nucleotide sequences of WS/Mong/05 and MDk/VN/05 HA genes have been deposited in the GenBank database under accession numbers EU723707 and EU723708 respectively . Reference sequences of the HAs of H5 subtype were uploaded from the Influenza Sequence Database at Los Alamos National Laboratory ( www . flu . lanl . gov ) [64] . Sequences ( nucleotides 77 to 1723 ) were compared by ClustalW alignment algorithm by using BioEdit Sequence Alignment Editor ( www . mbio . ncsu . edu/BioEdit/bioedit . html ) . To estimate phylogenetic relationships , we analyzed nucleotide sequences by the neighbor-joining method with 500 bootstraps by using PHYLIP ( the PHYLogeny Inference Package ) version 3 . 65 ( http://evolution . gs . washington . edu/phylip . html ) . | Highly pathogenic avian influenza A viruses of H5N1 subtype have spread through Eurasia and Africa with continuing cases of human infection , suggesting the potential to become a pandemic influenza virus . Pigs which are susceptible to infection with both human and avian influenza A viruses are one of the natural hosts where a pandemic virus could be produced . In this study , we characterized in a pig model the infection caused by four H5N1 virus strains isolated from humans , poultry and wild birds . We demonstrated that exposure of pigs through the nose with H5N1 viruses or consumption of meat from infected chickens resulted in infection with mild weight loss . In contrast to mouse and ferret animal models where some of viruses were highly pathogenic and replicated in multiple organs , replication of H5N1 viruses in pigs was restricted to the respiratory tract , mainly to the lungs , and tonsils . Mild to moderate bronchiolitis and pneumonia were observed in the lungs of infected animals . Our results demonstrated that domestic pigs had low susceptibility to infection and disease with highly pathogenic H5N1 influenza A viruses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/emerging",
"viral",
"diseases",
"virology/animal",
"models",
"of",
"infection",
"virology"
] | 2008 | Domestic Pigs Have Low Susceptibility to H5N1 Highly Pathogenic Avian Influenza Viruses |
Hantaviruses are zoonotic viruses transmitted to humans by persistently infected rodents , giving rise to serious outbreaks of hemorrhagic fever with renal syndrome ( HFRS ) or of hantavirus pulmonary syndrome ( HPS ) , depending on the virus , which are associated with high case fatality rates . There is only limited knowledge about the organization of the viral particles and in particular , about the hantavirus membrane fusion glycoprotein Gc , the function of which is essential for virus entry . We describe here the X-ray structures of Gc from Hantaan virus , the type species hantavirus and responsible for HFRS , both in its neutral pH , monomeric pre-fusion conformation , and in its acidic pH , trimeric post-fusion form . The structures confirm the prediction that Gc is a class II fusion protein , containing the characteristic β-sheet rich domains termed I , II and III as initially identified in the fusion proteins of arboviruses such as alpha- and flaviviruses . The structures also show a number of features of Gc that are distinct from arbovirus class II proteins . In particular , hantavirus Gc inserts residues from three different loops into the target membrane to drive fusion , as confirmed functionally by structure-guided mutagenesis on the HPS-inducing Andes virus , instead of having a single “fusion loop” . We further show that the membrane interacting region of Gc becomes structured only at acidic pH via a set of polar and electrostatic interactions . Furthermore , the structure reveals that hantavirus Gc has an additional N-terminal “tail” that is crucial in stabilizing the post-fusion trimer , accompanying the swapping of domain III in the quaternary arrangement of the trimer as compared to the standard class II fusion proteins . The mechanistic understandings derived from these data are likely to provide a unique handle for devising treatments against these human pathogens .
Hantaviruses are a small group of zoonotic viruses of rodents , bats and insectivores such as moles and shrews [1] . They are often transmitted to humans by persistently infected rodents , causing serious outbreaks of pulmonary syndrome or of hemorrhagic disease with renal syndrome [2 , 3] . The case fatality rates can reach 50% , for instance in the case of the “Sin Nombre” hantavirus outbreak in the 1990s in the four-corners area in the US [4] . The name hantavirus derives from the prototype virus , Hantaan virus , which was discovered in the early 1950s during the Korean war , when troops stationed by the Hantaan river developed hemorrhagic manifestations [5] . Outbreaks of hantavirus disease of varying severity have occurred periodically in the last decades throughout the Americas [6 , 7] and in Europe and Asia [8 , 9] . It is therefore important to understand the structural organization of hantavirus particles as a step forward in attempts to devise curative or preventative strategies . Hantaviruses constitute one of five genera forming the Bunyaviridae family of enveloped RNA viruses , which have a genome composed of three segments of single-stranded RNA of negative polarity [10] . The bunyavirus proteins involved in genome replication–the large ( L ) polymerase and the nucleocapsid ( N ) protein , encoded in the large and small genome segment , respectively–are very similar to their counterparts in other families of segmented negative-strand ( sns ) RNA viruses , such as the Arenaviridae or the Orthomyxoviruses [11] . In contrast , the envelope glycoproteins , which derive from a polyprotein precursor encoded in the medium ( M ) size genomic segment , are totally unrelated . Whereas the other snsRNA virus families display class I membrane fusion proteins characterized by a central alpha-helical coiled-coil in their post-fusion form , the bunyavirus envelope proteins have properties of class II enveloped viruses [12]; i . e . , they are β-sheet-rich proteins ( as predicted from their amino acid sequences ) such as those found in the icosahedrally symmetric flaviviruses and alphaviruses . The latter , like viruses in the Bunyaviridae family other than the hantaviruses , are transmitted to vertebrates ( or to plants in the case of tospoviruses ) by insect or tick vectors , and are accordingly termed arthropod-borne viruses or “arboviruses” . In class II viruses , the membrane fusion protein is the second in a tandem of proteins–Gn and Gc in the bunyaviruses—encoded sequentially within a single precursor polyprotein . They heterodimerize in the ER and are then transported to the site of budding , which is the Golgi apparatus for a number of bunyaviruses [13] , although some hantaviruses were reported to bud at the plasma membrane [14 , 15] . The bunyavirus Gc glycoproteins were predicted to adopt a “class II” fold using proteomic computational analyses [16] , and Gc from Andes virus ( a hantavirus endemic in South America ) was modeled using the flavivirus E protein as template [17] , thereby predicting the location of the “fusion loop” , which was later functionally confirmed as important for fusion by site directed mutagenesis [18] . Similarly , for La Crosse virus in the Orthobunyavirus genus , mutagenesis of the predicted fusion loop region—deduced by comparison to the alphavirus fusion glycoprotein E1—confirmed the importance of this region for entry [19] . More recently , the determination of the crystal structure of Gc of the Rift Valley Fever virus [20] , a bunyavirus in the Phlebovirus genus , demonstrated that it indeed has the typical fold of class II membrane fusion proteins , providing a conclusive proof . These observations are compatible with the icosahedral organization of the phleboviral particles observed by electron microscopy [21 , 22] . Extrapolation to the other genera should be done with caution , however , since the sequence similarity is very low and not easily detectable by current methods . In the case of the Flaviviridae family , for instance , where viruses outside the Flavivirus genus were also expected to have class II fusion proteins , the prediction was proven wrong when the corresponding crystal structures were determined , i . e . the E2 protein from the pestiviruses [23 , 24] and from the hepatitis C virus [25 , 26] . Structural studies on members of the Bunyaviridae family are relatively limited , providing the low-resolution overall arrangement of spikes at the virus surface , but not the organization of the individual proteins . Differently to the icosahedral organization observed in virions of the Phlebovirus [21 , 22] and Orthobunyavirus [27] genera , hantavirus particles were shown to be pleomorphic , with some virions showing a roughly spherical and others an elongated aspect [28] . Cryo-electron tomography and sub-tomogram averaging revealed that the Gn/Gc spikes are arranged with apparent 4-fold symmetry [29 , 30] on a curved square lattice , incompatible with icosahedral symmetry . The resulting array of hantavirus spikes does not cover the whole surface of the observed particles . The organization of the Gn/Gc subunits within the spikes was interpreted as a ( GnGc ) 4 hetero-octamer . A recent study described the crystal structure of the Gn ectodomain of Puumala hantavirus , which was docked on the spike using the 3D reconstruction of Tula hantavirus extended to 16Å resolution , placing Gn at an exposed site on the spike , distal to the viral membrane [31] . Here , we report the crystal structure of the ectodomain of Gc from Hantaan virus in a monomeric pre-fusion conformation and in its trimeric post-fusion form , validating its prediction as a class II fusion protein , albeit presenting a number of hantavirus-specific features . Combined with structure-guided functional studies on the related Andes virus , we show that hantavirus Gc has a multi-partite membrane interaction surface , with residues outside the cd loop ( which is the fusion loop in standard class II proteins ) also being important for membrane insertion and fusion . We further show that Gc requires the formation of a carboxylate-carboxylic acid hydrogen bond , which can form only at acidic pH , for structuring the membrane interacting region . In addition , we show that hantavirus Gc has an N-terminal segment ( the “N tail” ) which is absent in other class II proteins and which is functionally involved in trimerization to form the stable post-fusion form . Further analysis also identifies a conserved “cysteine” signature in the amino acid sequence of Gc giving rise to a disulfide bonding pattern conserved in the Nairovirus , Orthobunyavirus and Tospovirus genera , but notably different from the phlebovirus Gc .
We expressed the recombinant ectodomain of Gc ( rGc ) from Hantaan virus strain 76–118 ( UniProtKB/SwissProt accession: P08668 . 1 ) in Drosophila S2 cells as described in Materials and Methods . It behaved in solution as a monomer , both at neutral and acidic pH , as assessed by size exclusion chromatography ( SEC ) and multi-angle static light scattering ( MALS ) ( S1A Fig ) . Electron micrographs of negatively stained samples of purified rGc showed a thin rod-like molecule of ~140Å in length ( see below ) . Despite multiple trials , rGc failed to crystallize on its own , and we therefore screened a human single-chain variable domain ( scFv ) antibody fragment library , which in principle was hantavirus-naïve [32] , to identify potential binders that could act as crystallization chaperones . We thus identified scFv A5 , which is very close to its germ line ( Table 1 ) and which interacted with rGc as monitored by ELISA ( see Methods ) . Further analysis by size-exclusion chromatography ( SEC ) together with multi-angle light scattering ( MALS ) , showed that scFv A5 forms a 1:1 complex with rGc ( S1B Fig ) . We obtained crystals diffracting to 3Å resolution of the A5/rGc complex at pH 7 . 5 , after limited proteolysis of the complex with trypsin . This treatment removed a C-terminal segment of rGc and the purification tags of both , A5 and rGc . We determined the X-ray structure by a combination of molecular replacement with the variable domains of an antibody and the anomalous scattering from a samarium derivative ( see Materials and Methods ) . The final electron density map was clear for amino acids ( aa ) 16–414 of rGc ( out of residues 1–457 in the intact Gc ectodomain , Fig 1A ) , with internal breaks at loops 84–91 and 108–132 ( Fig 1B ) . These disordered segments map to the tip of domain II , at the membrane interacting region , as discussed below . All A5 residues were clearly resolved in density , except for the linker connecting light and heavy chains in the scFv construct . An atomic model of the complex was built into the electron density with the program Coot and refined with Phenix . refine to an R factor of 22% and free R factor of 27% ( S1 Table ) . The scFv makes an important contribution to the packing contacts in the crystal . The antibody buries about 1 , 000 Å2 of the accessible surface of rGc , with 60% of the contacts made by the light chain ( which is un-mutated with respect to its germ line , see Table 1 ) . In spite of the relatively large buried antibody/antigen surface , affinity measurements by surface plasmon resonance ( SPR ) indicated an A5:rGc binding constant in the micromolar range , in line with the absence of affinity maturation of the human scFv library ( S1C and S1D Fig ) . The structure shows that Gc displays a typical class II fusion protein fold ( Fig 1 ) , first observed in the ectodomain of the flavivirus E [34] and alphavirus E1 [35] fusion proteins , and more recently in the rubella virus E1 glycoprotein [36] and in phlebovirus Gc [20]–the only other genus of the Bunyaviridae family for which a Gc structure is available . The class II fold features a central β-sandwich domain ( termed domain I ) made of eight β-strands labeled B0 through I0 , connected sequentially with up-and-down topology and arranged in two antiparallel β-sheets , the “inner” B0I0H0G0 and the “outer” C0D0E0F0 sheets apposed against each other ( the names of the β-sheets refer to their orientation in the post-fusion trimer ) . Like flavivirus E and phlebovirus Gc , hantavirus Gc has an additional short N-terminal β-strand , A0 , which starts at residue 18 and runs parallel to strand C0 at the edge of the outer sheet . The N-terminal segment , upstream of strand A0 and which contains a number of residues strictly conserved across the Hantavirus genus ( Fig 1D ) , is disordered in the crystals . The segments connecting β-strands D0 to E0 in the outer sheet and H0 to I0 in the inner sheet are very long and elaborated . They make up domain II ( yellow in the Figures ) , which is composed of 13 β-strands ( labeled a through l ) and a couple of short helices ( η1 and α2 , Fig 1 ) . Strand j’ , unique to hantavirus Gc and inserted between helix α2 and strand k ( Fig 1D ) carries the single Gc N-linked glycosylation site at Asn280 ( Fig 1B ) , which is strictly conserved across hantaviruses ( Fig 1D ) . Domain II has an elongated shape with two subdomains , a central , domain I-proximal open β-barrel made of β-strands klaefgj’ , and a distal “tip”—a β-sandwich between the bdc β-sheet and the ij β-hairpin , which projects the cd loop ( which is the fusion loop in the standard arbovirus class II proteins ) at its distal end . This region corresponds to the disordered Gc tip in the crystal structure , stabilized in part by the scFv A5 ( Fig 1B ) . Finally , after strand I0 , domain I connects via a 12 residue linker ( cyan in Fig 1 ) to domain III ( blue ) , which has an immunoglobulin superfamily C2 subtype fold [37] composed of β-strands A through G ( Fig 1B and 1D ) . Hantavirus Gc has a total of 26 cysteine residues in the ectodomain , 24 of which are present in the crystallized fragment , making 12 disulfide bonds . The surface area buried at the interface between domains I and III is very small , and is stabilized in the crystals by a cobalt-hexamine ion ( Fig 1B ) , which was identified in crystal optimization screens to improve the diffraction of the crystals . Although Gn was shown to be displayed prominently on the hantavirus spikes , and some of the epitopes of neutralizing antibodies were mapped there [31] , a number of hantavirus neutralizing antibodies target Gc as well . The only neutralization escape mutant mapping to Gc reported in the literature corresponds to an S287F change in Puumala hantavirus Gc and confers escape from neutralizing Mab 1C9 [38] , which maps to the k strand on one side of domain II ( Fig 1C ) . There are also data on Gc residues that affect binding of neutralizing antibodies for various other hantaviruses . Although often such epitopes are conformational and cannot be mimicked by peptides , the epitope of the neutralizing murine Mabs 3G1 and 3D8 [39] against Hantaan virus have been respectively mapped by peptide scanning to residues 96–105 [40] and 242–248 [41] . The 3G1 epitope thus maps to the bc loop and largely overlaps with that of scFv A5 , whereas the 3D8 epitope maps to the i strand , at the opposite side ( Fig 1C ) . The epitope of a human antibody against Hantaan virus , Y5 , was also identified by peptide scanning , and mapped to two discontinuous segments of the Gc polypeptide , 268–276 and 307–315 [42] , which correspond , respectively , to the region around helix α2 in domain II and to the end of strand I0 and the linker between domains I and III ( see Fig 1C and 1D ) . These two segments are far apart on the Gc monomer , but may be located more closely in a multimeric arrangement of Gc on the hantavirus spike . Although the docking of Gn appeared to be clear in the reported 16Å resolution electron density map of Tula hantavirus [31] and allowed to understand how the Gn epitopes are exposed on the spike , docking rGc is less clear in the same map and will need to await higher resolution data and/or a crystal structure of a Gn/Gc heterodimer to unambiguously assess where the Gc epitopes lie on the spike . In particular , as the epitopes map toward the Gc tip , which is partially disordered in the crystals , the conformation of this flexible region may be affected by Gn-Gc interactions on the spike . We investigated the behavior of rGc in interaction with lipids , and found that it binds liposomes at acidic pH as detected by flotation on sucrose gradients and by SPR on a matrix with immobilized liposomes ( see Materials & Methods ) ( S2A Fig ) . The SPR measurements also detected binding to liposomes at pH 7 . 4 ( S2B Fig ) , although to a lower extent than at pH 5 . 5 . We found that the interaction required the presence of cholesterol in the liposomes ( Fig 2A ) , in agreement with recent studies [43] . Addition of the scFv A5 interfered with lipid binding ( Fig 2A ) , in line with its epitope lying close to the membrane interacting region of Gc ( Fig 1 ) . Because the A5 epitope overlaps with that of the neutralizing Mab 3G1 , our data suggest that the neutralization mechanism of antibodies targeting this region involves blocking membrane insertion of Gc . Electron microscopy showed that the Gc proteoliposomes display radial projections with a shorter and thicker aspect than the observed overall shape of monomeric rGc in solution ( Fig 2B ) . The rGc projections on the liposomes are very similar in size and shape to the trimeric projections made by class II viral fusion proteins in their post-fusion conformation on liposomes [36 , 44 , 45] . This similarity suggested that rGc had adopted its predicted post-fusion conformation , as recently evidenced for Andes virus Gc by sucrose sedimentation in an in vitro system [46] . Because attempts to crystallize the membrane inserted form of rGc failed , we introduced a mutation in the predicted fusion loop , which caused rosette formation upon concentration after detergent solubilization ( in line with the model for insertion of trimeric post-fusion class II proteins into membranes , reviewed in [47 , 48] ) . The fusion loop mutation was inspired by a recent report showing that the trimeric post-fusion form of the flavivirus class II fusion protein E could be crystallized in its post-fusion , trimeric form in the absence of detergent by replacing Trp 101 by histidine , as this residue is prominently exposed at the membrane facing-end of the post-fusion trimer [49] . We accordingly substituted Trp115 , predicted to be at the tip of the cd loop in hantavirus Gc [18] by histidine . The rGc W115H mutant indeed crystallized under mildly acidic conditions ( pH 6 . 5 ) in the rhombohedral space group R3 , and the crystals diffracted to 1 . 6Å resolution . We determined the crystal structure of acid-pH rGc by molecular replacement using the individual domains of Gc , and refined the atomic model to 1 . 6 Å resolution to an R factor of 14% ( free R factor 17% ) ( S1 Table ) . A single Gc protomer ( or trimer subunit ) is present in the asymmetric unit of the crystals , packing about a crystallographic 3-fold axis and adopting the characteristic class II post-fusion form ( Fig 2C ) . As in the other class II proteins [36 , 50–53] , the post-fusion form shows a drastic re-orientation of domain III such that it packs laterally against the domain I/II junction of both , the same and the adjacent subunits in the trimer ( Fig 2C ) . This relocation of domain III is in line with recent data showing that exogenous domain III can block the fusion process [54] by binding to the domain I/II inner trimer core and interfering with the necessary translocation of domain III to reach the post-fusion hairpin conformation , as had been shown earlier for alpha- and flaviviruses [55] . The Gc structures show that during the pre- to post-fusion transition , domain II hinges by 26 degrees about the domain I/II junction , thereby bringing the domain II tips of the three protomers into contact at the trimer tip ( Fig 2D ) . The A5 epitope remains accessible on the trimer , and modeling shows that three scFvs can bind simultaneously to one trimer ( S2C Fig ) . The observed inhibition of trimer insertion is likely due to the scFv protruding further at the tip of the trimer than the fusion loop itself , helping the complex to remain in solution . The buried area per Gc subunit in the trimer is 2290 Å2 , and the residues at the interface are mostly hydrophilic and conserved ( S3 Fig ) . In contrast to the neutral pH form , the tip of domain II , ( the cd loop but also the neighboring parts of the bc and ij loops ) displayed clear electron density and allowed tracing the polypeptide chain with no breaks . As expected , the side chain of Trp115 ( which is His115 in the mutant used for the crystals ) is exposed at the very tip of domain II , where it would be expected to insert into membranes . In a previous study , Trp115 was indeed shown to be essential for fusion activity of Andes hantavirus [18] . That study also showed that Asn118 in the cd loop was essential for membrane fusion . The low pH structure now shows that the Asn118 side chain makes a crucial set of hydrogen bond interactions with the peptide backbone of the cd loop and its main chain with the ij loop ( Fig 2E ) . This key structuring-role of the domain II tip explains the strict conservation of Asn118 across hantaviruses ( Fig 1D ) and its functional importance for fusion . In addition to the role of Asn118 in structuring the fusion loop , we observed that the disorder at the tip of domain II in the monomeric , pre-fusion form of Gc begins at residue Asp108 in β-strand c , within a strictly conserved 106-EXD-108 amino acid motif ( where X is any amino acid ) ( Fig 1D ) . In the post-fusion structure , the side-chain of Glu106 is connected via hydrogen-bonds to the indole ring of the strictly conserved Trp98 and to the Asp108 side chain ( Fig 3A , left panel ) , making a relatively short ( 2 . 6 Å distance ) carboxylate—carboxylic acid hydrogen bond . The pK of the amino acids in the latter interaction is therefore shifted in the structure , which was obtained at pH 6 . 5 ( see S1 Table ) ( the normal pK of glutamic and aspartic acid is 4 . 2 and 3 . 8 , respectively ) , such that a proton is present in between . Of note , in crystals of rGc also at pH 6 . 5 obtained in the presence of KCl at concentrations above 200 mM , Gc was in its post-fusion form but the tip was disordered ( S4 Fig ) . The crystal-packing environment was not responsible for the observed disorder , as the crystals were isomorphous , having the same symmetry and the same cell parameters , and diffracting to high resolution ( around 1 . 4 Å ) . Inspection of the structure further showed that a K+ ion from the crystallization conditions becomes trapped near the central 3-fold axis of the rGc trimer , coordinated by the side chain hydroxyl group of Tyr105 together with the main-chain carbonyl oxygens of Phe240 , Gly242 of the same subunit and of Asp259 from a neighboring protomer , as well as an immobilized water molecule ( Fig 3B ) . Tyr105 directly precedes the di-carboxylate 106-EXD-108 motif , and as it adjusts its orientation to chelate the K+ ion , it alters the main chain such that Glu106 is pulled away from the interaction with Asp108 , becoming de-protonated and now accepting hydrogen bonds from the imidazole ring of His104 , while still maintaining the interaction with the indole ring of Trp98 ( Fig 3A , middle panel ) . These results therefore indicate that formation of the carboxylate-carboxylic acid hydrogen bond is essential to the organization of the tip of domain II , and that the reason why this region is disordered in the neutral pH structure is that this interaction cannot form ( Fig 3A , right panel ) . This disordered region in Gc at neutral pH is in line with the altered mobility of Gc in SEC at the two pH values measured , with the monomer at neutral pH displaying a larger Stokes radius for the same molecular mass ( S1A Fig ) . We tested the functional relevance of these interactions in the Andes hantavirus system , for which it was shown that expression of the M genomic segment ( i . e . , coding for wild type Gn and Gc proteins ) in cells induces syncytium formation when treated at low pH . We thus introduced each of the following Gc substitutions: E106A , E106Q , D108A , D108N and W98A into Andes virus Gc using this plasmid , and compared syncytium formation by the mutants and by the wild type protein . Although the level of Gn and Gc that reached the cell surface was similar to wild type ( S5 Fig ) , there was no syncytium induced by the mutants except for D108N ( Fig 3C ) , which can still make a hydrogen bond with the Glu106 side chain ( Fig 3A , left panel ) . The reverse situation , in the E106Q mutant , is not viable , indicating that the Glu106 side chain is essential in this process , perhaps because of its dual interaction with Asp108 and Trp98 ( Fig 3A , left panel ) . We confirmed these results by introducing the same mutations into a system of SIV particles pseudotyped with the Andes virus glycoproteins , which allows visualization of entry by expression of a fluorescent reporter gene [56] . Again , only the D108N mutant was as efficient as wild type for entry ( Fig 3D ) , whereas none of the other mutants was , in spite of being present in similar amounts as wild type Gn and Gc on these particles ( S5 Fig ) . The requirement for a glutamic acid to be protonated in order to organize the structure of the domain II tip only upon acidification is unique to hantavirus Gc , as it has not been described so far for any other membrane fusion protein . An important difference with the post-fusion structures of the arbovirus class II proteins is that in the quaternary organization of hantavirus Gc , domain III takes the place occupied in the other trimers by its counterpart from a neighboring protomer . This had been observed previously in the structure of the rubella virus E1 glycoprotein in its post-fusion conformation ( Fig 4A ) , the only other non-arbovirus viral class II protein of known structure [36] . In this altered quaternary organization of the Gc trimer , the strictly conserved glycan chain of Gc at Asn280 in domain II fits snugly into a groove at the subunit interface , with the glycan making a number of inter-chain hydrogen bonds with hydrophilic amino acids at the domain III surface from the adjacent subunit ( Fig 4B ) . Ablation of this glycosylation site in Hantaan virus Gc was shown to give rise to a Gn/Gc glycoprotein complex that was able to reach the cell surface [57] , but could not induce syncytium of transfected cells upon low pH treatment [58] . These observations are in line with the added trimer stability provided by the glycan contacts . The domain III swap is accompanied by the N tail and by strand A0 ( residues 19–21 in the pre-fusion form ) , which switches from its parallel interaction with strand B0 to run antiparallel ( residues 14–19 in the post-fusion form ) to C0 of the neighboring subunit in the trimer ( Fig 2F ) , thereby augmenting the inner sheet and providing an extensive pattern of inter-subunit main chain hydrogen bonds . Other inter-chain interactions between strictly conserved residues involving the Gc N tail include hydrogen bonds and salt bridges between His14 and Asp174 in β-strand G0 and between Asp11 and His399 in domain III , as well as Trp9 packing against Pro321 and disulfide 9 ( Cys322-Cys352 ) ( Fig 4C ) , also in domain III . The domain I/III linker ( residues 310–321 ) runs along the A0 strand , making several antiparallel β-sheet interactions with it ( Fig 4C ) . At the very N-terminal end , residues 1 through 5 ( which are variable in sequence across the hantaviruses , Fig 1D ) project into solvent and are disordered . As the N tail is not present in the other class II proteins , but in hantaviruses a number of its residues are strictly conserved ( Fig 1D ) and are seen in the structure to make a network of interactions , we turned to the Andes virus system to functionally test some of these residues . We chose to substitute Asp11 , Thr12 and His14 by alanine , and also His14 by tyrosine to see if a bigger side chain could functionally substitute for histidine . These mutants were all expressed correctly and reached the cell surface ( S5 Fig ) , but syncytium formation and cell entry by the corresponding pseudotyped SIV particles was abrogated ( Fig 4D and 4E ) . In order to further explore whether trimerisation of the mutants is impaired , we made virion-like particles ( VLPs ) of Andes virus [60] harboring the mutations D11A and H14Y in Gc . We harvested VLPs from cells transfected with wild type Gn/Gc or with wild type Gn and the mutant Gc , which secreted VLPs at similar levels ( S5 Fig ) . We analyzed the VLPs after low pH treatment followed by detergent solubilization for Gc trimer formation by sedimentation on a sucrose gradient . The amount of mutant Gc trimer formation was similar to the wild type VLPs ( Fig 4F ) , indicating that trimerization is not impaired . We therefore analyzed the stability of the resulting acid-induced mutant Gc trimers by trypsin digestion of the VLPs . We observed that , contrary to wild type Gc , the low pH treated D11A and H14Y mutants were not resistant to proteolytic degradation ( Fig 4G ) . In parallel , we found that the alanine substitution of Tyr88 , Trp115 or Phe250 , which are located at the tip of Gc domain II and which are also impaired in fusion ( see below ) but for which the side chains are not involved in inter-protomer contacts in the trimer , resulted in trypsin-resistant trimers as wild type Gc , serving as a positive control . These data indicate that the inter-subunit interactions of the Gc N tail are important to confer sufficient stability of the Gc post-fusion trimer in order to be fusion active . The hantavirus Gc ij loop is longer than the corresponding loop in standard class II fusion proteins , and it places Phe250 at the tip of the trimer , along with Trp115 and Pro123 in the cd loop and Tyr88 in the bc loop ( Fig 5A ) . To understand whether these residues are involved in the functional targeting of the host cell membrane , we performed alanine substitutions and examined the corresponding mutants in the context of Andes hantavirus with the tools described above . We examined these mutants alongside the W115A mutant characterized previously [18] . As W115A , the Y88A , P123A and F250A mutants were properly expressed and the corresponding Gn/Gc complexes trafficked to the plasma membrane of transfected cells ( S5 Fig ) . Except for the P123A mutant , which behaved as wild type and served a positive control , the Y88A and F250A mutants were impaired in syncytia formation , and did not support entry of the pseudotyped particles into cells ( Fig 5E and 5F ) , similar to the previous results with the W115A mutant . We also analyzed the interaction of VLPs generated with the same mutants with fluorescently labeled liposomes using a sucrose gradient . When VLPs and liposomes incubated at pH 7 were run on the gradient , the liposomes were found by fluorescence floating on top of the gradient , while Gc was recovered from the bottom fractions ( Fig 5G ) . But when the VLPs were incubated with liposomes at pH 5 . 5 , each of the single mutants was recovered in the top fractions of the gradient , as was wild type ( Fig 5G ) , indicating that substitution by alanine of a single residue at the tip of domain II was not sufficient to abolish the interaction with membranes necessary to float with the liposomes . We also tested VLPs containing double mutations ( which produced VLPs in similar yields as wild type , S5 Fig ) : whereas Y88A/W115A still partially floated with the liposomes , W115A/F250A remained in the bottom fractions at acidic pH . But neither of them mediated low pH-induced syncytia formation , as expected from the single substitution mutants ( Fig 5E ) . In order to identify the actual step at which substitution of W115A and F250A block the membrane fusion process , we tested whether these single or double mutants still underwent acid-induced Gc trimerization , since it was shown previously that Gc can trimerize in the absence of membrane insertion [46] . As expected , sedimentation in a sucrose gradient of acid-treated VLPs including Gc mutants W115A and W115A/F250A revealed that , independently of the membrane inserting activity , these mutants underwent homotrimerization as did wild type Gc ( Fig 5H ) . This result is in line with the structure of the post-fusion trimer , in which the side chains of these residues are not involved in trimer contacts but are exposed at the membrane-interacting side of the trimer ( Fig 5A ) . To further investigate the stage at which fusion is blocked with these mutants , we incubated pyrene-labeled VLPs [61] bearing wild type Gc or the mutants with liposomes , in order to monitor lipid mixing . Acidification resulted in decrease of the fluorescence of the pyrene excimer within 20 sec in the case of wild type Gc , reflecting lipid mixing ( Fig 5I ) during fusion . In contrast , the VLPs carrying the Gc double mutant W115A/F250A , which do not insert into target membranes as monitored by liposome flotation , displayed no signal for lipid mixing , corroborating the assay ( Fig 5I ) . When we ran this experiment with VLPs containing the Gc fusion inactive single substitution mutants Y88A , W115A or F250A , we could still detect lipid mixing upon low pH incubation with the liposomes ( Fig 5I ) , indicating that these mutants led to an incomplete fusion process , most likely arrested at the hemifusion stage , as they do not induce full fusion ( Fig 5E and 5F ) . Indeed , if only the labeled lipids of the outer leaflet become diluted during hemifusion , then the expectation is to obtain a lower lipid mixing signal , as observed– , provided that there is no lipid flipping from inner to outer leaflets of the membrane during the time frame of the experiment . The fact that the mutants did not induce full fusion ( Fig 5E and 5F ) indicates that the observed lipid mixing was due to hemifusion with negligible lipid flipping under the conditions of the experiment . These results therefore indicate that the Gc single mutants do not insert stably enough into the membrane to induce full fusion , but they still can induce lipid mixing . We conclude from these results that the longer ij loop observed in hantavirus Gc , as well as the bc loop with Tyr88 projecting into the membrane , have functional implications in engaging the target membrane such that full fusion can proceed . In contrast to the arbovirus class II proteins , which appear to have a single fusion loop–the cd loop–in hantaviruses the membrane interacting surface is multipartite . Amino acid sequence alignment of hantavirus Gc with its counterparts from viruses of the various genera of the Bunyaviridae family allowed the identification of a motif that systematically identifies Gc glycoproteins from four out of the five genera , leaving out the phleboviruses . The alignment used to extract this motif is displayed in Fig 6 , and includes four of the disulfide bonds that stabilize the tip of domain II as well as one of the disulfides in domain I ( the one stapling together β-strands E0 and F0 ) . This alignment allows the prediction of the connectivity of the additional cysteines in the other genera ( S8 Fig ) . Two features appear important: as hantavirus Gc , the other genera also have N-terminal extensions , which are quite large ( as in orthobunyavirus Gc ) , and lack the N-terminal disulfide bond stapling β-strand A0 and C0 as in the other class II fusion proteins featuring an A0 strand ( i . e . , phlebovirus Gc , flavivirus E and the cellular fusion protein EFF-1 ) . Such a disulfide bond would be incompatible with the rearrangements of strand A0 that are necessary to have a swapped domain III in the post-fusion form , and one prediction therefore is that the phlebovirus Gc will have the “standard” class II quaternary arrangement , whereas in all the other genera the post-fusion form of Gc is likely to feature a swapped domain III . Similarly , the ij loop is also longer in the Nairovirus , Orthobunyavirus and Tospovirus genera than in the standard class II proteins , and is likely to contribute to the membrane interacting surface ( see Fig 5A , 5B and 5C ) . In contrast , the carboxylate—carboxylic acid hydrogen bond in strand c , which structures the tip of domain II , appears as a hantavirus-specific feature , as these residues are not conserved across these bunyavirus genera .
Membrane fusion is a critical step in entry for any enveloped virus , and in hantaviruses it is mediated by the highly conserved glycoprotein Gc . Because of this conservation , the features identified in the structures of Hantaan virus Gc in its pre- and post-fusion forms can be applied to all members of the Hantavirus genus . We have taken advantage of this conservation to use the molecular tools developed to test the glycoproteins of Andes virus for function . By combining structural and functional data , one important aspect that we have discovered is the multi-partite nature of the membrane interaction surface of hantavirus Gc as well as the key role played by the N tail for fusion . These results set hantavirus Gc—and by extension , also Gc from the three more closely related genera in the Bunyaviridae family , see below–apart from the more standard class II fusion proteins observed in the flaviviruses and phleboviruses . The latter have an A0 strand ( contrary to alphavirus E1 , which lacks strand A0 ) but it is locked by a disulfide bond to the C0 strand of the same polypeptide chain , prohibiting a conformational rearrangement similar to the domain III swap , which is accompanied by the N tail ( Fig 2F ) . The various loops interacting with the target membrane in hantavirus Gc are reminiscent of the bi-partite membrane contacting region of class III fusion proteins [65] , with two fusion loops—which may also require elements from the C-terminal , membrane proximal region to engage the target membranes and induce full fusion . A more extensive membrane binding region was previously observed in the class II fusion protein E1 of rubella virus , which features an insertion within the cd loop to make two short α-helices and an additional β-strand ( c’ ) running parallel to strand c , such that the bdc β-sheet at the tip of domain II becomes a four-stranded bdcc’ β-sheet [36] . This results in two fusion loops , cc’ and c’d , projecting toward the target membrane , and a Ca2+ site in between the two loops that is essential for function [66] . An analogous role appears to be played by Asn118 in hantavirus Gc , by bridging two loops in the membrane interacting region ( Fig 2E ) . A further similarity with the rubella virus post-fusion E1 trimer is the swapped domain III , although in E1 there is neither A0 strand nor N tail to accompany this conformational change , and the transition is not yet understood in the absence of a structure of E1 in its pre-fusion form . The mechanistic implications derived from the structures of the hantavirus Gc can be extended to other bunyaviruses , further broadening the scope of this work . Indeed , our comparison of hantavirus Gc with its counterparts of the other genera indicates that the hantavirus and nairovirus Gc proteins are closer to each other than they are to Gc from other bunyaviruses , and that Asn118 , which plays a key structuring role within the cd loop and the interaction with the ij loop ( Fig 2E ) is conserved across the two genera ( Fig 6 and S8 Fig ) . We also note the conservation in nairoviruses of Trp9 and His14 of the N tail , which are involved in the network of interactions illustrated in Fig 4C , supporting the notion that a similar domain III swap may occur in nairoviruses as well . As nairoviruses require the additional cleavage of preGc into mature Gc at the N-terminal end , at an “RKPL” site corresponding to a non-standard subtilase SKI-1 like protease [67] , it is possible that the cleavage is necessary to release the N tail such that the fusogenic conformational change can take place ( the RKPL sequence is about 40 residues upstream the first residue displayed in the alignment of the S8A Fig ) . The sequence conservation pattern further shows that orthobunyavirus and tospovirus Gc proteins are also closer to each other than they are to the others ( S8B Fig ) , and feature an insertion in the ij loop , which becomes even longer ( Fig 6 ) . The amino acid alignment indicates that a dibasic motif downstream the disulphide 4 in the ij loop ( highlighted in a cyan background in Fig 6 ) will face an acidic “EE” motif in the cd loop ( highlighted in the same Figure ) at the location of Asn118 in hanta- and nairoviruses , suggesting that a different set of polar/electrostatic interactions may replace the network formed by Asn118 at the tip of domain II in Gc of these two genera . Our results on hantavirus Gc therefore reveal a number of unanticipated aspects of the bunyaviruses in general . In particular , they introduce an evolutionary hierarchy for the five genera based on the Gc gene , which appear to display a deep branching site between phleboviruses and the others , the latter then splitting into two branches each , giving rise to hantaviruses and nairoviruses on the one hand , and to orthobunyaviruses and tospoviruses on the other , with mechanistic similarities for the membrane fusion process accompanying this diversification . We thus propose to group these fusion proteins as a sub-class within class II , having by specificity the fact of presenting a multipartite membrane targeting region , together with an N tail involved in the fusogenic conformational rearrangement . Additional structure-function studies on the Gc orthologs from each of these branches will further nail down the practical implications of the identified evolutionary trends , opening the possibility of unveiling common vulnerability sites on Gc to be targeted by broad-spectrum anti-bunyavirus compounds to help treat patients against these deadly pathogens .
In order to obtain milligrams amount of soluble Hantaan virus Gc , we inserted a synthetic gene codon-optimized for expression in Drosophila cells , into a modified pMT/BiP plasmid ( Invitrogen ) . This initial construct , termed pMT-Gn/rGc , included the full length Gn followed by the Gc ectodomain , i . e . lacking the transmembrane and cytoplasmatic domains . To help in the purification we included two strep-tag sequences separated by a ( GGGS ) 3 linker and preceded by an enterokinase cleave site . As this construct produced only small amounts of soluble rGc , most likely because it is retained within the cells , either by Gn and/or because the fusion loop of Gc interacts with cellular membranes , we removed Gn from the construct to make pMT-rGc . As mentioned in the main text , we also introduced the W115H mutation in this plasmid ( pMT-rGc-W115H ) . Similarly , scFv A5 , identified as binding to rGc by screening the Griffin library using helper phage KM13 [68] , was cloned into the modified pMT/BiP plasmid followed by a double strep-tag ( pMT-scFvA5 ) . These plasmids were used separately to obtain stable transfectants of Drosophila S2 cells together with the pCoPuro plasmid ( ratio 1:20 ) for puromycin selection . The stable cell lines were selected and maintained in serum-free Insect-Xpress medium containing 7 μg/ml puromycin . Cultures of 1–3 liters were grown in spinner flasks in Insect-Xpress medium supplemented with 1% penicillin/streptomycin antibiotics to about 1 x 107 cells/mL , and the protein expression was induced with 4 μM CdCl2 . After 5 days , the S2 media supernatant was concentrated to 40 ml and supplemented with 10 μg/mL avidin and 0 . 1M Tris-HCl pH 8 . 0 , centrifuged 30 minutes at 20 , 000 g and purified by strep-tactin affinity chromatography and gel filtration . The yields were about 5–10 mg/L for rGc and rGc-W115H and 10–15 mg/L for scFv A5 . For crystallization , rGc was incubated on ice for 30 minutes with a 1 . 2 molar excess of scFv A5 with the pH adjusted to 8 . 5 by adding TrisHCl pH 8 . 5 to a final concentration of 100 mM . The mixture was digested with trypsin ( mass ratio of 1:100 ) , for 1 hour at 37°C , stopping the reaction by adding 1 mM of PMSF and cooling on ice . The digest was loaded to a gel filtration Superdex 75 16/60 column in 10 mM Tris HCl pH 8 . 0 , 150 mM NaCl , and the fractions of the peak corresponding to the complex were pooled and concentrated in the same buffer for crystallization trials . The rGc-W115H construct was used to obtain the post-fusion form without adding detergent , as explained in the main text . Digestion with enterokinase ( New England Biolabs ) after Strep-tactin affinity purification to remove the Strep-tag for crystallization showed an immediate cleavage but overtime a second , shorter resistant fragment accumulated overnight at 4°C . Only this second enterokinase resistant fragment , in which rGc appeared to lose the stem region , resulted in crystals . Inspection of the stem region indeed indicates several lysine residues where enterokinase could cleave . In the final protocol , the enterokinase treatment was allowed to proceed overnight at 4°C , and then the digest was submitted to gel filtration on a Superdex 200 16/60 column in 10 mM Tris HCl pH 8 . 0 , 150 mM NaCl . The protein was then buffer exchanged and concentrated in 20 mM Bis-Tris pH 6 . 1 , 150 mM NaCl , using a Vivaspin centricon , to a final concentration of 5–10 mg/ml . We determined the structure of the rGc/scFv A5 complex by a combination of molecular replacement ( MR ) and single-wavelength anomalous dispersion ( SAD ) . Native crystals were grown in 60 mM Na-HEPES pH 7 . 5 , 40 mM hexamine cobalt chloride salt , 13 . 5% ( w/v ) PEG 4K , 7 . 4% ( v/v ) 2-propanol , and 1% ( v/v ) glycerol and cryo-protected in the same solution supplemented with 22% ( v/v ) PEG 400 . The crystals used for the heavy atom derivative were grown in presence of 60 mM Na-HEPES pH 7 . 5 , 20 mM hexamine cobalt chloride salt , 13% ( w/v ) PEG 4K , 7 . 4% ( v/v ) 2-propanol , and 1% ( v/v ) glycerol . They were subsequently soaked for 18h in mother liquor supplemented with 2 mM samarium and cryo-cooled using 22% ( v/v ) PEG 400 as cryo-protectant before plunging into liquid nitrogen . In order to optimize the anomalous signal we collected a highly redundant dataset ( S1 Table ) , merging three independent datasets collected from a single crystal using a kappa goniometer ( kappa = 10° , 0° , -10° ) using an “inverse beam” strategy . The final SAD dataset could be processed up to a resolution of 3 . 65 Å and showed a usable ( CCanom > 0 . 3 ) anomalous signal up to 4 . 5 Å . To determine the phases , we first modelled an scFvA5 molecule using the RosettaAntibody3 program [69] through the ROSIE interface [70] . We then used the resulting model as MR template for PHASER [71] with the native dataset at 3 Å resolution . We obtained a single solution with a translation function Z-score of 7 . 4 in which the scFv molecules are packed together back to back around a two-fold axis , with the CDR loops exposed to solvent , leaving enough space to accommodate one Gc molecule . The initial MR phases were used to identify a set of heavy atom sites in the Sm-SAD dataset using the HySS ( Hybrid Substructure Search ) module of the PHENIX package [72] , and we used them to calculate SAD phases with PHASER . The combined MR and SAD phases were improved by density modification using RESOLVE [73] . All these steps were done automatically using the AutoSol wizard [74] in PHENIX . The final map was of enough quality to start manual model building , and the extended and corrected model was used as input model in a new MR-SAD cycle . With this iterative procedure we built a model that comprises full domains I and III , and a large part of domain II . We used domains I and III as MR templates to determine the structure of the rGc-W115H trimer at 1 . 6 Å resolution and build a complete model using this dataset . We then used the structure of the high-resolution post-fusion form as a guide to finish the model building and to generate restraints for refinement of the complex rGc/scFvA5 . The crystals of the rGc-W105H trimer were grown in 100 mM Na-MES pH 6 . 5 , 10 . 8% ( w/v ) PEG 8K , and 7% ( v/v ) glycerol and were cryo-protected in the same solution supplemented with 20% ( v/v ) glycerol . As described above , the structure was solved by MR using the partial models of domains I and III from the rGc/scFvA5 crystals . We also identified conditions of rGc-W105H trimer crystal growth in the presence of 500 mM KCl , which showed no density for the fusion loop region . We then carried out a detailed study of the effect of KCl by growing crystals of rGc-W105H in the presence of increasing concentrations of KCl . We collected datasets in the presence of 100 mM KCl , ( resolution 1 . 8 Å ) , 200 mM KCl ( 1 . 7 Å ) , 300 mM KCl ( 1 . 6 Å ) , 500 mM ( 1 . 5 Å ) , and 600 mM KCl ( 1 . 4 Å ) , where the values in parentheses are the resolution limits in each case . The structures of these crystals were determined by molecular replacement using as a model rGc-W105H . We refined the structures using phenix . refine and the final model validated with Molprobity [75] and CheckMyMetal [76] . The statistics of all crystals and refinement is provided in the S1 Table . Liposomes were prepared fresh each time by the freeze-thaw and extrusion method [77] . DOPC ( 1 , 2-dioleoyl-sn-glycero-3-phosphocholine ) , DOPE ( 1 , 2-dioleoyl-sn-glycero-3-phosphoethanolamine ) , sphingomyelin ( from bovine brain ) , cholesterol ( from ovine wool ) , PC ( phosphocholine , from egg yolk ) and PE ( phosphoethanolamine , prepared from egg phosphatidylcholine by transphosphatidylation ) were purchased from Avanti Polar Lipids . Type 1 , 2 , 3 and 4 liposomes were made using DOPC alone , DOPC/cholesterol ( 1/1 ) , DOPC/DOPE/sphingomyelin/cholesterol ( 1/1/1/3 ) and PC/PE/sphingomyelin/cholesterol ( 1/1/1/1 . 5 ) , respectively . Samples of rGc were negatively stained with phosphotungstic acid and screened with a Tecnai G2 Spirit Biotwin microscope 5 ( FEI , USA ) operating at an accelerating voltage of 120 kV . To obtain the rGc/liposomes pictures , 200 ng of rGc were added to 10 ul of a solution of 1 mM of type 3 liposomes in 20 mM MES pH 5 . 5 , 150 mM NaCl ( MN 5 . 5 ) . After 1 minute incubation at room temperature , the sample was spotted onto carbon coated glow discharged grids , contrasted with 2% phosphotungstic acid and screened in a TecnaiG2 Spirit Biotwin microscope operating at an accelerating voltage of 120 kV . Images were recorded using a 4Kx4K camera Eagle ( FEI , USA ) and the TIA software ( FEI , USA ) . All experiments were performed on a Biacore T200 instrument ( GEHealthcare ) equilibrated at 25°C in two different running buffers: PBS pH 7 . 4 , and MN pH 5 . 5 ( 20 mM MES pH 5 . 5 , 150 mM NaCl ) . We used the expression plasmid pI . 18/GPC for the expression of the full length GPC coding region ( encompassing both Gn and Gc in the M genomic segment ) of Andes virus strain CHI-7913 [56] . Site-directed mutations were generated by DNA synthesis and sub-cloning into pI . 18/GPC using intrinsic restriction sites . For expression , 293FT cells ( Invitrogen ) ( 3 . 6×106 ) grown in 100 mm plates were calcium-transfected with 8–20 μg of DNA and 48 h later , cell surface proteins were labelled with biotin in order to separate the biotinylated ( surface proteins ) from non-biotinylated ( intracellular proteins ) fractions using a cell surface protein isolation kit ( Pierce ) . For protein detection by western blot , primary antibodies anti-Gc monoclonal antibody ( MAb ) 2H4/F6 [78] or anti-β-actin MAb ( Sigma ) were used at 1:2 , 500 and subsequently detected with an anti-mouse immunoglobulin horseradish peroxidase conjugate ( Thermo Fisher Scientific ) 1:5 , 000 and a chemiluminescent substrate ( SuperSignal WestPico , Pierce ) . This assay was performed as previously described [18] . Briefly , Vero E6 cells ( ATCC ) seeded into 16 well chamber slides were transfected with the pI . 18/GPC wt or the different mutant constructs using lipofectamine 2000 ( Invitrogen ) . The DNA amounts were adjusted to obtain similar levels of Gc at the cell surface; the cells were accordingly transfected with plasmid DNA ranging between 0 . 5–1 . 5 μg . 48 h later , the cells were incubated in E-MEM ( pH 5 . 5 ) at 37°C for 5 min , subsequently washed , and the incubation continued for 3 h at 37°C in E-MEM ( pH 7 . 2 ) . The cell cytoplasm was then stained for one hour with 1 μM of CellTracker CMFDA ( Molecular Probes ) and cells were next fixed for 20 min with 4% paraformaldehyde . For immunofluorescence labelling , cells were permeabilized with 0 . 1% Triton X-100 in PBS and Gc labelled using the MAb 2H4/F6 1:500 and secondary antibody anti-mouse immunoglobulin conjugated to Alexa555 1:500 ( Invitrogen ) . Finally , nuclei were stained for 5 min with DAPI 1 ng/μL and samples examined under a fluorescence microscope ( BMAX51 , Olympus ) . The fusion index of Gc-expressing cells was calculated using the formula: 1- [number of cells/number of nuclei] . Approximately 200 nuclei per field were counted ( 20X magnification ) and five fields used to calculate the fusion index for each sample ( n = 2 ) of two independent experiments . VLPs were harvested from supernatants of 293FT cells transfected with the pI . 18/GPC wt or the different mutant constructs at 48 h post-transfection as previously established [60] . Subsequently , VLPs were concentrated by ultracentrifugation for one hour at 135 , 000 g and detected by reducing SDS PAGE and western blot using anti-Gc MAb as described above . The presence of VLPs was further corroborated by negative-stain electron microscopy using phosphotungstic acid 2% pH 7 . 4 ( FEI Tecnai 12 Transmission Electron Microscope , Philips ) . Simian immunodeficiency virus ( SIV ) vectors pseudotyped with ANDV Gn and wild type or mutant Gc were prepared as previously described [56] . Briefly , 293FT were transfected with the following plasmids: pSIV3+ [79] , pGAE1 . 0 [80] ( kindly provided by Jean-Luc Darlix , INSERM , ENS-Lyon , France ) and pI . 18/GPC wild type or the different mutant constructs . At 72 h post-transfection , pseudotyped vectors released into the supernatant were harvested , concentrated by ultracentrifugation at 135 , 000 g and used to transduce Vero E6 cells ( ATCC , CR-1586 ) . 72 h post-inoculation , cells were trypsinized and GFP expression assessed by flow cytometry ( FACScan , Becton Dickinson ) . Transduction titers were calculated using the percentage of GFP positive cells , counting at least 10 , 000 cells of each condition . The coflotation of viral particles with liposomes was performed as previously established [46] . Briefly , VLPs or GPC-pseudotyped SIV vectors prepared from wild type or mutant pI . 18/GPC construct were incubated with 200 mM 1 , 6-diphenyl-1 , 3 , 5-hexatriene ( DPH ) -labelled liposomes ( Type 4 ) for 30 min at pH 7 . 4 or 5 . 5 . The VLP-liposome mixture was then added to the bottom and adjusted to 25% ( w/v ) sucrose . Additional sucrose steps of 15% and 5% were then over-layered . After centrifugation for 2 h at 300 , 000 g , liposomes were detected by the fluorescence emission of DPH ( λex = 230 nm; λem = 320 nm ) and VLPs by western blot using anti-Gc MAb 2H4/F6 . Acid-induced Gc homotrimerization was tested as established before [46] . VLPs or GPC-pseudotyped SIV vectors prepared from wt or mutant pI . 18/GPC constructs were incubated for 30 min at 37°C at the indicated pHs to allow multimerization changes . Next , Triton X-100 1% ( v/v ) was added to allow the extraction of the membrane glycoproteins from the viral particle . The extracted glycoproteins were then added to the top of a sucrose gradient ( 7–15%; w/v ) and centrifuged at 150 , 000 g for 16 h . Finally , fractions were collected and the presence of Gc was analyzed by western blot using MAbs anti-Gc 2H4/F6 . The stability of the Gc homotrimer was studied by analyzing its resistance to trypsin digestion as determined before [46] . VLPs were incubated at pH 5 . 5 for 30 min at 37°C to allow Gc multimerization changes or pH 7 . 4 as a digestion control . Next , the VLPs were digested with 500 μg/ml of TCPK trypsin ( Sigma ) for 30 min , stopping the reaction by the addition of sample buffer and heating to 95°C for 10 min . The extent of Gc digestion was determined by western blot , using an anti-Gc MAb . The trypsin resistance of wild type and mutant Gc was quantified by dividing the densitometry values of the digested Gc signal by the undigested Gc assay input control for each mutant , using the Fiji software [81] . The average value and standard derivation of 3 experiments was calculated and a Student’s t test was performed for statistical evaluation: *** , P < 0 . 00025; ** , P < 0 . 0025; * , P < 0 . 025 . For the lipid mixing assay VLPs were metabolically pyrene-labeled by supplementing the producing cell’s media with 25 μg/ml of 1-pyrenehexadecanoic acid ( Molecular Probes ) . Labeled VLPs were mixed with liposomes and lipid mixing was monitored by the decrease in pyrene excimer fluorescence generated by the dilution of the pyrene-labeled phospholipids with the unlabeled phospholipids in the liposome membrane in a continuously stirred fluorimeter cuvette at 37°C . Fluorescence was recorded continuously at 480 nm using a Varian Eclypse Fluorescence Spectrophotometer ( Agilent Technologies ) at an excitation wavelength of 343 nm using a 10-nm slit width for excitation and emission . After a stable base line was established , the pH of the solution was lowered to 5 . 5 for reaction initiation ( time = 0 ) . The base line excimer value was defined as 0% lipid mixing and the maximal extent of excimer dilution was defined by the addition of detergent Triton X-100 after lipid mixing of each condition concluded . All relevant data are within the paper and its Supporting Information files . The coordinate files of the structures described in this manuscript have been submitted to the Protein Data Bank , and the corresponding accession codes are listed in the S1 Table . | Hantaviruses belong to the Bunyaviridae family of enveloped viruses . This family englobes in total five established genera: Tospovirus ( infecting plants ) , and Phlebovirus , Orthobunyavirus , Nairovirus and Hantavirus infecting animals , some of which cause serious disease in humans . An important characteristic of the hantaviruses is that they are not transmitted to humans by arthropod vectors , as those from the other genera , but by direct exposure to excretions from infected small mammals . As all enveloped viruses , they require the activity of a membrane fusogenic protein , Gc , for entry into their target cells . Our structural analysis of the hantavirus fusion protein Gc led to the identification of a conserved pattern of cysteines involved in disulfide bonds stabilizing the Gc fold . This motif is matched exclusively by all of the available bunyavirus Gc sequences in the database , with the notable exception of phlebovirus Gc , which appears closer in structure to the fusion proteins of other families of arthropod-borne viruses , such as the flaviviruses and alphaviruses . This analysis further suggests mechanistic similarities with hantaviruses in the fusion mechanism of viruses in the remaining three most closely related bunyavirus genera , which we propose belong to a new separate sub-class of fusion proteins with a multipartite membrane targeting region . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"chemical",
"bonding",
"medicine",
"and",
"health",
"sciences",
"vesicles",
"crystal",
"structure",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"condensed",
"matter",
"physics",
"microbiology",
"viral",
"structure",
"neuroscience",
"viruses",
"membrane",
"fusion",
"hantavirus",
"rna",
"viruses",
"ganglion",
"cells",
"crystallography",
"cellular",
"structures",
"and",
"organelles",
"hydrogen",
"bonding",
"bunyaviruses",
"physical",
"chemistry",
"solid",
"state",
"physics",
"animal",
"cells",
"medical",
"microbiology",
"microbial",
"pathogens",
"chemistry",
"liposomes",
"cell",
"membranes",
"physics",
"cellular",
"neuroscience",
"cell",
"biology",
"virology",
"viral",
"pathogens",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"afferent",
"neurons",
"physical",
"sciences",
"retinal",
"ganglion",
"cells",
"organisms"
] | 2016 | Mechanistic Insight into Bunyavirus-Induced Membrane Fusion from Structure-Function Analyses of the Hantavirus Envelope Glycoprotein Gc |
The current outbreak of Ebola Virus Disease in Upper West Africa is the largest ever recorded . Molecular evidence suggests spread has been almost exclusively through human-to-human contact . Social factors are thus clearly important to understand the epidemic and ways in which it might be stopped , but these factors have so far been little analyzed . The present paper focuses on Sierra Leone , and provides cross sectional data on the least understood part of the epidemic—the largely undocumented spread of Ebola in rural areas . Various forms of social networking in rural communities and their relevance for understanding pathways of transmission are described . Particular attention is paid to the relationship between marriage , funerals and land tenure . Funerals are known to be a high-risk factor for infection . It is suggested that more than a shift in awareness of risks will be needed to change local patterns of behavior , especially in regard to funerals , since these are central to the consolidation of community ties . A concluding discussion relates the information presented to plans for halting the disease . Local consultation and access are seen as major challenges to be addressed .
The present outbreak of Ebola Virus Disease ( EVD ) in Upper West Africa is the worst ever recorded . As of late December 2014 there were 6808 confirmed EVD cases [1] and there are no clear signs of the disease coming under control . The international community is alarmed , and resources are being rushed to the region to try and stem further spread . The epidemic is an outbreak of the Zaire strain of the virus [2] , previously associated with death rates of up to 90 per cent . Death rates in the Upper West African outbreak average 70 percent [3] . The epidemic has been traced to a single index case—the infection of a 2 year-old boy in the village of Meliandou , in the Republic of Guinea [4 , 5] . Previous outbreaks of the disease have occurred in remote forest edge communities , e . g . in the Democratic Republic of Congo and Gabon , and have been associated with hunting and eating of bush meat , though human to human transmission also occurred , especially via hospitals . The bush meat scenario is thought to explain the index case , but thereafter appears not to be appropriate for the present epidemic . Human-to-human transmission appears to be the main if not sole source of infection in Liberia , Guinea and Sierra Leone . In this paper we offer some data and observations relating to the Sierra Leone epidemic ( Fig 1 ) . If human-to-human contact is the main mode of transmission attention needs to be paid to underlying social factors . The paper is divided into three sections . A case-study based scenario for the spread of EVD in Sierra Leone is described ( based on interviews and direct observations ) . We propose that greater attention should be paid to rural buffers for the disease . We then identify and explain the role of processes related to marriage , land and burials significant for spread of the disease . A concluding discussion considers what assistance might be necessary if rural communities are to reduce transmission rates . Fogbo is a Kpa-Mende settlement located on the Taia river about 12 km . north of Taiama , the headquarters town of Kori chiefdom , in Moyamba District ( Fig 2 ) . Reachable only by track , the village has a population of about 500 people , larger than average for the region . Reports of Ebola in Fogbo filtered into Taiama in early August . The Community Health Officer visited the village and took a blood sample from a man suspected of having the disease . The health worker also ascertained that the case was connected to an outbreak in Daru ( Kailahun District ) . Ebola had reached Daru when a wife of the Paramount chief ( see Box 1 ) visited her sick sister , the wife of the Paramount Chief of Kissi Teng , the chiefdom including Koindu market on the Guinea border . A boy infected in the town of Daru came to Kenema , to visit his father . During the visit the young man began to develop symptoms , was taken to hospital , tested positive , and died . The father had also become infected . Apparently not wanting to be hospitalized , he left Kenema by night , evading the curfew , and travelled to his home town Fogbo , where he was cared for by his sister , a Sowei ( an elder of the women's secret society—known for her medical knowledge ) . The town's people and the man's sister did not know he had Ebola . A few days later the Sowei also became sick . The Community Health Officer was again informed and he took a blood sample , but the Sowei died before the result was available . The villagers concluded , without waiting for the result , that it was Ebola . The town chief called the health officials to come and take charge of the body , but they were unable to attend , and later instructed the people to bury the dead Sowei , but not to wash the corpse . Prominent women in the community insisted a Sowei respected by her society should be given a fitting burial , so they washed and buried the body . Corpse washing is an important part of local rituals for the dead . Thereafter , the wife of the town chief was stricken with EVD and died . Since then 16 women and one man have died , all apparently of EVD . By early-September it was reported that somebody in the village was dying every day , and there was nobody to bury the corpses . Local officials sent a message that if the villagers buried the dead without the consent of the government the people would be fined or imprisoned . The Fogbo people waited for the burial team to come . The team had still to reach the village three weeks later . By this time many people had left the village and gone to live on their rice farms . These farms , often several kilometers from the village , are equipped with simple shelters against the rain , where meals are prepared . More distant farms have special sleeping platforms . Retreating to the farm for days at a time in August-September is normal , since this helps protect the ripening crop from bird and rodent damage , and deters human thieves . Meanwhile , attendance at the woman's funeral had spread the virus to neighboring villages—Kowama and Bauya , where four people died , and six more were evacuated to an Ebola treatment facility in Kenema . Some of those infected in Kowama sought help from a retired pharmacist in the busy main-road trading center at Moyamba Junction , where the national "lockdown" called in September 2014 revealed both cases and bodies . One schoolteacher in Moyamba Junction died of Ebola at his home in Mile 91 , a larger trading location 16 km along the main road to Freetown . As of 20th September six people in Moyamba Junction had died and others were sick . The case figured on the radio during the government's lockdown period ( 19–21st September ) intended to facilitate tracing of hidden cases of Ebola . It was then reported many people had abandoned Moyamba Junction , perhaps fearing to be quarantined . The Fogbo case seems typical for the Sierra Leone epidemic , where the disease has moved at times in large jumps along the main road system , passing from town to town , but at other times diverts into the interior to infect isolated villages , where it is little noticed , reported or acted upon , only then to burst out again in a larger town or market center . This pendulum swing between roadside locations and buffer villages in the interior needs to be stopped . Developing effective strategy for this will require close attention to the social factors that allow , or encourage , the virus to spread in more isolated villages . The Fogbo case introduces a number of important social factors in Ebola transmission—notably , the role of the family , marriage , funerals , migration and markets . In this section we focus on each of these factors in turn , and offer some specific data about these variables . The aim is to draw attention to issues to be considered if Ebola control is to be achieved . We rely on data collected over the past four years during multiple rounds of detailed rural survey work intended to assess levels of rural institutional change in the post-civil war period . Four sources are used: ( i ) a study of household structures and food security in three isolated communities in northern Moyamba District adjacent to Fogbo , undertaken in May-June 2014 , ( ii ) a national random sample of 2200 rural households in 117 villages in 47 chiefdoms undertaken in 2014 ( S3 Dataset ) , ( iii ) a survey of 91 villages in 7 chiefdoms around the Gola Rainforest National Park in Kenema , Kailahun and Pujehun districts undertaken in 2013 ( S4 Dataset ) , and ( iv ) a survey of 187 village communities and 2460 households undertaken in 2010 in the same region ( S5 Dataset ) .
In the Fogbo case , discussed above , a carrier exposed to the disease in an urban location traveled towards a rural village , where family help and local remedies were sought . Distance frustrated attempts by the authorities to impose isolation and safe burial . This set up a new incubation focus for the disease , which spread locally without attracting further attention . Traders seeking rice and other food commodities then interacted with this local rural focus of the disease and drew the virus back towards market centers and the main roads , triggering further expansion of the epidemic . What does this pendulum swing imply for attempts to control the disease ? It is generally agreed that rapid identification , removal and isolation of cases for treatment is essential . This is made more complicated if the disease periodically dives into the backcountry . Drawing it out will require some ingenuity . The scale of the epidemic makes the active following of cases into the interior practically impossible . The alternative is for the unwell to seek out testing and treatment voluntarily , and in sufficient numbers to bring the reproduction rate of the disease below 1 . 0 . For this to work , our analysis above suggests there are three key tasks . First , accurate information has to be conveyed in interior settlements about the true causes of the disease and infection pathways . A survey in summer 2014 of attitudes in seven districts in Sierra Leone suggested that whereas many people accepted the reality of Ebola , more than 80 percent still thought it was caused by eating bush meat [6] . The role of funerals , body washing , social networking and market exchange in spreading the disease also needs to be carefully explained . Second , the option of seeking hospitalization has to be made truly attractive to the sick . This means that evidence the disease can be survived must be made more widely known . Survival rates in treatment centers also need to be boosted beyond a current estimated 35 percent [17] . These authors suggest ways in which this can be done . Local triage centers will also need to offer effective rapid testing , guaranteed medical supplies for treating other diseases , and reimbursement of travel costs incurred by the unwell and their family carers . Moving a very sick person out of an isolated village across non-motorable tracks is a major deterrent to referrals . The difficulty , expense , and ordeal is so high that it often seems better to the family not to move the patient and let God decide . Often a hammock ( perhaps three or four times more expensive than a motor bike taxi ) is the only realistic transport option . The costs to families , and the risks to helpers , should be fully assessed and built into the way any triage centers work , if they are to attract cases from interior villages . Third , attention has to be focused on how family groups in the villages can protect themselves from the disease when all other options are foreclosed . Families will not readily abandon their relatives in extreme crisis , so they need information on how to minimize the risks associated with tending sick patients . This is now addressed in a poster [18] offering guidance on "what to do while you wait" ( for arrival of an Ebola ambulance ) . In some cases , this wait can be indefinite [19] . The poster describes the need for one member of the family only to be designated as a nurse , and for all other members to provide support and encouragement , but only from a distance . Early use of oral rehydration salts [ORS] is recommended , coupled with advice on supplying this without the nurse directly handling the drinking cup . This now needs to be followed by some attention to practical items often lacking in village conditions ( e . g . sufficient supplies of soap , disinfectant , protective clothing , rubber gloves , buckets and ORS ) . Some guidance is also now available in the form of a protocol from the World Health Organization [20] covering safe and respectful burial in village conditions . Villagers are very clear ( see S1 Text ) that mandatory Ebola burials , carried out by one or other of the 90 or so national cadre of trained Ebola burial teams have caused considerable difficulty , due to haste and disrespectful treatment of bodies . At times this has amounted to little more than sanitary disposal . The protocol is a great improvement , since it now specifies that a pastor or imam be present in all Ebola funerals , and the involvement , at a distance , of family witnesses . But as yet the protocol does not allow enough local input to accommodate the sociological concerns about debt and inheritance mentioned above . It is important for these issues to be faced by those in charge of burial teams , since it will open the door to better local cooperation . Flexibility over burial ritual was already apparent even before the advent of Ebola . Where it was impracticable to bring a body home for burial the family pragmatically accepted that the funeral had to be organized in the place where death occurred . Further flexibility will be encouraged if villagers participate in focus sessions where they have an active hand in agreeing on the safest compromises . Corpse washing should be discouraged , but if it cannot be avoided then it should be done only with biohazard protection . Families should be encouraged to meet to agree to postpone outstanding final marriage settlements for the duration of the epidemic . In coming together to debate these issues villagers might also be encouraged to form village health clubs , to develop informal community "bye-laws" ( club rules ) to regulate against the most dangerous practices . In the case of Ebola , these rules might specify acceptable funeral practices , and when , why and how to quarantine patients if no other options present . In some Ebola epidemics villagers have resorted to building temporary shelters adjacent to settlements to care for suspected Ebola victims . Similar developments might be encouraged through village health clubs in Sierra Leone . Rules governing care provision while waiting for assistance need to be debated and agreed , e . g . to limit the number of carers to one person per family . The Ebola epidemic in Upper West Africa is the largest ever seen , and Sierra Leone is now the most seriously affected country . The international community perceives the epidemic as a threat to global security , and an abundance of help has now been provided to all three countries . Experts agree that with logistics in place containment should be a straightforward task . One thing that could blow this assessment off course is the persistence or revival of rural buffers of the disease . In Sierra Leone these are not found in forest-edge communities associated with zoonotic transmission but in the much more numerous farming villages that incubated the rebel war of the 1990s , due to inaccessibility and poor communications . An effective approach to control of Ebola Virus Disease requires detailed knowledge of these interior rural landscapes and how they function , including the key part played by rural-urban extended family networks . In turn , this knowledge should feed effective planning to extinguish the numerous further localized outbreaks that can be expected as a result of a now rampant urban epidemic feeding back upon far-flung rural locales as a result of dense rural-urban family and economic networking . | Ebola virus disease is a disease of social intimacy . The main infection pathways are through nursing of the sick and through preparation of corpses for burial . In African rural communities these are activities mainly undertaken by close family members . Infection thus spreads to those most intimate with the patient . In effect , Ebola virus disease exacts a high price for family loyalty . It also causes a potential breach between family members and the authorities responsible for imposing infection control . Understanding the social pathways through which infection spreads , and the conflict of human values invoked by infection control are thus key topics in managing the current Ebola epidemic in Upper West Africa . The paper offers a picture , based on case study data from central Sierra Leone , of some of the social factors to be taken into account in efforts to control the epidemic . | [
"Abstract",
"Introduction",
"Discussion"
] | [] | 2015 | Social Pathways for Ebola Virus Disease in Rural Sierra Leone, and Some Implications for Containment |
The present study aimed to evaluate a hypothetical Leishmania amastigote-specific protein ( LiHyp1 ) , previously identified by an immunoproteomic approach performed in Leishmania infantum , which showed homology to the super-oxygenase gene family , attempting to select a new candidate antigen for specific serodiagnosis , as well as to compose a vaccine against VL . The LiHyp1 DNA sequence was cloned; the recombinant protein ( rLiHyp1 ) was purified and evaluated for its antigenicity and immunogenicity . The rLiHyp1 protein was recognized by antibodies from sera of asymptomatic and symptomatic animals with canine visceral leishmaniasis ( CVL ) , but presented no cross-reactivity with sera of dogs vaccinated with Leish-Tec , a Brazilian commercial vaccine; with Chagas' disease or healthy animals . In addition , the immunogenicity and protective efficacy of rLiHyp1 plus saponin was evaluated in BALB/c mice challenged subcutaneously with virulent L . infantum promastigotes . rLiHyp1 plus saponin vaccinated mice showed a high and specific production of IFN-γ , IL-12 , and GM-CSF after in vitro stimulation with the recombinant protein . Immunized and infected mice , as compared to the control groups ( saline and saponin ) , showed significant reductions in the number of parasites found in the liver , spleen , bone marrow , and in the paws' draining lymph nodes . Protection was associated with an IL-12-dependent production of IFN-γ , produced mainly by CD4 T cells . In these mice , a decrease in the parasite-mediated IL-4 and IL-10 response could also be observed . The present study showed that this Leishmania oxygenase amastigote-specific protein can be used for a more sensitive and specific serodiagnosis of asymptomatic and symptomatic CVL and , when combined with a Th1-type adjuvant , can also be employ as a candidate antigen to develop vaccines against VL .
Visceral leishmaniasis ( VL ) caused by Leishmania donovani and L . infantum/L . chagasi represents an important disease in the world , leading to nearly 500 , 000 new cases and 50 , 000 deaths annually . The first choice of treatment for all forms of leishmaniasis is still based on the use of the parenteral administration of pentavalent antimonials; however , increased parasite resistance and several side effects reported by patients have been important problems [1] , [2] . Liposomal amphotericin B is effective but is too expensive for most patients [3] . Results from clinical trials using oral miltefosine are encouraging; however , therapy is linked to both potential toxicity and teratogenicity , and should not be given to childbearing-age women [4] . Therefore , the development of new strategies to prevent leishmaniasis has become a high priority . The evidence of life-long immunity to leishmaniasis has inspired the development of prophylactic vaccination protocols against the disease , but few have progressed beyond the experimental stage . Most experimental vaccines have focused on the mouse model for cutaneous leishmaniasis . Several studies have demonstrated the Type-1 cells mediated immunity-dependence for protective responses against disease . Moreover , Th1 cells response has also been correlated with protection against VL [5] . In this context , the protective immunity in murine VL primarily depends on an IL-12-driven Th1 cells response , leading to an increased IL-2 and IFN-γ production . Substantial uptake of inducible NO syntase by IFN-γ generates NO from splenic and liver cells , thereby controlling parasite multiplication in these organs [6] , [7] . By contrast , TGF-β , IL-10 , and IL-13 represent major disease promoting cytokines , leading in turn to the suppression of the Th1 response [8] . Low levels of IL-4 commonly enhance vaccine-induced protection by indirectly increasing IFN-γ production by T cells [9] . In recent decades , the majority of studies have focused on Leishmania promastigote antigens for vaccine development [10] , [11] , [12] , [13]; however , amastigote antigens also seem to be appropriate targets for the immune responses elicited by vaccines , given that after a few hours of infection and during the active disease , this parasite stage becomes exposed to the host immune system [14] . In addition , the fact that promastigotes can be easily cultured in vitro , as opposed to axenic amastigotes , has hampered the identification of amastigote-specific stage antigens [15] . In the present work , a hypothetical amastigote-specific L . infantum protein , LiHyp1 ( XP_0014689 41 . 1 ) , which has been identified by an immunoproteomic approach , was recognized by antibodies present in sera samples of dogs with asymptomatic and symptomatic VL [16] . The amastigote-specific Leishmania protein gene ( LiHyp1 ) is predicted to encode a protein with a theoretical molecular weight of 36 . 6 kDa . An in silico sequence comparison revealed that LiHyp1 belongs to the super-oxygenase family in Leishmania , and is an alkylated DNA repair protein . The ability of the recombinant protein ( rLiHyp1 ) to induce protection against infection with virulent L . infantum promastigotes was assessed in BALB/c mice . The results showed that rLiHyp1 was antigenic and specifically recognized by canine VL ( CVL ) sera , whereas a Th1 response , induced by immunization of a combination of rLiHyp1 and saponin , was able to confer protection against L . infantum . This protection correlated with a Leishmania antigens-specific and IL-12-dependent IFN-γ production , mediated mainly by CD4 T cells , as well as by a diminished production of parasite-specific IL-4 and IL-10 . Thus , the present study demonstrates that this unique amastigote-specific protein , a member of the super-oxygenase family in Leishmania , can be a new candidate for the improvement of serodiagnosis of CVL and , when associated with a Th1-type adjuvant , to develop an effective vaccine against VL .
Experiments were performed in compliance with the National Guidelines of the Institutional Animal Care , and Committee on the Ethical Handling of Research Animals ( CEUA ) from the Federal University of Minas Gerais ( Law number 11 . 794 , 2008 ) , with code number 043/2011 . Sera samples used in this study were kindly provided by Prof . Fernando Aécio de Amorim Carvalho ( Department of Pharmacology and Biochemistry , UFPI ) and Prof . Maria Norma Melo ( Department of Parasitology , Institute of Biological Sciences , UFMG ) . Female BALB/c mice ( 8 weeks age ) were obtained from the breeding facilities of the Department of Biochemistry and Immunology , Institute of Biological Sciences , UFMG , and were maintained under specific pathogen-free conditions . Experiments were carried out using the L . infantum ( MOM/BR/1970/BH46 ) strain . Parasites were grown at 24°C in Schneider's medium ( Sigma , St . Louis , MO , USA ) , supplemented with 10% heat-inactivated fetal bovine serum ( FBS , Sigma ) , 20 mM L-glutamine , 200 U/mL penicillin , and 100 µg/mL streptomycin , at pH 7 . 4 . The soluble L . infantum antigenic extract ( SLA ) was prepared from 1×1010 stationary-phase promastigote cultures ( 5–7 day-old ) , as described [17] . Parasites were kindly provided by Prof . Maria Norma Melo . The LiHyp1 ( XP_001468941 . 1 ) nucleotide and amino acid sequences used in this study were obtained from the National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov ) . The local alignment of the LiHyp1 sequence against the available complete genomes of other organisms was performed by BLAST . Analyses of basic physical and chemical properties , as well as phylogenetic analysis , were performed in a TriTrypDB database ( http://tritrypdb . org ) . The recombinant protein ( rLiHyp1 ) was obtained after having cloned a DNA L . infantum fragment containing the LiHyp1 coding region . Initially , genomic DNA was extracted by a phenol:chloroform extraction , as described [17] , and it was used as a template . Forward ( 5′-GAAGGATCCAGCATGTCTATCGTGTCGAG-3′ ) and reverse ( 5′-GGAAAGCTTCGCTTGCGGCGTCACGTGAGC-3′ ) primers were designed according to the DNA sequence of the ORF described in the L . infantum genome sequence database ( LinJ . 35 . 1290 ) . The PCR product was cloned into the pGEM-T easy vector confirmed by sequencing and transferred to the pET21a expression vector ( Novagen ) , using the BamHI and HindIII restriction enzymes included in the primers for this purpose ( underlined ) . Recombinant plasmid was transformed into Escherichia coli BL21 ( DE3 ) . The recombinant protein expression was performed by adding 0 . 5 mM IPTG ( isopropyl-β-D-thiogalactopyranoside , Promega , Montreal , Canada ) for 4 h at 37°C , when cells were lysed by a homogenizer after five passages through the apparatus . The product was centrifuged at 13 . 000× g for 20 min at 4°C , while the rLiHyp1 , containing a tag of 6× residues of histidine , was purified under non-denaturing conditions , using a 5 mL HIS-Trap column ( GE Healthcare Life Science ) attached to an FPLC ( GE Healthcare Life Science ) system . After purification , the recombinant protein was passed through a polymyxin-agarose column ( Sigma ) to remove residual endotoxin content . To evaluate the antigenicity of rLiHyp1 , sera samples from healthy ( n = 37 ) , vaccinated with Leish-Tec® ( n = 18 ) , T . cruzi experimentally infected ( n = 18 ) , asymptomatic ( n = 19 ) and symptomatic L . infantum-infected dogs ( n = 15 ) were used . All animals were considered symptomatic when three or more of the following symptoms were present: loss of weight , alopecia , adenopathy , onychogryphosis , hepatomegaly , conjunctivitis and exfoliate dermatitis on the nose , tail and ear tips; and asymptomatic when they were free from clinical symptoms . In the infected animals , the diagnosis of the disease was defined when amastigotes were seen in Giemsa stained smears of bone marrow aspirates or promastigotes were identified on culture of peripheral blood or bone marrow aspirates . Sera were considered positive when tested by indirect immunofluorescence . A titration curve was performed to determine the best protein concentration and antibody dilution to perform ELISA . Plates ( Falcon ) were sensitized with rLiHyp1 ( 1 . 0 µg/well ) or SLA ( 0 . 5 µg/well ) for 18 h at 4°C . Free binding sites were blocked with a PBS-Tween 20 0 . 05% ( PBST ) and 5% casein solution for 2 h at 37°C . After five washes with PBST , plates were incubated with 100 µL of canine sera for 1 h at 37°C . Serum samples were diluted 1∶200 in PBST and 0 . 5% casein . After , plates were washed seven times with PBST and incubated with 1∶10 . 000 anti-dog IgG antibody ( Sigma , St . Louis , USA ) horseradish peroxidase conjugated . The reaction was developed through incubation with H2O2 , along with orto-phenylenediamine and citrate-phosphate buffer pH 5 . 0 , for 30 min in the dark , and was stopped by adding H2O2 2 N . Optical densities were read at 492 nm in an ELISA microplates spectrophotometer ( Molecular Devices , Spectra Max Plus , Concord , Canada ) . For immunoblotting experiments , the recombinant protein was submitted to a 10% SDS-PAGE and blotted onto a nitrocellulose membrane ( 0 . 2 µm pore size , Sigma , St . Louis , USA ) . Membranes were blocked with PBST and 5% casein solution , and were incubated for 2 h at 37°C before the first incubation with a pool of sera samples of asymptomatic CVL , diluted 1∶100 in PBST . Peroxidase conjugated anti-dog IgG ( 1∶5 . 000 ) was used as a second antibody ( Sigma ) . Reactions were revealed by adding chloronaphtol , diaminobenzidine , and H2O2 . Mice ( n = 8 , per group ) were vaccinated subcutaneously in their left hind footpad with 25 µg of rLiHyp1 associated with 25 µg of saponin ( Quillaja saponaria bark saponin , Sigma ) , with adjuvant or only diluent ( PBS ) . Three doses were administered at 2-week intervals . Four weeks after the final immunization , animals ( n = 4 , per group ) were euthanized for the analysis of the immune response elicited by vaccination . At the same time , the remaining animals were infected subcutaneously in the right hind footpad , with virulent 1×107 stationary-phase promastigotes of L . infantum , when 10 weeks after the animals were euthanized , and the liver , spleen , bone marrow ( BM ) , and the paws' draining lymph nodes ( dLN ) were collected to determine parasite burden and evaluation of the immune response . The liver , spleen , BM , and dLN were collected for parasite quantification , following a limiting-dilution protocol [18] . Briefly , the organs were weighed and homogenized using a glass tissue grinder in sterile PBS . Tissue debris was removed by centrifugation at 150× g , and cells were concentrated by centrifugation at 2000× g . Pellets were resuspended in 1 mL of Schneider's insect medium supplemented with 20% FBS . Two hundred and twenty microliters were plated onto 96-well flat-bottom microtiter plates ( Nunc , Nunclon® , Roskilde , Denmark ) and diluted in log-fold serial dilutions in supplemented Schneider's medium with a 10−1 to 10−20 dilution . Each sample was plated in triplicate and read 7 days after the beginning of the culture at 24°C . Pipette tips were discarded after each dilution to avoid carrying adhered parasites from one well to another . Results are expressed as the negative log of the titer ( i . e . , the dilution corresponding to the last positive well ) adjusted per microgram of tissue . Splenocyte cultures and cytokine assays were performed before infection and at 10th week after challenge , as described [17] . Briefly , single-cell preparations from spleen tissue were plated in duplicate in 24-well plates ( Nunc ) at 5×106 cells per mL . Cells were incubated in DMEM medium ( non-stimulated , background control ) , or separately stimulated with SLA ( 25 µg mL−1 ) or rLiHyp1 ( 20 µg mL−1 ) , at 37°C in 5% CO2 for 48 h . IFN-γ , IL-4 , IL-10 , IL-12 , and GM-CSF levels were assessed in the supernatants by a sandwich ELISA provided in commercial kits ( BD OptEIA TM set mouse IFN-γ ( AN-18 ) , IL-12 and GM-CSF; Pharmingen , San Diego , CA , USA; and Murine IL-4 and IL-10 ELISA development kits; PeproTech® , São Paulo , Brazil ) ; following manufacturer's instructions . In order to block IL-12 , CD4 , and CD8 mediated T cell cytokine release , spleen cells of mice vaccinated with rLiHyp1 plus saponin and challenged with L . infantum were in vitro stimulated with SLA ( 25 µg mL−1 ) , and incubated in the presence of 5 µg mL−1 of monoclonal antibodies ( mAb ) against mouse IL-12 ( C17 . 8 ) , CD4 ( GK 1 . 5 ) , or CD8 ( 53-6 . 7 ) . Appropriate isotype-matched controls – rat IgG2a ( R35-95 ) and rat IgG2b ( 95-1 ) – were employed in the assays . Antibodies ( no azide/low endotoxin™ ) were purchased from BD ( Pharmingen , San Diego , CA , USA ) . The statistical analysis was made using the GraphPad Prism software ( version 5 . 0 for Windows ) . Statistical analysis with the vaccinated and/or infected mice was performed by one-way analysis of variance ( ANOVA ) , using the Bonferroni's post-test for multiple comparisons of groups . Receiver Operating Characteristic ( ROC ) curves were used to analyze the data obtained using sera samples of dogs . Statistical analysis between CVL and the control groups were performed by one-way ANOVA using Tukey's multiple comparison test . Differences were considered significant when P<0 . 05 . Data of shown in this study are representative of two independent vaccination' experiments , which presented similar results .
In the present study , a putative member of the super-oxygenase family in Leishmania was fused as a recombinant protein to an N-terminal 6× histidine-tag and expressed in E . coli . The recombinant protein ( rLiHyp1 ) was purified by nickel affinity chromatography ( Fig . 1A ) , and tested for serodiagnosis of CVL . Initially , a pool of sera of asymptomatic dogs was able to recognize the rLiHyp1 by immunoblotting analysis , as seen in Fig . 1B . Sera were individually tested in ELISA against rLiHyp1 , and the results indicated that all sera samples of symptomatic dogs , and 18 out of 19 samples of asymptomatic CVL animals were able to recognize the recombinant protein . In contrast , antibodies from T . cruzi-infected , Leish-Tec® vaccinated or healthy dogs did not react with the rLiHyp1 protein ( Fig . 1C ) . To determine the diagnostic performance of rLiHyp1 for CVL , Receiver-Operating Characteristic ( ROC ) curves were constructed to determine area under curve ( AUC ) and sensitivity and specificity values in the experiments . In the results , it was observed that the performance of rLiHyp1 proved to be highly effective in order to identify sera samples of symptomatic and asymptomatic CVL , and also to differentiate them in relation to the other sera samples employed in this study ( Fig . 1D and 1E , respectively ) . The immunogenicity of the rLiHyp1 was evaluated in BALB/c mice , 4 weeks after the last vaccine dose . Following in vitro stimulation with rLiHyp1 , spleen cells from vaccinated mice significantly produced higher levels of IFN-γ , IL-12 , and GM-CSF than those secreted by spleen cells from control mice ( saline and saponin groups ) . No increase in IL-4 and IL-10 production could be observed in any experimental group , after stimulation with rLiHyp1 ( Fig . 2A ) . The ratio between IFN-γ/IL-4 and IFN-γ/IL-10 levels; as well as between IL-12/IL-4 and IL-12/IL-10 levels showed that vaccinated animals presented an elevated Th1 immune response after rLiHyp1-stimulus ( Fig . 2B and 2C , respectively ) . In addition , mice vaccinated with rLiHyp1 plus saponin presented an rLiHyp1-specific humoral response , with the predominance of IgG2a isotype ( Fig . 2D ) . Next , the present study analyzed whether the immunization with the rLiHyp1 plus saponin was able to induce protection against L . infantum . The infection was followed up over a 10-weeks period , when the parasite burden in the liver , spleen , BM , and dLN was determined . Significant reductions in the number of parasites were observed in the different evaluated organs of vaccinated mice , as compared with those that received only saline or saponin ( Fig . 3 ) . In this context , vaccinated mice with rLiHyp1 plus saponin showed significant reductions in the parasite load in liver ( 3 . 8- and 3 . 3-log reductions , Fig . 3A ) , spleen ( 3 . 7- and 3 . 5-log reductions , Fig . 3B ) , BM ( 3 . 0- and 3 . 0-log reductions , Fig . 3C ) , and dLN ( 3 . 9- and 3 . 6-log reductions , Fig . 3D ) , in comparison to the saline and saponin groups , respectively . Attempting to determine the influence of immunization with rLiHyp1 plus saponin on the L . infantum specific killing effectors functions in the spleen of infected mice , nitrite was assayed as an indicator of nitric oxide ( NO ) production in spleen cells . The nitrite production was significantly higher in mice vaccinated with rLiHyp1 plus saponin after stimulation with SLA , as compared to the control groups that produced minimum amounts of this product ( data not shown ) . The production of cytokines in the supernatants of spleen cells cultures stimulated with rLiHyp1 and SLA after challenge was analyzed to determine the immunological correlates of protection induced by rLiHyp1 . The spleen cells from mice vaccinated with rLiHyp1 plus saponin produced higher levels of SLA-specific IFN-γ , IL-12 and GM-CSF cytokines than those secreted by spleen cells from control groups , 10 weeks after infection ( Fig . 4A ) . In contrast , the SLA-driven production of IL-4 and IL-10 showed that vaccination with rLiHyp1 plus saponin induced no production of these cytokines in the vaccinated and infected animals . The contribution of CD4 and CD8 T cells and the dependence of IL-12 production for the SLA-specific IFN-γ response from the spleen cells of mice immunized with rLiHyp1 plus saponin and challenged with L . infantum were evaluated . The IFN-γ production was completely suppressed using anti-IL-12 or anti-CD4 monoclonal antibodies in the spleen cells cultures ( Fig . 4B ) . The addition of anti-CD8 antibodies to the cultures also decreased the production of IFN-γ , as compared to the cell cultures without treatment ( 1 . 881±139 pg/mL before , and 1 . 533±110 pg/mL after including anti-CD8 antibodies ) ; however , the production of this cytokine proved to be higher than that produced by the use of the anti-CD4 monoclonal antibody . As observed before challenge , the ratio between IFN-γ/IL-4 and IFN-γ/IL-10 , and between IL-12/IL-4 and IL-12/IL-10 indicated that vaccinated mice developed a specific Th1 immune response , which was maintained after infection in these animals ( Fig . 4C and 4D , respectively ) . In this study , very low levels of anti-SLA antibodies could be observed in the sera of all mice groups challenged with L . infantum . However , it was possible to detect that vaccinated and infected mice presented SLA-specific IgG2a antibodies that were significantly higher than the obtained IgG1 levels ( Fig . 4E ) .
Different Leishmania proteins with antigenic properties were recently identified by an immunoproteomic approach applied to L . infantum promastigotes and amastigote-like proteic extracts [18] , including hypothetical proteins of the parasites . The fact that antibodies present in the sera of infected dogs recognized these hypothetical proteins indicates that they are expressed by parasites during active infection , and are antigenic to the host's immune system . In this context , the DNA encoding one of these Leishmania hypothetical proteins , which was specifically recognized by antibodies in the amastigote-like antigenic extracts , was cloned and expressed in E . coli and tested for its antigenicity and prophylactic properties . Immunoblotting and ELISA analyses demonstrated that the recombinant LiHyp1 protein ( rLiHyp1 ) was specifically recognized by antibodies present in the sera of dogs with symptomatic and asymptomatic VL , yet it presented no cross-reactivity with the sera of dogs vaccinated with a Brazilian recombinant vaccine , Leish-Tec® , or with animals experimentally infected with T . cruzi , demonstrating , besides antigenicity its potential for improvement of CVL serodiagnosis . Dogs are also reservoirs for T . cruzi parasites in endemic areas for VL transmission in Brazil , and seroprevalences of anti-T . cruzi antibodies , which may cross react with Leishmania antigens , of 21 . 9% and up to 57 . 0% have been reported [19] , [20] . In a previous study , it was demonstrated that sera from dogs naturally infected with L . infantum displayed reactivity with Leishmania ribosomal proteins ( LRP ) through Western-Blot analysis . A comparison between LRP and SLA showed that LRP had a similar sensitivity in ELISA , but higher specificity than the SLA-based assays in the diagnosis of CVL [21] . However , the technical purification of LRP is complex and labor-intensive . In contrast , the production of recombinant proteins is less complex and allows obtain large amounts of proteins , when compared to production of semi-purified extracts of the parasites ( like LRP ) . In this context , the use of the rLiHyp1 protein in the serodiagnosis of CVL is attractive . Amastigote antigens have been far less tested as vaccine candidates against VL [15] . Therefore , a vaccine that is able to elicit immune responses against intracellular amastigotes of Leishmania may present advantages not only for prophylactic , but also for therapeutic vaccines . In this context , the immunization with rLiHyp1 plus saponin was able to induce a predominant Th1 immune response , which was characterized by an in vitro rLiHyp1-specific production of IFN-γ , IL-12 and GM-CSF , combined with the presence of very low levels of IL-4 and IL-10 . After infection , mice immunized with rLiHyp1 plus saponin , when compared to control groups , displayed significant reductions of the number of parasites in all evaluated organs ( liver , spleen , BM , and dLN ) , which correlated a specific rLiHyp1- and SLA-dependent IFN-γ production in the spleen , one of the main cytokines implicated in the acquired immunity against infection with Leishmania [22] , [23] , [24] . The CD4 T cells proved to be the major source of IFN-γ in the protected mice , since depletion of these cells in cultures of spleen cells stimulated with SLA significantly abrogated this response . Similarly , in the vaccinated mice , IFN-γ production proved to be IL-12-dependent . Although previous reports have shown that the activation of both CD4 and CD8 T cells subsets may be important for the killing of parasites in mice vaccinated with different parasite recombinant antigens [25] , [26] , the present study's data suggest that CD8 T cells may contribute in a less extension to the induction of IFN-γ mediated response elicited by the rLiHyp1 plus saponin formulation . Besides production of IFN-γ , these cells may contribute to infection control by their direct cytotoxic effect on infected cells , as previously demonstrated in other experimental conditions [24] . Altogether , the present study indicates that immunization with rLiHyp1 plus saponin primed BALB/c mice for an rLiHyp1-specific Th1 response that was sustained after L . infantum infection challenge . The present study also showed that the protection in BALB/c mice against L . infantum is associated with a significant decrease in the production of macrophage deactivating cytokines , like IL-4 and IL-10 . Very low levels of Leishmania-specific IL-10 were detected after the stimulation of spleen cells from vaccinated mice , 10 weeks after infection . In contrast , spleen cells from both control mice groups showed a significantly higher production of this cytokine . Indeed , control of the parasite-mediated IL-10 response in vaccinated mice may be critical for protection , since this cytokine is considered to be the most important factor for VL progression after infection with viscerotropic Leishmania species in IL-10 deficient mice [12] , [27] , [28] , or in mice treated with an anti-IL-10 receptor antibody [29] . In BALB/c mice , the IL-4-dependent production of IgG1 antibodies is associated with disease progression due to some Leishmania species , including L . amazonensis [30] , but it is not confirmed in L . infantum or L . donovani [31] , [32] . Nonetheless , in BALB/c mice vaccinated with recombinant A2 protein or LRP plus saponin , the protection against cutaneous or visceral leishmaniasis have been also correlated with a decrease in Leishmania-specific IL-4 and IL-10 mediated response [12] , [17] , [22] , [33] . Spleen cells from vaccinated mice , as compared to the control groups , produced higher levels of rLiHyp1- and SLA-specific GM-CSF , a cytokine related with macrophage activation and resistance in murine models against different intracellular pathogens , including L . major [34] , L . donovani [35] , and L . chagasi ( = L . infantum ) [12] . It has also been shown that the immunization of humans with a crude Leishmania antigenic preparation using this cytokine as an adjuvant commonly induces a parasite-specific Th1 response [36] , and that the administration of a therapeutic vaccine containing some Leishmania antigens plus GM-CSF could be correlated with the cure of lesions in the muco-cutaneous leishmaniasis [37] . A critical aspect for Leishmania vaccines development refers to the pre-clinical model chosen for initial screening of vaccine candidates . Although sand fly transmitted infection in hamsters more closely resemble the natural transmission and the human disease , this infection model requires specific laboratory conditions and trained personnel staff , which are not widely available , hindering its general use as a first step for testing vaccine efficacy against VL [38] . In contrast , BALB/c mice infected with L . donovani or L . infantum is one the most widely studied murine models for VL , and is therefore naturally selected over other models for this purpose [17] , [39] , [40] , [41] . Murine models have also allowed the characterization of the immune mechanisms required to develop organ-specific immune response against Leishmania [41] , [42] . Therefore , the evaluation of the parasite burden in different organs is an important marker of vaccine efficacy against VL in these models . In a recent study , it was demonstrated that the subcutaneous route of inoculation of L . infantum in BALB/c mice induces a faster infection development in the animals and higher parasite burden in different tissues as compared to the intravenous challenge [41] . In this context , the subcutaneous route was selected to evaluate the efficacy of rLiHyp1 plus saponin vaccine against L . infantum . In addition , in comparative studies , it was found that protection afforded by vaccination might be improved in animals challenged by intradermal/subcutaneous route as compared to those receiving an intravenous challenge [43] , [44] . Nevertheless , additional studies may well be carried out in order to extend the observations present herein of the protective efficacy of rLiHyp1 plus saponin vaccination to other infection models and experimental conditions . In conclusion , the present study's data indicated that a Leishmania amastigote-specific protein , member of the super-oxygenase family , LiHyp1 , is antigenic in the CVL , and also conferred protection in BALB/c mice against L . infantum . Protection correlated with the CD4 T cells response characterized by high IFN-γ , IL-12 , and GM-CSF , and low IL-4 and IL-10 levels . Therefore , the LiHyp1 protein constitutes a new and promising antigen candidate for serodiagnosis and vaccine development against VL . | Life-long immunity to leishmaniasis in recovered patients has inspired the development of vaccines against disease . The present study aimed to evaluate a non-described hypothetical Leishmania amastigote-specific protein , identified by an immunoproteomic approach in L . infantum , attempting to select a new candidate antigen for specific serodiagnosis and a vaccine against visceral leishmaniasis ( VL ) . The recombinant protein ( rLiHyp1 ) was recognized by antibodies from sera of asymptomatic and symptomatic canine visceral leishmaniasis ( CVL ) , but presented no cross-reactivity with sera of vaccinated dogs , those with Chagas' disease or healthy animals . In addition , the rLiHyp1 plus saponin was able to induce a Th1 response , which was based on the production of high levels of IFN-γ , IL-12 , and GM-CSF after in vitro stimulation in BALB/c mice . The protective efficacy of rLiHyp1 plus saponin was evaluated in mice challenged with L . infantum promastigotes . Challenged and vaccinated mice showed significant reductions in the number of parasites in all evaluated organs , and the protection was associated with a Th1-type response . Therefore , the present study reveals a new potential candidate for the improvement of serodiagnosis of CVL , as well as an effective vaccine candidate against VL . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"veterinary",
"immunology",
"veterinary",
"diseases",
"veterinary",
"diagnostics",
"zoonotic",
"diseases",
"leishmaniasis",
"parasitic",
"diseases",
"veterinary",
"science",
"veterinary",
"medicine"
] | 2013 | Antigenicity and Protective Efficacy of a Leishmania Amastigote-specific Protein, Member of the Super-oxygenase Family, against Visceral Leishmaniasis |
Septic pneumonias resulting from bacterial infections of the lung are a leading cause of human death worldwide . Little is known about the capacity of CD8 T cell-mediated immunity to combat these infections and the types of effector functions that may be most effective . Pneumonic plague is an acutely lethal septic pneumonia caused by the Gram-negative bacterium Yersinia pestis . We recently identified a dominant and protective Y . pestis antigen , YopE69–77 , recognized by CD8 T cells in C57BL/6 mice . Here , we use gene-deficient mice , Ab-mediated depletion , cell transfers , and bone marrow chimeric mice to investigate the effector functions of YopE69–77-specific CD8 T cells and their relative contributions during pulmonary Y . pestis infection . We demonstrate that YopE69–77-specific CD8 T cells exhibit perforin-dependent cytotoxicity in vivo; however , perforin is dispensable for YopE69–77-mediated protection . In contrast , YopE69–77-mediated protection is severely impaired when production of TNFα and IFNγ by CD8 T cells is simultaneously ablated . Interestingly , TNFα is absolutely required at the time of challenge infection and can be provided by either T cells or non-T cells , whereas IFNγ provided by T cells prior to challenge appears to facilitate the differentiation of optimally protective CD8 T cells . We conclude that cytokine production , not cytotoxicity , is essential for CD8 T cell-mediated control of pulmonary Y . pestis infection and we suggest that assays detecting Ag-specific TNFα production in addition to antibody titers may be useful correlates of vaccine efficacy against plague and other acutely lethal septic bacterial pneumonias .
Plague , one of the world's most deadly infectious diseases , has killed hundreds of millions of humans during three major pandemics [1] . It is caused by the Gram-negative facultative intracellular bacterium Yersinia pestis , which is naturally maintained in rodent reservoirs . Fleas transmit Y . pestis between rodents and to other mammals . Human infections typically result from fleabites as well , but a pneumonic form of plague can spread from human to human via infectious respiratory droplets . Pneumonic plague is fulminant and nearly always fatal unless treated with antibiotics within 24 h of symptom onset . Although natural outbreaks of pneumonic plague are uncommon , the high mortality rate , small window for treatment , existence of antibiotics-resistant strains , and potential for use as an airborne biological weapon fosters research aimed at the development of effective countermeasures . Mouse models of pulmonary Y . pestis infection are considered translational tools for the development of pneumonic plague countermeasures because the pathology of plague in rodents is highly similar to that observed in humans . Analogous septic pneumonias caused by more common bacteria , including members of the Klebsiella , Streptococcus , and Staphylococcus species , are leading causes of death worldwide [2] , [3] . Thus , murine models of plague also provide tools for studying basic mechanisms of immune defense against acutely lethal bacterial infections that seed the human lung and then disseminate to cause septic morbidity . Ab-based subunit vaccines composed of the Y . pestis F1 and LcrV proteins provide rodents and some nonhuman primates with substantial protection against pulmonary Y . pestis infection [4] . Despite inducing high titer Ab responses , these vaccines fail to induce adequate protection in all nonhuman primates , most notably in African green monkeys [4] , [5] , [6] . This observation raises the possibility that Abs may not suffice to protect humans against pneumonic plague . Recent studies indicate T cells also contribute to protection against pulmonary Y . pestis infection in mice and the cytokines TNFα , IFNγ and IL-17 are required for optimal T cell-mediated protection [7] , [8] . For example , B cell-deficient mice vaccinated with live attenuated Y . pestis are protected against lethal challenge , and depleting T cells or neutralizing TNFα and IFNγ at the time of challenge fully abolishes the protection [7] . TNFα and IFNγ also contribute to Ab-mediated protection in wild-type mice: the passive protection conferred by therapeutic administration of F1 and LcrV-specific mAb and the active protection conferred by immunization with a recombinant F1/LcrV vaccine are both abolished by neutralization of TNFα and IFNγ [9] , [10] . Together , these findings suggest that pneumonic plague vaccines should also aim to induce cellular immunity that produces cytokines , in addition to inducing Ab-mediated humoral immunity . CD8 T cells are critical for defense against a variety of pathogens , including viruses , protozoa and bacteria [11] , [12] . The effector functions used by CD8 T cells to resist pathogens include secretion of cytokines like TNFα and IFNγ and Ag-specific cytolysis of infected cells [11] , [12] . Recently we identified a dominant and protective T cell epitope recognized by Y . pestis-specific CD8 T cells [13] . Immunizing wild-type C57BL/6 mice with this single peptide , YopE69–77 , primes Ag-specific CD8 T cells that suffice to confer protection against lethal pulmonary Y . pestis challenge [13] . Moreover , immunizing mice with YopE69–77 also elicits a CD8 T cell response that protects against lethal intragastric challenge with Yersinia pseudotuberculosis , the enteropathogen from which Y . pestis evolved [14] . A prior study showed that CD8 T cells and perforin , a key molecule for cytolysis , are required to protect naïve mice against attenuated Y . pseudotuberculosis infection [15] . However , the relative contributions of cytokines and cytolysis to CD8 T cell-mediated anti-Yersinia immunity in vivo have never been reported . Furthermore , it is not yet clear whether T cells or other cell types produce the TNFα and IFNγ required for effective T cell-mediated defense against plague . In this study , we examined the effector functions of YopE69–77-specific CD8 T cells during pulmonary Y . pestis infection , and investigated their relative contributions to protection mediated by YopE69–77 immunization . Using a combination of gene-deficient mice , Ab-mediated depletion , cell transfers , and bone marrow chimeric mice , we demonstrate that YopE69–77-specific CD8 T cells exhibit perforin-dependent cytotoxicity in vivo , but perforin is dispensable for YopE69–77-specific CD8 T cell-mediated protection . In contrast , TNFα and IFNγ are critical for protection and YopE69–77-mediated protection is severely impaired when production of TNFα and IFNγ by CD8 T cells is simultaneously suppressed .
Prior studies established that intranasal immunization with YopE69–77 , a dominant CD8 T cell epitope of the Y . pestis YopE protein , confers C57BL/6 mice with CD8 T cell-mediated protection against pulmonary Y . pestis infection [13]; however , the mechanism of protection has yet to be defined . To investigate the relative contributions of cytokines and cytolysis to YopE69–77-specific CD8 T cell-mediated protection , we first examined the protective efficacy of YopE69–77 immunization . Wild-type C57BL/6 mice were immunized intranasally with adjuvant cholera toxin ( CT ) alone or adjuvant with 1 µg of YopE69–77 and then challenged intranasally with Y . pestis . As previously described [13] , immunization with YopE69–77 conferred protection against 20 median lethal dose ( MLD ) of Y . pestis strain D27 , with 80% YopE69–77-immunized mice surviving the lethal challenge ( Figure 1A ) . Immunization with YopE69–77 also conferred significant protection against a higher dose ( 200 MLD ) challenge of Y . pestis strain D27 ( Figure 1B ) , with 67% mice surviving . Moreover , immunization with YopE69–77 even sufficed to confer modest yet significant protection against intranasal challenge with fully virulent pigmentation locus-positive Y . pestis strain CO92 ( Figure 1C ) . Although 75% of YopE69–77-immunized mice ultimately succumbed to the fully virulent challenge , protection was evidenced by both a 1 . 5-day prolongation in median survival time and an increase in the overall survival rate ( Figure 1C ) . These data indicate that YopE69–77 immunization confers significant CD8 T cell-mediated protection against lethal pulmonary challenge with multiple Y . pestis strains . To investigate the mechanism of CD8 T cell-mediated defense in vivo , we focused on the model employing 20 MLD of Y . pestis strain D27 challenge , as this model showed the greatest degree of protection . To test whether immunization with YopE elicits a CTL response , we measured the ability of YopE67–77-specific CD8 T cells to lyse peptide-pulsed autologous primary lymphocytes using a standard in vivo CTL assay [16] . Naïve splenocyte target cells from CD45 . 1 congenic C57BL/6 mice were pulsed with YopE69–77 peptide and labeled with a high concentration of CFSE . Control target cells were pulsed with OVA257–264 peptide and labeled with a low concentration of CFSE . The pulsed cells were then mixed at a one-to-one ratio and injected intravenously into wild-type C57BL/6 mice that had been previously immunized . Approximately 20 h later , splenocytes were harvested and the proportion of transferred target cells labeled with high or low levels of CFSE was determined by flow cytometry . The proportion of cells recovered from naïve C57BL/6 mice was used as the comparator ( Figure 2A , a ) . As expected , near equal proportions of both target cell populations were recovered from mice immunized with CT adjuvant alone , indicating that no specific lysis was taking place ( Figure 2A , b ) . Cells harvested from YopE69–77-immunized mice had lower proportions of CFSEhigh target cells , indicating that the YopE69–77-pulsed target cells had been lysed in vivo ( Figure 2A , c and d ) . In contrast , cells harvested from OVA257–264-immunized mice had lower proportions of CFSElow target cells , indicating that the OVA257–264-pulsed target cells were killed ( Figure 2A , e and f ) . Mice immunized with 10 µg of peptide ( Figure 2A , d and f ) exhibited stronger in vivo cytotoxic activity than mice immunized with 1 µg of peptide ( Figure 2A , c and e ) . Together , these data suggest that immunization with YopE69–77 primes T cells that have the ability to specifically kill YopE69–77-pulsed target cells in vivo . Perforin can play a critical role in CD8 T cell-mediated cytotoxicity , and perforin-deficient mice display increased susceptibility to many viral , bacterial and protozoan infections [11] , [12] , [17] . To investigate whether perforin contributes to YopE69–77-specific CD8 T cell-mediated cytotoxicity , Ag-pulsed target cells were transferred into immunized perforin-deficient mice and the CTL assay was performed . The YopE69–77- and OVA257–264-immunized perforin-deficient mice showed significantly reduced cytotoxic activity against YopE69–77- and OVA257–264-pulsed target cells , respectively , in comparison with immunized wild-type mice ( Figures 2B and 2C ) . The reduced cytotoxicity could not be explained by reduced T cell priming , as the peripheral blood of the immunized wild-type and perforin-deficient mice harbored comparable percentages of YopE69–77-specific ( Figure 2D ) or OVA257–264-specific ( Figure 2E ) CD8 T cells . These data demonstrate that YopE69–77-specific CD8 T cells have the ability to kill YopE69–77-pulsed target cells in vivo in a perforin-dependent manner . We next investigated whether perforin is required for protection against lethal Y . pestis challenge . Wild-type or perforin-deficient mice were immunized with YopE69–77 and then challenged intranasally with 20 MLD of Y . pestis strain D27 . As shown in Figure 3A , most control mice immunized with adjuvant alone or with control peptide OVA257–264 succumbed to Y . pestis challenge , whereas most wild-type and perforin-deficient mice immunized with YopE69–77 survived . Similar results were observed when mice were immunized with either 1 µg or 10 µg YopE69–77 ( Figure 3A zand data not shown ) . Measurements of bacterial burden in lung and liver tissues at day 4 after Y . pestis challenge showed significantly decreased numbers of CFU in both the YopE69–77-immunized wild-type and perforin-deficient mice , as compared with control mice immunized with adjuvant alone or with OVA257–264 ( Figure 3B ) . Neither the survival data nor the bacterial burden data revealed a significant role for perforin in the YopE69–77-specific CD8 T cell-mediated protection against Y . pestis . Therefore , although the YopE69–77-specific CD8 T cells exhibit perforin-dependent cytotoxic activity in vivo ( Figure 2 ) , perforin is dispensable for protection against lethal Y . pestis challenge . We previously demonstrated that immunization with YopE69–77 also protects against lethal intragastric challenge of mice with Y . pseudotuberculosis [14] . Other investigators reported that naïve perforin-deficient mice display increased susceptibility to intravenous challenge with an attenuated Y . pseudotuberculosis strain [15] . To investigate whether perforin is required for YopE69–77-specific CD8 T cell-mediated protection against Y . pseudotuberculosis , we immunized wild-type and perforin-deficient mice and challenged intragastrically or intravenously with 10 MLD of virulent Y . pseudotuberculosis strain 32777 . We observed that both wild-type and perforin-deficient mice immunized with YopE69–77 were specifically protected against intragastric and intravenous Y . pseudotuberculosis challenge ( Figures 3C and 3D , respectively ) . These data indicate that perforin is also dispensable for YopE69–77-specific CD8 T cell-mediated protection against Y . pseudotuberculosis . To investigate roles for cytokines in CD8 T cell-mediated protection against Y . pestis , we immunized TNFα- or IFNγ-deficient mice with YopE69–77 and then challenged with 20 MLD of Y . pestis strain D27 . While most YopE69–77-immunized wild-type mice survived the challenge , almost all YopE69–77-immunized TNFα-deficient mice and IFNγ-deficient mice succumbed ( Figure 4A and 4B ) . Notably , the wild-type and IFNγ-deficient mice generated comparable frequencies of YopE69–77-specific CD8 T cells after immunization , and despite their impaired survival , the TNFα-deficient mice consistently generated higher frequencies of specific cells ( data not shown ) , implying that cytokine deficiency does not quantitatively impair the expansion and/or persistence of YopE69–77-specific CD8 T cells . These results suggest that TNFα and IFNγ play critical roles during YopE69–77-specific CD8 T cell-mediated protection against pulmonary Y . pestis infection . To assess whether protection mediated by YopE69–77-specific CD8 T cells requires cytokines at the time of challenge , YopE69–77-immunized wild-type mice were treated with neutralizing mAb specific for TNFα or IFNγ . Control mice were treated with an isotype-matched mAb of irrelevant specificity . As shown in Figure 4C , while mice treated with mAb to neutralize IFNγ at the time of challenge displayed decreased but still significant protection ( p<0 . 01 ) , nearly all mice treated with mAb to neutralize TNFα succumbed . These results suggest that production of TNFα is particularly critical at the time of pulmonary Y . pestis challenge . A variety of cell types can produce TNFα during the immune response to infection . To assess whether a specific cell type is responsible for producing the critical TNFα during CD8 T cell-mediated defense against Y . pestis , we performed studies with previously described mice possessing cell-type-specific deficiencies in TNFα expression [18] . Control mice and mice deficient for TNFα expression in either the macrophage/neutrophil lineage ( MN-TNF KO ) or the T cell lineage ( T-TNF KO ) were immunized with YopE69–77 . Immunized TNF-floxed littermate control mice largely survived challenge with 20 MLD of Y . pestis strain D27 ( Figure 5 ) . Unexpectedly , both the MN-TNF KO mice and the T-TNF KO mice were also protected by YopE69–77 immunization , with 90% and 80% surviving the lethal challenge , respectively ( Figures 5A and 5B , respectively ) . These data suggest that TNFα derived from either macrophages/neutrophils or T cells can suffice to confer YopE69–77-mediated protection against pulmonary Y . pestis challenge . To definitively assess whether CD8 T cells are required to produce TNFα or IFNγ , we next assayed protection conferred by primed CD8 T cells isolated from YopE69–77-immunized TNFα- or IFNγ-deficient mice . Specifically , wild-type , TNFα-deficient , or IFNγ-deficient mice were immunized with YopE69–77 and their CD8+ splenic T cells were purified by magnetic beads and injected intravenously into naïve wild-type recipient mice , which were then challenged with 20 MLD of Y . pestis strain D27 . The mice that received CD8+ T cells from YopE69–77-immunized wild-type mice showed significantly improved survival as compared with mice that received CD8+ T cells from control adjuvant-immunized mice ( p<0 . 0001 ) , with 43% of the mice surviving ( Figure 6A ) . Mice that received CD8+ T cells from YopE69–77-immunized TNFα- or IFNγ-deficient mice displayed more modest but significant protection ( both p<0 . 05 ) , with 29% and 27% surviving the challenge , respectively . Thus , YopE69–77-specific CD8 T cells lacking the capacity to produce either TNFα or IFNγ can confer significant protection against Y . pestis . In our experience , the transfer of primed T cells followed shortly thereafter by an infectious challenge can sometimes provide non-specific protection ( data not shown ) , perhaps owing to the potential of effector and memory CD8 T cells to rapidly secrete IFNγ in response to IL-12 and IL-18 in the absence of cognate antigen [19] . In fact , the mice that received CD8+ T cells from OVA257–264-immunized wild-type mice displayed some protection , with 24% surviving the challenge ( Figure 6A ) . Moreover , the protection in this primed T cell transfer model was relatively weak , as compared with our prior studies of intact mice , thus making it difficult to study the mechanism . Additionally , these YopE69–77-specific CD8 T cells were primed in an environment where cytokine was deficient . In an attempt to overcome these possible caveats to the experiments shown in Figure 6A , we next developed a model to study cytokine-deficient T cells that were primed in situ . First , we isolated naïve splenocytes from wild-type or gene-deficient mice and transferred them to TCRβδ-deficient mice , which lack T cells , thus creating chimeric mice in which primed T cells could only arise from cytokine-deficient donor splenocytes . Then , we immunized and challenged these splenocyte chimeras . The chimeras that received wild-type splenocytes showed significant YopE-specific protection ( p<0 . 0001 ) ; 70% of the YopE69–77-immunized mice survived and only 7% of the control OVA257–264-immunized mice survived ( Figure 6B ) . All the YopE69–77-immunized mice that received splenocytes from perforin-deficient mice also survived , further confirming that perforin is dispensable for protection against Y . pestis mediated by primed CD8 T cells ( Figure 6B ) . YopE69–77-immunized mice that received splenocytes from TNFα-deficient or IFNγ-deficient mice also displayed significant protection ( both p<0 . 0001 ) , exhibiting 56% and 70% survival , respectively . Thus , mouse models employing either the transfer of primed CD8+ T cells ( Figure 6A ) or the priming of CD8+ T cells in situ ( Figure 6B ) demonstrate that YopE69–77-specific CD8 T cells lacking the capacity to produce either TNFα or IFNγ can confer significant protection against Y . pestis . The observation that YopE69–77-specific CD8 T cells lacking either TNFα or IFNγ exhibit slightly impaired protection suggested that the TNFα and IFNγ produced by CD8 T cells may have complementary roles . To explore this possibility , we generated TNFα and IFNγ double-deficient ( TNFα/IFNγ-deficient ) mice . We observed that T cell-deficient mice that received splenocytes from TNFα/IFNγ-deficient mice were poorly protected by YopE69–77 immunization ( Figure 6B ) , with only 18% surviving lethal Y . pestis challenge . The observation that further removing the remaining cytokine from either the TNFα- or IFNγ-deficient mice leads to a significant and substantial decrease in survival suggests complementary roles for TNFα and IFNγ produced by CD8 T cells . In the splenocyte chimera model shown in Figure 6B , the cytokine-deficient splenic T cells had developed in the thymuses of cytokine-deficient mice , perhaps imprinting them with altered functions . To overcome this potential concern , a final set of studies was performed using mixed bone marrow chimeras . Lethally irradiated TCRβδ-deficient mice were reconstituted with a mixture of bone marrow cells isolated from gene-deficient or wild-type mice . In each case , 75% of the bone marrow cells came from TCRβδ-deficient mice , thereby enabling reconstitution of wild-type elements of all leukocyte populations except for T cells . The remaining 25% of the bone marrow cells came from wild-type mice or gene-deficient mice , thereby enabling the development of wild-type or gene-deficient T cells , respectively . Six weeks after reconstitution , the mice were immunized and then challenged with 20 MLD of Y . pestis strain D27 . As shown in Figure 7A , 88% of the YopE69–77-immunized mice reconstituted with wild-type T cells were protected , while all of the OVA257–264-immunized mice reconstituted with wild-type T cells succumbed . Likewise , all YopE69–77-immunized mice reconstituted without T cells ( i . e . mice that received only TCRβδ-deficient bone marrow ) succumbed to challenge infection . Consistent with Figures 6 , the YopE69–77-immunized chimeric mice reconstituted with TNFα- , IFNγ- or perforin-deficient T cells all showed significantly improved protection when compared with mice lacking T cells ( all p<0 . 0001 ) , indicating that production of TNFα- , IFNγ- or perforin by T cells is not absolutely essential . Indeed , the degree of protection observed in YopE69–77-immunized chimeric mice reconstituted with TNFα- or perforin-deficient T cells did not differ significantly from that observed in mice reconstituted with wild-type T cells ( Figure 7A ) . Although the YopE69–77-immunized chimeric mice reconstituted with IFNγ-deficient T cells displayed significantly increased protection in comparison with mice lacking T cells , the degree of protection was significantly reduced in comparison with mice reconstituted with wild-type T cells ( p<0 . 001 , Figure 7A ) . Notably , survival was further reduced in mice reconstituted with TNFα/IFNγ-deficient T cells ( p<0 . 05 in compared with mice reconstituted with IFNγ-deficient T cells ) , with only 18% of mice surviving the lethal challenge ( Figure 7A ) . It is worth noting that we confirmed the efficiency of bone marrow reconstitution and YopE immunization in mice prior to Y . pestis infection by measuring the frequency of CD4 T cells , CD8 T cells , and YopE69–77-specific CD8 T cells in the peripheral blood . In spite of slightly lower frequency of CD8 T cells in the peripheral blood of mice reconstituted with TNFα- , IFNγ- or TNFα/IFNγ-deficient T cells than that of mice reconstituted with wild-type T cells ( data not shown ) , the frequency of YopE69–77-specific CD8 T cells , as measured by KbYopE69–77 tetramer , was not lower than that of mice reconstituted with wild-type T cells ( Figure 7B ) . In fact , mice reconstituted with TNFα-deficient T cells generated even more YopE69–77-specific CD8 T cells than mice reconstituted with wild-type T cells , even though they exhibited reduced survival ( Figure 7A ) . Subsequently , we generated bone marrow chimeras , immunized and challenged them as described above , and then euthanized mice at day 4 after challenge to measure bacterial burden . This analysis revealed significantly decreased bacterial CFU in lung ( Figure 7C ) and liver ( Figure 7D ) tissues of YopE69–77-immunized chimeric mice reconstituted with wild-type , TNFα- or IFNγ-deficient T cells , as compared with OVA257–264-immunized chimeric mice reconstituted with wild-type T cells . Thus , deficient production of either TNFα or IFNγ by T cells does not impair their ability to control bacteria . In contrast , we observed significantly increased numbers of bacterial CFU in lung and liver tissues of YopE69–77-immunized chimeric mice reconstituted with TNFα/IFNγ-deficient T cells , as compared with YopE69–77-immunized chimeric mice reconstituted with wild-type T cells ( Figure 7C and 7D ) . In fact , the burden of YopE69–77-immunized chimeric mice reconstituted with TNFα/IFNγ-deficient T cells achieved levels approaching those of OVA257–264-immunized chimeric mice reconstituted with either wild-type or TNFα/IFNγ-deficient T cells , which were not protected ( Figure 7A ) .
In this study , we conducted experiments to better understand the mechanisms of immunization-induced protective CD8 T cell responses and their relative contributions to defense against pulmonary Y . pestis infection . We extended our prior work [13] and demonstrated that immunization with YopE69–77 suffices to confer remarkable protection against pigmentation locus-deficient Y . pestis strain D27 ( Figures 1A and 1B ) . YopE69–77-immunized mice can withstand as high as 200 MLD challenge ( Figure 1B ) . Intranasal administration of Y . pestis strain D27 causes mortality in mice but does not cause the fulminant pneumonia characteristic of fully virulent Y . pestis [20] . However , we also found that YopE69–77-immunized mice were significantly protected against intranasal infection with the fully virulent Y . pestis strain CO92 ( Figure 1C ) . To our knowledge , this is the first report that T cell-mediated immunity can protect against fully virulent Y . pestis infection . Notably , the protection was incomplete , suggesting that YopE69–77-specific CD8 T cells alone may be insufficient to combat fully virulent Y . pestis strains . Taking advantage of the robust protection observed in mice infected with 20 MLD Y . pestis strain D27 , we manipulated immune responses to identify effector functions that are critical for CD8 T cell-mediated anti-Yersinia immunity in vivo . Researchers typically use gene-deficient mice , adoptive cell transfer models and Ab-mediated depletion methods to demonstrate roles for different effector functions of cellular immunity during host defense against microbial infections . There are caveats to each approach and determining the relative contribution of each specific effector function by any particular type of immune cell can be technically challenging . First , effector functions usually are not unique to a particular cell type . Both CD8 T cells and NK cells utilize perforin-mediated cell killing , whereas TNFα and/or IFNγ can be produced by CD4 T cells , CD8 T cells , NK cells , and some innate cells such as macrophages and neutrophils . Second , the removal of any particular effector function can inadvertently alter cell differentiation , homeostasis , and/or the immunodominance hierarchies of Ag-specific cells . For example , IFNγ-deficient and perforin-deficient mice exhibit altered expansion , contraction and immunodominance of Ag-specific CD8 T cells during Listeria infection [21] . Third , effector and memory Ag-specific CD8 T cells have the potential to rapidly secrete IFNγ in response to IL-12 and IL-18 in the absence of cognate Ag [19] , hence transferring primed effector/memory Ag-specific cells to mice followed by infection could confer innate protection in an Ag nonspecific manner . In an effort to address these complexities and overcome the limitations of any single methodology , we used multiple approaches to examine the contributions of potential effector mechanisms used by CD8 T cells to combat septic pneumonic plague . YopE69–77-immunized perforin-deficient mice , which display severely impaired Ag-specific cytotoxic activity in vivo ( Figure 2 ) , are resistant to lethal Y . pestis and Y . pseudotuberculosis challenge ( Figure 3 ) . Likewise , the levels of protection provided by wild-type and perforin-deficient YopE69–77-specific CD8 T cells are indistinguishable in T cell chimeric mice ( Figure 6B and 7A ) . These data strongly suggest that perforin does not contribute substantially to CD8 T cell-dependent adaptive host defense against Yersinia . In contrast , a recent publication by Bergman et al . suggested that perforin can be critical for protection against Y . pseudotuberculosis infection [15] . They studied naïve mice infected with an attenuated strain of Y . pseudotuberculosis and reported that perforin deficiency impaired protection , as evidenced by greater bacterial burden in the spleens and livers . When the surviving perforin-deficient mice were challenged with a virulent strain of Y . pseudotuberculosis , the “immunized” perforin-deficient mice only exhibited significantly impaired protection if animals without detectable bacteria were excluded from the analysis . Since we found no evidence that perforin deficiency impairs either bacterial clearance or survival in YopE69–77-immunized mice , the combined data suggest that perforin may be important for primary defense against Yersinia but can be dispensable for adaptive defense . While perforin deficiency has little impact on protection mediated by YopE69–77-specific CD8 T cells , we note that perforin deficiency did not completely abolish all cytotoxicity in our model ( Figure 2B ) . Thus , we cannot exclude the possibility that the residual cytotoxicity , which is independent of perforin , may contribute to adaptive defense against Yersinia . Future studies will need to assess whether such other cytotoxic pathways , for example , Fas/FasL and TNF/TNF receptors , contribute to YopE69–77-specific CD8 T cell-mediated protection . In contrast to perforin deficiency , cytokine deficiency dramatically impairs anti-Yersinia immunity mediated by YopE69–77-specific CD8 T cells . Data presented in this study highlights the complexity of cytokine-mediated protection . YopE69–77-immunized mice that completely lack the ability to produce either TNFα or IFNγ succumb to infection , suggesting that each of these cytokines are absolutely required for CD8 T cell-mediated anti-Yersinia immunity ( Figures 4A and 4B ) . However , the data in Figure 4C reveal that IFNγ is not absolutely required for protection at the time of challenge . It implies that IFNγ may have a role in the development of YopE69–77-specific CD8 T cells that have the capacity to combat Y . pestis . Moreover , the data in Figure 6 reveal that IFNγ production by T cells is not required for resistance to Y . pestis , implying that cell types other than T cells must be capable of producing enough IFNγ to confer modest yet significant protection . In contrast , chimeric mice in which the T cells cannot produce IFNγ exhibit significant impairments in survival after YopE immunization and Y . pestis challenge ( Figure 7A ) . While we cannot completely exclude the possibility that cell reconstitution in mice deficient for T cell-derived IFNγ is suboptimal , the frequency of YopE69–77-specific CD8 T cells in the peripheral blood prior to challenge is similar in wild-type mice and chimeric mice whose T cells cannot produce IFNγ ( Figure 7B ) . Since other cells in the environment are capable of producing IFNγ in that model , the data suggest that a cell-intrinsic defect resulting from the loss of IFNγ influences some quality of differentiating CD8 T cells . One possibility is that mice whose T cells cannot produce IFNγ may have reduced TNFα production or responsiveness . Indeed , IFNγ has been shown to enhance TNF receptor expression [22] . In contrast to IFNγ , Ab-mediated depletion of TNFα at the time of Y . pestis challenge abrogates nearly all protection in YopE69–77-immunized mice , suggesting that TNFα is absolutely required for protection at the time of challenge ( Figure 4C ) . Cells of the macrophage , neutrophil and T cell lineages can each produce TNFα . However , TNFα production by T cells is not absolutely critical for resistance to Y . pestis , implying that cell types other than CD8 T cells must be capable of producing enough TNFα to confer modest yet significant protection ( Figures 5B , 6 and 7 ) . We found that TNFα from macrophages/neutrophils is not absolutely required for protection either , at least not when the mice have intact IFNγ production ( Figure 5A ) . These observations imply that TNFα from one cell type can substitute for the loss of TNFα in another cell type in pulmonary Y . pestis infection . Alternatively , residual production of TNFα from cells other than T cells and macrophages/neutrophils could also contribute to protection . In contrast to the dramatic impact of Ab-mediated depletion of TNFα at the time of challenge , the impact of depleting CD8 T cell-derived TNFα seems to be modest ( Figures 6 and 7 ) . One caveat is that we repeatedly observed a higher frequency of YopE69–77-specific CD8 T cells in the peripheral blood of mice whose T cells could not produce TNFα ( Figure 7B and data not shown ) . Thus , it is conceivable that impairments in protection caused by the absence of T cell-derived TNFα can be restored , at least partially , by an increase in number of YopE69–77-specific CD8 T cells . While depleting either TNFα or IFNγ from CD8 T cells impairs protection to a certain extent , simultaneously depleting both cytokines additionally compromises protection . Specifically , chimeric mice whose T cells can produce neither cytokine display significantly reduced survival when compared with mice whose T cells cannot produce either TNFα or IFNγ ( Figures 6B and 7A ) . Some TNFα production from either T cells or non-T cells , in concert with intact IFNγ production , appears to be sufficient for near optimal protection ( Figure 5 ) . However , the data suggest that TNFα and IFNγ production by CD8 T cells work complementarily to confer optimal protection . One likely mechanism by which these cytokines contribute to host survival is through the control of bacterial burden ( Figures 7C and 7D ) . TNFα and IFNγ are known to activate macrophages and neutrophils to produce reactive nitrogen and oxygen intermediates that assist in the control of intracellular bacteria . Adding purified TNFα and IFNγ to the macrophage cultures can overcome Y . pestis-induced suppression of macrophage anti-bacterial activity in vitro [23] . These cytokines can also stimulate neutrophils to secret antimicrobial molecules to control extracellular bacteria . Although depleting both TNFα and IFNγ produced by T cells dramatically compromises the protection mediated by YopE69–77-specific CD8 T cells , it does not completely abolish the protection . We did observe a minimal of 20% host survival , which is significant in comparison with OVA257–264-immunized wild-type mice and reproducible . This finding implies that while CD8 T cell-derived TNFα and IFNγ are critical for host defense against Y . pesitis , they may not be the sole players . Despite the observation that perforin is dispensable for CD8 T cell-mediated protection against Y . pestis ( Figure 3 ) , its function may contribute to host survival when the two key effector functions of CD8 T cells are absent . Moreover , other mediators that CD8 T cells can produce during infection may also contribute to protection . For example , CD8 T cell production of macrophage inflammatory protein-1α ( MIP-1α/CCL3 ) is required for anti-Listeria immunity [24] , [25] . One could argue that in our splenocyte transfer and bone marrow chimera models ( Figures 6B and 7A , respectively ) , CD4 T cells share the same defect with CD8 T cells . However , we found that CD4 T cells are not required for YopE69–77-mediated protection , since YopE69–77-immunized CD4-deficient mice were protected as well as YopE69–77-immunized wild-type mice ( unpublished data ) . Thus , we think it unlikely that a deficiency of CD4 T cell-derived cytokines accounts for the lost of protection we observe in these models . CD8 T cells can utilize a variety of mechanisms to protect against infectious agents . Understanding the effective and critical functions of the CD8 T cell response against pathogens that cause septic bacterial pneumonias and defining the combinations of effector functions that optimally combat such pathogens should improve our ability to design effective vaccines . Our study shows that YopE69–77-specific CD8 T cells exhibit perforin-dependent cytotoxic activity in vivo , but this effector function does not appear to be required for anti-Yersinia immunity . In contrast , production of TNFα and IFNγ is essential for CD8 T cell-mediated protection . Interestingly , the time at which these cytokines must be produced appears to differ , as does their essential cellular sources . Overall , the data suggest that CD8 T cell-derived TNFα and IFNγ exert complementary functions and CD8 T cells require both cytokines to provide optimal defense against pulmonary Y . pestis infection . Our study further suggests cytokine-producing CD8 T cells may be a valuable addition to subunit plague vaccines and assays detecting Ag-specific TNFα production may be a particularly useful correlate of plague vaccine efficacy . Given that septic bacterial pneumonias are a leading cause of death , it will be interesting to determine whether cytokine production , rather than cytolysis , is generally more important for T cell-mediated defense against septic bacterial pneumonias .
All animal studies were conducted in accordance with the Guide for Care and Use of Laboratory Animals of the National Institutes of Heath and approved by Trudeau Institute Animal Care and Use Committee ( IACUC protocols # 02-022 and 02-161 ) . Wild-type C57BL/6 , B6 . 129S7-Ifngtm1Ts ( IFNγ-deficient ) , B6 . 129S-Tnftm1Gkl ( TNFα-deficient ) , C57BL/6-Prf1tm1Sdz ( perforin-deficient ) , B6 . 129P2-Tcrbtm1Mom Tcrdtm1Mom ( TCRβδ-deficient ) , B6 . SJL-Ptprca Pepcb/BoyJ ( CD45 . 1 ) mice were purchased from The Jackson Laboratory ( Bar Harbor , ME ) and then bred in the specific-pathogen-free Trudeau Institute Animal Breeding Facility . TNFα/IFNγ double-deficient mice were generated at Trudeau Institute . Mice deficient for TNFα production in macrophage and neutrophils ( MN-TNF KO mice ) or T cells ( T-TNF KO mice ) were generated by crossing mice with TNF “floxed” genes to mice carrying the LysM-Cre or CD4-Cre transgenes , respectively [18] . All strains are on the C57BL/6 background . Experimental mice were matched for age and sex , and first immunized between 6 and 10 weeks of age . Pigmentation locus-deficient Y . pestis strain KIM D27 was originally provided by Dr . Robert Brubaker ( Michigan State University , East Lansing , MI ) . Strain D27 bacilli from frozen glycerol stocks were grown overnight at 26°C in Bacto heart infusion broth ( Difco Laboratories , Detroit , MI ) supplemented with 2 . 5 mM CaCl2 . Cultures were then diluted to an OD of 0 . 1 at 620 nm , regrown for 3–4 h at 26°C , quantified by OD measurement , and then washed and resuspended in saline at the desired concentration . The numbers of bacterial CFU in the inocula were confirmed by plating . Infections were performed by applying 30 µl to the nares of mice that were lightly anesthetized with isoflurane . The intranasal median lethal dose ( MLD ) for strain D27 is approximately 1×104 CFU when the bacteria are grown and administered as described above [26] . The fully virulent pigmentation locus-positive Y . pestis strain CO92 NR-641 was obtained through the NIH Biodefense and Emerging Infections Research Resources Repository , NIAID , NIH . Bacteria from frozen glycerol stocks were grown overnight at 26°C in Bacto heart infusion broth . Cultures were then diluted to an OD of approximately 0 . 1 at 620 nm , regrown for 2 . 5–3 h at 26°C , quantified by OD measurement , and prepared at the desired concentration . The numbers of bacterial CFU in the inocula were confirmed by plating . Infections were performed by applying 25 µl to the nares of mice that were lightly anesthetized with isoflurane . Preliminary studies established that the intranasal MLD for strain CO92 is approximately 1×103 CFU when the bacteria are grown and administered as described above . Y . pseudotuberculosis serotype O:1 strain 32777 bacilli from frozen glycerol stocks were grown overnight at 26°C in Luria-Bertani medium to reach an OD of approximately 1 . 2 at 600 nm . The cultures were then washed and resuspended in saline to achieve the desired concentration . For intragastric infections , mice were fasted for 20–21 h and then inoculated with 200 µl of Y . pseudotuberculosis through a 20-gauge feeding needle . For intravenous infections , mice were injected with 200 µl of Y . pseudotuberculosis via the tail vein . The intragastric and intravenous MLD for strain 32777 is approximately 5×108 CFU and 12 CFU , respectively , when the bacteria are grown and administered as described above . Unless otherwise indicated , mice were lightly anesthetized with isoflurane and immunized intranasally on days 0 , 7 and 21 with 15 µl saline solution containing 1 µg cholera toxin ( CT; List Biological Laboratory , Campbell , CA ) as adjuvant and 1 µg YopE69–77 peptide ( H2N-SVIGFIQRM-OH; New England Peptide , Gardner , MA ) or control OVA257–264 peptide ( H2N-SIINFEKL-OH ) . Mice were then challenged with 20 or 200 MLD of Y . pestis strain D27 , 10 MLD of Y . pestis strain CO92 or 10 MLD of Y . pseudotuberculosis strain 32777 at day 37 . In some experiments , PBL were collected by mandibular bleeding at day 35 to assay Ag-specific CD8 T cells by flow cytometry . Splenocytes harvested from naïve CD45 . 1+ congenic mice were used as target cells . After lysing red blood cells using ammonium chloride buffer , the splenocytes were pulsed with either YopE69–77 or OVA257–264 peptide ( 10 µM ) for 1 h at 37°C in complete medium ( DMEM supplemented with 10% heat-inactivated FCS , 2 mM L-glutamine , 1 mM sodium pyruvate , 0 . 1 mM nonessential amino acids , 1% penicillin-streptomycin and 55 µM 2-mercaptoethanol ) . After washing and resuspending in Hank's balanced salt solution , YopE69–77-pulsed cells were incubated with 10 µM CFSE and OVA257–264-pulsed cells were incubated with 1 µM CFSE for 10z min in a 37°C water bath . CFSE labeling was then stopped by adding FBS to a final concentration of 20% . Cells were washed twice with complete medium and mixed at a 1∶1 ratio . A total of 2×107 cells were then injected intravenously into immunized recipient mice . Spleen cells were collected 20–22 h later and stained with PE-conjugated anti-CD45 . 1 mAb , biotin-conjugated anti-CD45 . 2 mAb and allophycocyanin-conjugated streptavidin . The frequency of each CFSE-labeled population in the target cell gate ( CD45 . 1+CD45 . 2− ) was determined by flow cytometry . Cells injected into naïve C57BL/6 mice were used as controls . The percent specific lysis was determined by the following formula: [1- ( ratio of cells recovered from naïve mice/ratio of cells recovered from infected mice ) ]×100 . For YopE-specific lysis , the ratio of recovery of OVA257–264-pulsed cells to YopE69–77-pulsed cells = ( percentage of CFSElow cells ) / ( percentage of CFSEhigh cells ) . For OVA-specific lysis , the ratio of recovery of YopE69–77-pulsed cells to OVA257–264-pulsed cells = ( percentage of CFSEhigh cells ) / ( percentage of CFSElow cells ) . The allophycocyanin-conjugated MHC class I peptide tetramers KbYopE69–77 and Kb OVA257–264 were supplied by the NIH Tetramer Facility and the Trudeau Institute Molecular Biology Core Facility , respectively . PBL were obtained from mandibular blood collected in heparin-containing eppendorf tubes . Red blood cells were lysed by treatment with ammonium chloride . Enumeration of YopE69–77-specific CD8 T cells by KbYopE69–77 tetramer staining was described previously [13] . In brief , cells were treated with 1 µg Fc block ( clone 2 . 4G2 ) for 10 min at 4°C , washed , and incubated with tetramers for 1 h at room temperature . After washing again , cells were stained with anti-CD4-FITC ( clone RM4–5 ) and anti-CD8-PE ( clone 53-6 . 7 ) for 30 min at 4°C . Data were collected on a BD Bioscience FACSCanto II and analyzed using FlowJo software . For survival studies , mice were monitored at least once daily after infection . Unresponsive or recumbent mice were considered moribund and euthanized . For measurement of bacterial burden , mice were euthanized by carbon dioxide asphyxiation at the indicated day after infection . Liver and lung tissues were harvested and homogenized in saline . Serial dilutions of the homogenates were plated on blood agar and incubated at 26°C for 48 h . Mice were treated with 1 mg mAb specific for TNFα ( clone XT3 . 11 , rat IgG1 ) or IFNγ ( clone XMG1 . 2 , rat IgG1 ) diluted in PBS . The mAb were administered intraperitoneally on the day before challenge infection . Control mice received 1–1 . 6 mg of isotype-matched rat IgG1 mAb ( clone HRPN ) . For primed CD8+ T cell transfers , donor mice were immunized intranasally as described above on days 0 , 7 and 21 . At day 36 , splenocytes were harvested , red blood cells were lysed and cell suspensions were applied to nylon wool columns to enrich T cells . After incubation at 37°C for 90 min , the nonadherent cells were eluted and CD8+ T cells were purified by magnetic activated cell sorting ( MACS; Miltenyi Biotec Inc . , Auburn , CA ) according to the manufacturer's instructions . The purified cells were incubated at 37°C for 90 min followed by centrifugation to release beads from cells . The purity of CD8+ donor T cells was ∼95% . A total of 1×107 CD8+ T cells were injected intravenously into naïve wild-type C57BL/6 recipient mice . The following day , the recipient mice were challenged with Y . pestis strain D27 . For naïve splenocyte transfers , donor splenocytes were isolated and red blood cells were lysed . After washing , 5×107 cells were injected intravenously into naïve TCRβδ-deficient recipient mice . The recipient mice were then immunized and challenged with Y . pestis strain D27 as described above . TCRβδ-deficient mice were lethally irradiated ( 950 rad provided in two doses ) and reconstituted with a total of 1×107 donor bone marrow cells . The donor cells comprised bone marrow cells from TCRβδ-deficient mice mixed with bone marrow cells from wild-type C57BL/6 mice or the indicated gene-deficient mice at a 3 to 1 ratio . Chimeric mice were allowed to reconstitute for 6 weeks before they were immunized and challenged with Y . pestis strain D27 . Statistical analyses were performed using the computer program Prism 5 ( GraphPad Software ) . Survival data were analyzed by log-rank tests . Bacterial burden data were analyzed by nonparametric Kruskal-Wallis test followed by Dunn's post test; CFU that fell below the detection limit of our assay were assigned values 0 . 2 log below the detection limit . All other data were analyzed by Student t test or one-way ANOVA as indicated . *p<0 . 5 , **p<0 . 01 , ***p<0 . 001 , ****p<0 . 0001; ns , not significant . | Bacterial pneumonia is one of the most common causes of death worldwide . Pulmonary infection of bacterium Yersinia pestis , the causative agent of plague , results in pneumonic plague and is extremely lethal . Mouse models of pulmonary Y . pestis infection are considered translational tools for the development of pneumonic plague countermeasures and studies of the basic mechanisms of immune defense against acutely lethal pulmonary bacterial infections . Here , we used several methods to investigate the functions that CD8 T cells exert to confer protection against pulmonary Y . pestis infection and evaluated their relative contributions . We found that although CD8 T cells have the ability to kill Y . pestis-infected cells , a function called cytotoxicity , this function is not required for CD8 T cells to protect against Y . pestis infection . In contrast , protection depends upon the ability of CD8 T cells to produce the cytokines TNFα and IFNγ , and mice whose T cells cannot produce these two cytokines are not protected . Therefore , we conclude that cytokine production , not cytotoxicity , is essential for CD8 T cell-mediated control of pulmonary Y . pestis infection and we suggest that assays detecting cytokine production may be useful correlates of vaccine efficacy against plague and other acutely lethal septic bacterial pneumonias . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"cytokines",
"immunity",
"to",
"infections",
"immunology",
"microbiology",
"bacterial",
"diseases",
"developmental",
"biology",
"yersinia",
"molecular",
"development",
"yersinia",
"pseudotuberculosis",
"vaccination",
"and",
"immunization",
"bacterial",
"pathogens",
"infectious",
"diseases",
"white",
"blood",
"cells",
"yersinia",
"pestis",
"animal",
"cells",
"medical",
"microbiology",
"t",
"cells",
"microbial",
"pathogens",
"immune",
"response",
"yersiniosis",
"immune",
"system",
"yersinia",
"pseudotuberculosis",
"infection",
"cell",
"biology",
"immunity",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"acquired",
"immune",
"system"
] | 2014 | TNFα and IFNγ but Not Perforin Are Critical for CD8 T Cell-Mediated Protection against Pulmonary Yersinia pestis Infection |
Salmonella enterica serovar Typhimurium is arguably the world’s best-understood bacterial pathogen . However , crucial details about the genetic programs used by the bacterium to survive and replicate in macrophages have remained obscure because of the challenge of studying gene expression of intracellular pathogens during infection . Here , we report the use of deep sequencing ( RNA-seq ) to reveal the transcriptional architecture and gene activity of Salmonella during infection of murine macrophages , providing new insights into the strategies used by the pathogen to survive in a bactericidal immune cell . We characterized 3583 transcriptional start sites that are active within macrophages , and highlight 11 of these as candidates for the delivery of heterologous antigens from Salmonella vaccine strains . A majority ( 88% ) of the 280 S . Typhimurium sRNAs were expressed inside macrophages , and SPI13 and SPI2 were the most highly expressed pathogenicity islands . We identified 31 S . Typhimurium genes that were strongly up-regulated inside macrophages but expressed at very low levels during in vitro growth . The SalComMac online resource allows the visualisation of every transcript expressed during bacterial replication within mammalian cells . This primary transcriptome of intra-macrophage S . -Typhimurium describes the transcriptional start sites and the transcripts responsible for virulence traits , and catalogues the sRNAs that may play a role in the regulation of gene expression during infection .
Salmonella enterica ( S . enterica ) is a food and water-borne pathogen responsible for widespread disease in humans and other animals . The serovars responsible for typhoid fever kill more than 250 , 000 people per year , while an estimated 94 million cases of Salmonella-mediated gastroenteritis cause 155 , 000 deaths each year [1 , 2] . Recently , it has been discovered that non-typhoidal serovars are causing an epidemic of invasive disease that is killing 680 , 000 people each year [3] . Decades of intense research have revealed intricate details of Salmonella pathogenicity [4] . S . enterica initiates infection in the small intestine by penetrating the mucus layer that protects the gut epithelium . During the infection process , S . enterica endures a series of hostile environments within the host , including the acidity of the stomach , antimicrobial peptides and bile in the intestine , and the toxicity of intracellular vacuoles [5] . These challenges are met by physiological and metabolic adaptations that allow the bacterium to resist the innate host defences . Salmonella pathogenicity island ( SPI ) 1 and SPI4-encoded proteins , and other virulence determinants , mediate the entry into epithelial cells [4 , 6] . The bacteria subsequently exit from epithelial cells and are taken up by the phagocytic cells of the innate immune system such as macrophages [7 , 8] . S . enterica responds to the phagosomal environment within macrophages by secreting effector proteins that generate a specialized intracellular compartment , the Salmonella-containing-vacuole ( SCV ) . The SCV allows S . enterica to evade macrophage killing , and infected macrophages become a vehicle for systemic bacterial spread [9 , 10] . Physiological , metabolic and effector protein-mediated adaptation strategies allow the bacteria to replicate within the SCV , and to form persister cells [10 , 11]; many of these adaptive processes are regulated at the transcriptional level [12] . Bacterial gene regulation is mediated by a combination of transcription factors , nucleoid-associated proteins and regulatory small non-coding RNAs ( sRNAs ) . Following the publication of the first S . enterica genome , microarray-based transcriptomic approaches were used to define regulons and stimulons of the model pathogen S . enterica serovar Typhimurium ( S . Typhimurium ) [13] . Because the microarray-derived data only provided a limited view of Salmonella gene expression inside macrophages [14–16] , an RNA-seq-based approach was required to gain the information for understanding mechanisms of gene regulation . RNA-seq analysis generates high-resolution transcriptomic data and accurate information on gene expression levels , and provides extensive information concerning the location of Transcriptional Start Sites ( TSS ) , the 5′ and 3′ un-translated regions of genes , antisense transcription , and sRNAs . We recently used this approach to reveal the complete transcriptional network of S . Typhimurium during growth in 22 laboratory conditions [17] . Here , we present the primary transcriptome of intra-macrophage S . Typhimurium strain 4/74 . All intra-macrophage gene expression and transcriptional organisation data are presented in our online resource , SalComMac [http://tinyurl . com/SalComMac] .
The intra-macrophage transcriptome of S . enterica was determined with S . Typhimurium strain 4/74 ( Dataset 1 in S1 Table ) within cultured murine RAW 264 . 7 macrophage-like cells that do not express the Nramp1 ( Slc11a1 ) host resistance cation-efflux pump [18] . Because earlier transcriptomic analyses showed that more than 90% of S . Typhimurium genes were expressed at similar levels during early , middle and late stages of macrophage infection [14] , we focused on a single time point . We used eight hours post-infection to coincide with the nitrosative burst in Salmonella-infected murine macrophages [14] . Total bacterial RNA was isolated and analysed by RNA-seq [17] ( Materials and Methods ) ( Fig 1 ) . Overall , 136 million sequence reads were generated from seven cDNA libraries . These represent two biological replicates of intra-macrophage Salmonella RNA-seq , two biological replicates of intra-macrophage Salmonella differential RNA-seq ( dRNA-seq ) and RNA-seq of the ΔssrA mutant and two biological replicates of wild-type 4/74 grown under in vitro SPI2-inducing conditions . Between 5 and 10 million uniquely-mapped reads were obtained from each library ( Dataset 2 in S1 Table ) , providing sufficient coverage for robust transcriptomic analysis [19] . Gene expression values were calculated by the Transcripts Per Million ( TPM ) approach [20] . A threshold TPM value of 10 was used as a cut-off to define gene expression ( Materials and Methods ) [17] . The intra-macrophage transcriptome was compared to our published RNA-seq-based transcriptome for early stationary phase ( ESP ) , an infection-relevant in vitro growth condition that is associated with high expression of S . Typhimurium SPI1 genes [17 , 21] . The precise nucleotide position of individual TSS was identified on a genome-wide scale by dRNA-seq [22] . In total , 3583 TSS were expressed by S . Typhimurium during infection of macrophages ( Dataset 3 in S1 Table ) . This included 3538 TSS expressed in the ESP condition [17] and 45 TSS which were newly identified in this study . To assign a relative strength to each TSS we determined the expression levels of the first 10 bases of each transcript , designated the promoter usage value ( PUV ) [17 , 23] . Because >99% of S . Typhimurium protein coding genes have a 5’ untranslated region ( UTR ) and 15% of protein-coding genes possess multiple TSS , the PUV allows promoter strength to be quantified independently of gene expression [17] . We used the relative PUV to compare the expression of S . Typhimurium TSS between the intra-macrophage and the ESP in vitro condition , and categorised the TSS as either ‘Macrophage up-regulated’ , ‘Macrophage down-regulated’ or ‘Macrophage-independent’ ( Materials and Methods ) . Of the 3583 TSS expressed in macrophages , 883 were macrophage up-regulated and 834 were macrophage down-regulated , compared with ESP ( Fig 2A; Dataset 3 in S1 Table ) . The TSS of the lgl-ripABC ( STM3117-3120 ) SPI13 operon [24 , 25] was the most highly up-regulated , with a relative PUV of >500 -fold . Other highly up-regulated TSS controlled the expression of genes such as trpE and sseJ . We found that 72% of the promoters reported to be highly expressed in the murine spleen [26] were up-regulated in RAW macrophages . Forty five new TSS were identified in this study , including the TSS of STM0854 that controls intra-macrophage expression of the major polycistronic transcript of SPI14 ( Fig 2B ) . Other novel TSS controlled the expression of genes involved in several core cellular processes including bglA , entB , fliN and nrdE , and a TSS that initiated a transcript antisense to the stfD coding gene . Of the 834 macrophage down-regulated TSS , the biggest reduction in promoter expression between macrophages and the ESP condition was more than 200-fold and associated with SPI1 genes and the flagellin-encoding fliC gene ( Dataset 3 in S1 Table ) . Salmonella pathogenicity island 2 ( SPI2 ) is required for Salmonella replication within eukaryotic cells and for systemic infection of mammalian hosts . SPI2 encodes the type III secretion system ( T3SS ) that delivers many effector proteins responsible for the function of the SCV within macrophages [4 , 27] . The transcriptional organization of SPI2 is shown in Fig 3 . We recently used dRNA-seq to discover a TSS upstream of ssaR [17] , which we now confirm by 5’ RACE ( S1 Fig ) . SPI2 is therefore transcribed as six operons inside macrophages ( Fig 3 ) . All SPI2 genes were up-regulated within macrophages , reflecting the phosphate/magnesium starvation and the acidity of the SCV [28 , 29] . The RNA-seq data were used to calculate promoter usage values for the different SPI2 promoters , identifying PssaM as the most up-regulated SPI2 promoter , followed by PssaR and PssaB ( Dataset 3 in S1 Table ) . We note that each of the six SPI2 promoters was also transcribed in the “InSPI2” growth condition , confirming that expression of all SPI2 operons occurs in vitro when stimulated by growth in an acidic low-phosphate environment [30] . The SPI2 island and genes that encode SPI2-translocated effectors are activated by the SsrAB two component system [31] . The SsrA sensor kinase phosphorylates the SsrB response regulator to activate gene expression [32–34] . To investigate the role of SsrA in the regulation of macrophage up-regulated TSS , we used RNA-seq to analyse the transcriptome of a ΔssrA mutant and wild-type 4/74 grown in InSPI2 medium . Of the 883 macrophage up-regulated TSS , 221 showed reduced ( >2-fold ) expression in the absence of SsrA and we infer that these are SsrA-activated ( Fig 2A; Dataset 3 in S1 Table ) . All the genes that encode SPI2-translocated effector proteins were controlled by SsrA-activated promoters . There are 662 macrophage up-regulated TSS that appear to have SsrA-independent regulatory mechanisms , and these merit further study . S . Typhimurium carries 12 pathogenicity islands on the chromosome of strain 4/74 [17 , 35 , 36] . Expression profiles of S . Typhimurium pathogenicity islands ( Fig 4; Datasets 4 and 5 in S1 Table ) reveal that SPI2 and SPI13 were the most highly up-regulated during infection of macrophages , by an average of 44 and 82-fold , respectively ( Dataset 5 in S1 Table ) . The SPI3 , SPI5 , SPI11 , SPI12 and SPI14 islands showed moderate intra-macrophage up-regulation . SPI6 and SPI9 show macrophage-independent expression , and both SPI1 and SPI4 were significantly down-regulated inside macrophages . Effector proteins of S . Typhimurium are secreted via the SPI1 T3SS , the SPI2 T3SS or through both translocation systems . We reported that the genes encoding SPI1-translocated effectors showed a SPI1-like expression pattern , and genes encoding SPI2-translocated effectors showed a SPI2-like expression pattern [17] . Our data show that the genes encoding all SPI2-translocated effectors were highly macrophage up-regulated ( Dataset 4 in S1 Table ) ( up to 70-fold ) , and the genes that encode the 7 effectors that are secreted by both the SPI1 and SPI2 T3SS were all expressed inside macrophages; the TPM values range from 50 to 230 ( Fig 5 ) . In contrast , genes encoding the 9 SPI1-translocated effectors were all macrophage down-regulated , by up to 160-fold , and were not significantly expressed within macrophages . Clearly , the actual intra-macrophage expression level of genes that encode candidate effector proteins has biological relevance . The transcriptomic data identified two specific SPI13 and SPI14-encoded operons that were highly up-regulated in macrophages ( Datasets 4 and 5 in S1 Table ) but were not significantly expressed in 20 in vitro conditions [17] . First , the SPI13-associated lgl-ripABC ( STM3117-STM3120 ) operon was >250 fold up-regulated within macrophage . The lgl-ripABC operon is required for Salmonella infection [37 , 38] , encoding enzymes that catabolise itaconate , an anti-microbial metabolite that is synthesised by infected macrophages [25 , 39] . Second , the SPI14-located STM0854-0857 operon is also required for Salmonella virulence [38] , showed moderate ( 3 to 20-fold ) intra-macrophage up-regulation , and was not expressed in in vitro growth conditions [17] . The TSS of the STM0854 and STM0859 transcripts were only expressed in macrophages , and not in any in vitro conditions . Taken together , these data suggest that the STM0854-0857 and lgl-ripABC operons respond to an intra-cellular signal that remains to be identified in macrophages . For ripABC , this signal may be itaconate [25] . For SPI3 , the PhoP-activated mgtCBR operon [40] was up-regulated >15 fold within macrophages , while other SPI3 genes ( slsA , marT and rhuM ) were moderately up-regulated . The role of mgtCBR in virulence involves the long leader of the mgtC transcript that encodes MgtP . The mgtC leader is responsive to ATP levels [41] and inhibits F1Fo ATP synthase to maintain ATP homeostasis in the acidic intra-macrophage environment [42] . SPI5 encodes effectors translocated by both SPI1 and SPI2 T3SS [43 , 44] . The sopB gene encodes a SPI1-translocated effector and is macrophage down-regulated by 50-fold . In contrast , the gene encoding the SPI2 effector pipB is up-regulated . PipB localizes to the SCV membrane and brings about the formation of tubular extensions , the Salmonella induced filaments ( SIFs ) [45 , 46] . The SPI6-encoded Type 6 secretion system [47] , is important for the colonization and systemic infections of chickens and mice [48 , 49] . None of the SPI6 genes were expressed in macrophages or in various in vitro conditions [17] . This is consistent with the reported repression of SPI6 genes by H-NS [50] . During infection of the gastrointestinal tract , the SPI1-encoded T3SS of S . Typhimurium is responsible for inflammatory diarrhoea and the invasion of non-phagocytic epithelial cells [51–53] . Thirty-three SPI1 genes were down-regulated within macrophages ( Dataset 4 in S1 Table ) , and were highly expressed at ESP , confirming earlier reports [17 , 54] . HilA , the transcriptional activator of SPI1 , is controlled by the co-ordinated action of HilC/HilD/RtsA , and consequently up-regulates the SPI1 island & SPI1-translocated genes [55–57] . The transcription of hilA is regulated by HilD , an important activator that controls cross-talk between SPI1 and SPI2 expression [55 , 58] . The hilA , hilC , hilD and rtsA regulatory genes are down-regulated more than 100-fold within macrophages , consistent with the down-regulation of the SPI1 island . The siiABCDEF operon of SPI4 encodes a Type 1 secretion system , and was down-regulated within macrophages . SiiE is a non-fimbrial adhesin responsible for the adhesion of Salmonella to epithelial cells and is expressed during the extra-cellular phase of infection [59 , 60] . Cross talk between SPI1 and 4 can promote tight binding of the bacterium to the epithelial membrane , and facilitate efficient SPI1 translocation [61] . Intracellular expression of individual bacterial genes or entire regulons can be used to investigate the microenvironment inside the host cell vacuole [62] . Direct comparison between this RNA-seq-based dataset ( Dataset 4 in S1 Table ) and previous microarray-based transcriptomic results confirm and extend key findings from Eriksson et al . ( 2003 ) and Hautefort et al . ( 2008 ) [14 , 15] . The datasets all show that the most highly macrophage up-regulated Salmonella gene is asr ( STM1485 ) , required for the intra-cellular replication of Salmonella [63] . The 890-fold up-regulation of asr reflects the acidic conditions within the SCV [64] ( Dataset 4 in S1 Table ) . To investigate the gene expression network of intra-macrophage Salmonella , we focused on 157 transcriptional regulators ( Dataset 6 in S1 Table ) . The levels of 34 transcription factors were >3-fold macrophage up-regulated , and 7 transcription factors were >3-fold macrophage down-regulated . To determine whether the differential expression of individual regulators was reflected by up- or down-regulation of the associated regulons , we compared the expression of several genes controlled by each transcription factor in ESP and macrophages . We observed that the up-regulation of SPI2 regulators ssrB , ompR and phoP and down-regulation of SPI1 regulators hilD , hilA , hilC , invF and sprB correlates with the expression of their respective regulons ( Fig 6A and 6B ) . The macrophage up-regulation of regulons that detoxify peroxide , detoxify nitric oxide and relieve envelope stress and protein misfolding ( soxS , oxyR , marA , marS , rpoE , rpoH and nsrR regulons and genes hmpA , msrA , ycfR , sbp , sodC , katG ) , reflects the bacterial response to the oxidative and nitrosative bursts that occurred during the infection process . Bacterial genes were assigned to functional groups to investigate the metabolic resources of macrophages . The most up-regulated functional categories of S . Typhimurium genes within macrophages are involved in carbohydrate and amino acid metabolism ( S2 Fig ) . The “nutritional immunity” hypothesis posits that the innate immune response of the host reduces the availability of important nutrients for intracellular bacteria [65] , which may explain why S . Typhimurium has evolved the ability to utilise a diverse range of host nutrients , including some sugars and amino acids that accumulate in murine macrophages during intracellular infection [66] . It is known that the major carbon sources utilised by S . Typhimurium in macrophages of the mouse spleen are deoxyribonucleotides , fatty acids , glucose , gluconate , glycerol , lactate and N-acetyl-glucosamine [67] . In our study , we observe the concerted up-regulation of multiple metabolic regulons in RAW macrophages that are consistent with the simultaneous degradation of deoxyribonucleotides , fatty acids , galactose , glucose , gluconate , glycerol , lactate , N-acetyl-glucosamine and sialic acid , while regulons controlling gluconate , maltose , myo-inositol and xylose metabolism showed significant macrophage down-regulation ( Fig 6 ) . Our current understanding of the intracellular metabolism of Salmonella in cultured macrophages coupled with the comprehensive data available for S . Typhimurium during infection of the murine spleen [66] suggest that cultured macrophages represent a good model for the study of the intracellular metabolism of Salmonella . Mammalian macrophages reduce intracellular levels of metals such as iron as part of their strategy to limit bacterial replication [68] , and S . Typhimurium responds by switching on the expression of metal-uptake systems . These include the intra-macrophage up-regulation of the sitABCD operon , responsible for manganese and iron transport [69] and of genes responsible for iron transport and biogenesis of iron-sulfur cluster containing proteins ( ent , fep , fhu , iro , sfb , sit and suf genes , as well as the yhfP ( iscR ) , and rstA regulons ) , magnesium ( mgtCBR ) transport and zinc ( zur ) uptake . We suggest that these expression patterns reflect the relatively low levels of magnesium , manganese , iron and zinc metals within the SCV [70] . Genes encoding the flagella and chemotaxis systems were significantly down-regulated in macrophages ( between 50 to 100-fold ) , consistent with previous reports for both the Typhimurium and Typhi serovars [14 , 15 , 71] ( Dataset 4 in S1 Table; S2 Fig ) . Specifically flh , flg , fli , flj , mot , che and aer genes were down-regulated . The flhDC-mediated regulation of flagellar transcription is complex [72] , and cross-talk between SPI1 and flagellar genes was recently reported [73] . The flagellar regulator FliZ is a post-transcriptional activator of flhDC that positively regulates SPI1 by activating the hilD-rtsAB cascade [74] . In turn , RtsB represses the flhDC promoter [57] . These regulatory mechanisms probably account for the down-regulation of flagellar genes within macrophages , consistent with the shut-down of flagellar synthesis associated with the non-motile bacteria found in the SCV [75] . This contrasts with the reported up-regulation of SPI1 and flagella that occurs when S . Typhimurium encounters the cytosol of epithelial cells [75] . To find genes that were up-regulated in the intra-macrophage environment but not in standard laboratory conditions , we used a comparative transcriptomic approach to identify genes that showed significantly higher expression in macrophages than in any of 20 in vitro conditions [17] ( Materials and Methods ) . Our analysis identified 31 genes that were specifically up-regulated within macrophages ( Dataset 7 in S1 Table; Fig 7A and 7B ) . These represent an interesting class of bacterial genes that are up-regulated in macrophages due to a factor encountered within macrophages and not in the in vitro growth conditions . The STM3117-STM3120 ( lgl-ripABC ) genes are a good example , of highly macrophage-induced genes ( Fig 7C ) that are involved in the detoxification of two SCV-specific metabolites , methylglyoxal and itaconate [24 , 25] . We propose that comparative transcriptomics will be a useful approach for identifying genes that respond to specific components of the SCV environment . The majority of the genes in Fig 7A have a STM or a yxx prefix and are designated as “FUN” genes , for “function unknown” [76] . Overall , 18 of the 31 genes that were specifically up-regulated within macrophages have previously been shown to be required for virulence ( Dataset 7 in S1 Table ) . We speculate that these FUN genes respond to a specific component of the intra-vacuolar environment of the macrophage and could play important roles in the process of infection . The identification of a discrete set of promoters that are up-regulated in macrophages could have therapeutic applications . Attenuated strains of S . Typhimurium have been used extensively as vaccines [77] , and for expressing anti-cancer proteins within tumours [78] . These technologies require specific Salmonella gene promoters to drive the production of foreign antigens [79] . For example , the ssaG promoter of SPI2 has been used to express E . coli heat labile toxin in S . Typhimurium [80] . However , the ssaG promoter is active in the gut [81 , 82] , and so may not be the ideal antigen delivery system . We sought to identify candidate promoters with the characteristics required to deliver antigens from attenuated live vaccine strains of S . Typhimurium during intracellular infection . We screened our intra-macrophage promoter expression data to identify primary TSS that were highly expressed within macrophages , and driving a downstream gene that was highly macrophage up-regulated . Eleven promoters were identified as suitable for antigen delivery during infection ( Dataset 8 in S1 Table ) , controlling the asr , bioB , iroB , sseJ , STM0854 ( SPI14 ) and ripC ( SPI13 ) genes . Of these , sseJ is highly expressed within mouse organs [83] . The ripC promoter may be ideal for antigen delivery as it is highly and specifically induced inside macrophages ( Dataset 3 in S1 Table; Fig 7C ) . However , high-level expression of heterologous antigens does not always generate the optimal stimulation of immune responses [79] , and over-expression of certain proteins could compromise bacterial fitness . For this reason , we categorized the macrophage-up-regulated genes from Fig 7A , based on their levels of intra-macrophage expression and identified the promoter of STM0854 as a promising candidate for moderate but specific induction of gene expression within macrophages ( Fig 7B ) . The 11 promoter candidates have the potential to deliver different levels of heterologous antigens and could be used to improve Salmonella-based intracellular vaccine delivery systems . Bacterial gene expression is controlled by transcription factors , nucleoid-associated proteins and sRNAs . Bacterial sRNAs are roughly 50–300 nucleotides in length , and play regulatory roles in key physiological activities like iron homeostasis , carbon metabolism , anaerobic adaptation , envelope stress and pathogenesis [84–88] . To date , 280 sRNAs have been identified in S . Typhimurium 4/74 [17] , but little is known about their role in virulence [5 , 85] . The fact that 246 of 280 sRNAs were expressed within macrophages ( TPM value >10; Dataset 9 in S1 Table ) suggests that many could potentially play a regulatory role during infection . In terms of relative expression , we found that 34 sRNAs were macrophage up-regulated and 119 sRNAs were macrophage down-regulated , compared to ESP ( Dataset 9 in S1 Table; Fig 8A ) . The Hfq chaperone protein mediates sRNA-mRNA interactions and binds to at least 115 S . Typhimurium sRNAs [17 , 89] , of which 19 were up-regulated within macrophages ( including RyhB-1/2 , OxyS , MicF and RybB ) and 56 were down-regulated ( including ArcZ , DsrA and DapZ ) , compared to ESP ( Dataset 9 in S1 Table; Fig 8B ) . The expression patterns of well-characterised sRNAs provide insight into the conditions experienced by S . Typhimurium bacteria in the SCV . For instance , up-regulation of the RpoE-dependent sRNAs MicA and RybB inside macrophages likely reflects envelope stress of S . Typhimurium during intracellular proliferation [90 , 91] . Another sRNA that is RpoE-dependent in E . coli , MicL ( RyeF ) [92] is up-regulated 30-fold within macrophages , but it is not yet known whether this sRNA is controlled by RpoE in Salmonella . The iron-regulated homologs RyhB-1 and RyhB-2 were the most highly up-regulated sRNAs within macrophages compared to ESP ( Dataset 9 in S1 Table , Fig 8C ) , reflecting the iron-limited intra-macrophage environment [14 , 17 , 93 , 94] . RyhB-1 and RyhB-2 ( named RfrA and RfrB in S . Typhi ) are also known to be important for replication of S . Typhi within macrophages [95] . Our data confirm that the IsrH , RyhB-1 and RyhB-2 ( IsrE ) sRNAs are up-regulated , as originally reported within J774 macrophages [93] . We analysed the expression of six sRNAs that were up-regulated within fibroblasts , a cell type that does not support the replication of Salmonella [96] . Two of these sRNAs , RyhB-1 and RyhB-2 , were also up-regulated in macrophages ( Dataset 9 in S1 Table ) . We identified several uncharacterized Hfq-associated sRNAs that were up-regulated within macrophages , including STnc440 , STnc470 and STnc3750 which have an expression pattern consistent with a role in virulence . The function of these sRNAs is currently under investigation . To determine whether macrophage-regulated sRNAs were phylogenetically conserved between fourteen serovars that represent much of the diversity of the Salmonella genus , we analysed 29 enterobacterial genomes ( Dataset 10 in S1 Table ) . We found that 176 sRNAs were conserved ( >90% sequence identity ) within the Salmonella genus , but not in other members of the Enterobacteriaceae ( <70% sequence identity ) , and were designated Salmonella-specific . About 10% ( 17 ) of the Salmonella-specific sRNAs were up-regulated within macrophages ( including STnc440 and IsrH ) while 74 were down-regulated in macrophages ( including DapZ and InvR ) , compared to ESP ( Fig 8C , Dataset 10 in S1 Table ) . We propose that some of these 91 macrophage-regulated sRNAs could play important roles in the regulation of gene expression during the intracellular phase of Salmonella infection . Salmonella bacteria are exposed to multiple stressors within the vacuolar compartment of macrophages , including acid pH , reactive oxygen and reactive nitrogen species . Adaptation to this hostile environment has a profound impact upon the transcriptome of S . Typhimurium , and we have now defined the TSS and sRNAs that react to the intra-vacuolar environment during the intracellular phase of the Salmonella infection cycle . Our data provide an overall view of sRNA expression within macrophages , and represent a resource for the investigation of post-transcriptional regulation during the intracellular life of Salmonella . This study offers new insights into the interaction of Salmonella with mammalian cells , and brings us a step closer to understanding the gene regulatory mechanisms that facilitate the success of this dangerous pathogen . The SalComMac online resource [http://tinyurl . com/SalComMac] is intended to simplify the comparison of the transcriptome of intra-macrophage and in vitro grown S . Typhimurium .
Salmonella enterica subspecies enterica serovar Typhimurium strain 4/74 was used for all experiments; 4/74 is the prototrophic parent of strain SL1344; the two strains differ by just eight single nucleotide polymorphisms [21 , 35 , 97] . For in vitro RNA isolation , bacterial cells were grown overnight in 5 mL Lennox ( L- ) Broth ( Dataset 1 in S1 Table ) , diluted 1:1000 into 25 mL L-broth , grown at 220 rpm and 37°C in a 250 mL flask until early stationary phase ( ESP , OD600 2 . 0 ) [17] . InSPI2 minimal media was used to induce expression of SPI2 in vitro [30] . For all intracellular studies , RAW 264 . 7 ( ATCC ) murine macrophage cells were maintained in Dulbecco’s Minimal Essential Medium ( DMEM ) supplemented with 5% fetal bovine serum & L-glutamine ( 2 mM final concentration ) and MEM non-essential amino acids without antibiotics , incubated at 37°C in 5% CO2 . All tissue culture reagents were supplied by Lonza . Approximately 109 RAW 264 . 7 macrophage cells were seeded in 175 cm2 tissue culture flasks and infected with complement-opsonized 4/74 cells at a multiplicity of infection ( MOI ) of 100:1 ( bacteria:macrophages ) [14] . Mouse serum ( Charles River Laboratories ) was used for opsonisation , and was stored at −80°C prior to use . After 30 minutes of infection , extracellular bacteria were killed by media containing 100 μg mL−1 gentamicin and incubated for a further 1h . The medium was then changed to ‘maintenance media’ containing 10 μg mL−1 gentamicin for the rest of the experiment . At 8 hours post infection , the infected macrophages were lysed in ice cold ‘RNA stabilisation solution’ [0 . 2% SDS , 19% ethanol , 1% acidic phenol in water] and incubated on ice for 30 minutes [14] to prevent RNA degradation [98 , 99] . The lysates containing intracellular Salmonella were collected , centrifuged and RNA was isolated from the bacterial pellets by a TRIzol-based method that yields both mRNA and sRNA . Briefly , the supernatant was discarded , the pellet was washed three times in 19% ethanol , 1% acidic phenol , re-suspended in the remaining liquid , transferred to a clean 1 . 5 mL Eppendorf tube and centrifuged at 20 , 000 × g at 4°C . The cell pellet was dissolved in 1 mL TRIzol ( Invitrogen ) on ice and transferred into a 2 mL heavy phase lock tube ( 5 Prime ) into which 400 μL of chloroform was added and immediately mixed for 10 seconds . After incubation at room temperature for 2 minutes , the mixture was centrifuged at 20 , 000 × g for 15 minutes . The RNA present in the upper phase was transferred to a fresh tube , and precipitated by adding 450 μL of isopropanol and incubated at room temperature for 30 minutes . The precipitated RNA was then pelleted by centrifugation at 20 , 000 × g for 30 minutes . The pellet was washed in 350 μL ethanol ( 70% ) and centrifuged at 20 , 000 × g for 10 minutes . The washed pellet was air-dried , re-suspended in RNase-free water by shaking ( 900 rpm ) for 5 min in a heating block ( 65°C ) ( Peqlab Thriller ) and stored at −80°C until cDNA library construction . The integrity of RNA was verified using an Agilent Bioanalyzer 2100 and RNA concentrations were measured using the nanodrop spectrophotometer ( Thermo Scientific ) and the Qubit fluorometer ( Invitrogen ) . Control RNA was isolated from bacterial cells grown in L- broth in vitro until ESP ( see above ) . The infection process , RNA preparation , sequencing and analysis were conducted in duplicate to provide independent data from biological replicates . The cDNA library preparation and Illumina sequencing was done by Vertis Biotechnologie AG ( Freising , Germany ) . The total RNA obtained from the biological replicates of intra-macrophage was digested for 45 minutes with DNase I ( Thermo Scientific ) according to the manufacturer’s instructions . Ribosomal RNA was not depleted . RNA samples were fragmented with ultrasound ( 4 pulses of 30 sec at 4°C ) . The 3’ ends of RNA were then subjected to poly ( A ) -tailing using poly ( A ) polymerase . The RNA was then treated with TAP ( Tobacco acid pyrophosphatase ) to remove the pyrophosphate group from the 5’ end , prior to ligation with an RNA adapter . First strand cDNA synthesis was done with an oligo ( dT ) adapter and M-MLV-RNaseH-reverse transcriptase ( Invitrogen ) , following PCR amplification of cDNA using high-fidelity DNA polymerase to a final concentration of approximately 20–30 ng μL-1 . The cDNAs were purified using the Agencourt AMPure XP kit ( Beckman Coulter Genomics ) , and analysed by capillary electrophoresis . The cDNA libraries were sequenced on an Illumina HiSeq 2000 system . For dRNA-seq , prior to cDNA preparation , an aliquot of the RNA samples were enriched for primary transcripts by treating with Terminator 5’-monophosphate dependent exonuclease ( Epicentre; TEX ) [22] . The sequence reads obtained from the different cDNA libraries were mapped against the 4/74 reference genome using the Segemehl software , with accuracy set to 100% [35 , 100] . The mapping coverage was increased by an iterative process that involved the sequential removal of any mismatched nucleotides from the 3’ end , and mapping the read against the 4/74 genome . This process was repeated until the individual sequence reads were accurately mapped to a single location on the chromosome , or until the length dropped below a minimum value of 20 nucleotides [17] . These uniquely-mapped reads were visualised with the Integrated Genome Browser ( IGB ) [101] and Jbrowse [102] . In total , 6 cDNA libraries ( including the biological replicates of RNA-seq , dRNA-seq and RNA-seq of InSPI2 grown ΔssrA & wild type S . Typhimurium 4/74 ) were generated . The expression values of each gene were calculated from the uniquely-mapped reads using the Transcript per Million ( TPM ) approach [20 , 103] . TPM considers the transcripts to represent a mixture of two distributions of expressed and non-expressed genes , and so is ideal for the analysis of bacterial transcriptomic data . As this approach involves normalization to gene size and the total amount of genome-wide transcription , TPM values can be compared between genes and between growth conditions [20 , 103 , 104] . The threshold for expression of a gene was TPM value 10 [17] . Genes with TPM value ≤10 were considered to be “not expressed” . The differential expression of each gene or sRNA within macrophages was calculated against the ESP comparator as a fold change ( macrophage versus ESP ) . The average and standard deviation of RNA-seq data ( TPM values ) was calculated for each gene from the 20 in vitro growth conditions reported earlier [17] . For each gene , the standard deviation was multiplied by five-fold to define a broad expression range that captured all but the most extreme expression levels across the 20 conditions . To identify genes that were specifically up-regulated in macrophage , we selected a strict cut-off of 3-fold more highly expressed than five standard deviations above the mean expression value from the 20 conditions . In other words , macrophage specific gene = TPM > 3 x ( average TPM in 20 conditions + 5σ ) . The genes that passed this cut-off are ‘not significantly expressed’ in any of the 20 in vitro conditions , are up-regulated within macrophages , and are listed in Fig 7A . A strict criterion was used to identify TSS , after visualization with the IGB browser [17] . Novel TSS were defined when a peak was enriched in the dRNA-seq data compared with the RNA-seq data in two biological replicates , and was located at the beginning of an expressed transcript . The Promoter Usage Value ( PUV ) for each TSS was quantified by calculating the TPM for the first 10 nucleotides from the TSS towards the direction of transcription ( from +1 to +10 ) . The PUV values were classified as follows: ( a ) ‘Macrophage independent’ TSS have similar PUV in macrophages and at ESP ( less than 2-fold up- or down-regulated ) ; ( b ) ‘Macrophage up-regulated’ TSS are expressed at least 2-fold higher in macrophages relative to ESP; and ( c ) ‘Macrophage down-regulated’ TSS are expressed at least 2-fold less in macrophages relative to ESP . The 5’ RACE ( rapid amplification of cDNA ends ) was carried out with or without treatment by TAP using DNase I-digested total RNA isolated from the InSPI2 condition [105] . Gene specific amplification was done with the linker-specific primer JVO-0367 and gene specific reverse primers ( Dataset 1 in S1 Table ) . TAP-enriched fragments were excised from an agarose gel , subcloned into a pTOPO vector ( Invitrogen ) and at least three clones were sequenced to validate individual TSS . The sRNA nucleotide sequences from 4/74 were aligned against a set of bacterial genomes belonging to Enterobacteriaceae using GLSEARCH [106] , and identical hits were extracted . The RNA-seq data generated from this study are deposited at the NCBI GEO under the accession numbers GSM1462575 to GSM1462579 , GSM1914919 and can be accessed at http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE59945 . | The burden of Salmonellosis remains unacceptably high throughout the world and control measures have had limited success . Because Salmonella bacteria can be transmitted from the wider environment to animals and humans , the bacteria encounter diverse environments that include food , water , plant surfaces and the extracellular and intracellular phases of infection of eukaryotic hosts . An intricate transcriptional network has evolved to respond to a variety of environmental signals and control the “right time/ right place” expression of virulence genes . To understand how transcription is rewired during intracellular infection , we determined the primary transcriptome of Salmonella enterica serovar Typhimurium within murine macrophages . We report the coding genes , sRNAs and transcriptional start sites that are expressed within macrophages at 8 hours after infection , and use these to infer gene function . We identified gene promoters that are specifically expressed within macrophages and could drive the intracellular delivery of antigens by S . Typhimurium vaccine strains . These data contribute to our understanding of the mechanisms used by Salmonella to regulate virulence gene expression whilst replicating inside mammalian cells . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | RNA-seq Brings New Insights to the Intra-Macrophage Transcriptome of Salmonella Typhimurium |
Because DNA packaging in nucleosomes modulates its accessibility to transcription factors ( TFs ) , unraveling the causal determinants of nucleosome positioning is of great importance to understanding gene regulation . Although there is evidence that intrinsic sequence specificity contributes to nucleosome positioning , the extent to which other factors contribute to nucleosome positioning is currently highly debated . Here we obtained both in vivo and in vitro reference maps of positions that are either consistently covered or free of nucleosomes across multiple experimental data-sets in Saccharomyces cerevisiae . We then systematically quantified the contribution of TF binding to nucleosome positiong using a rigorous statistical mechanics model in which TFs compete with nucleosomes for binding DNA . Our results reconcile previous seemingly conflicting results on the determinants of nucleosome positioning and provide a quantitative explanation for the difference between in vivo and in vitro positioning . On a genome-wide scale , nucleosome positioning is dominated by the phasing of nucleosome arrays over gene bodies , and their positioning is mainly determined by the intrinsic sequence preferences of nucleosomes . In contrast , larger nucleosome free regions in promoters , which likely have a much more significant impact on gene expression , are determined mainly by TF binding . Interestingly , of the 158 yeast TFs included in our modeling , we find that only 10–20 significantly contribute to inducing nucleosome-free regions , and these TFs are highly enriched for having direct interations with chromatin remodelers . Together our results imply that nucleosome free regions in yeast promoters results from the binding of a specific class of TFs that recruit chromatin remodelers .
The genomes of all eukaryotic organisms are packaged into nucleosomes , which are the fundamental units of chromatin , each composed of approximately 147 base pairs ( bp ) of DNA wrapped around a histone octamer . Recent developments in technologies for measuring chromatin marks by chromatin immunoprecipitation ( ChIP ) on microarrays ( ChIP-Chip ) or by sequencing ( ChIP-seq ) have enabled the construction of genome-wide maps of nucleosome positions and modifications at high resolution across various conditions . These experimental data have revealed that nucleosomes are not uniformly distributed across the genome but rather that transcription start and termination sites are relatively depleted of nucleosomes [1] , [2] . Furthermore , nucleosome positioning has been shown to vary across physiological conditions [3] . It has long been accepted that nucleosomes have intrinsic sequence preferences which influence nucleosome positioning , e . g . [4]–[6] . At the same time , it has also long been known that barriers in the DNA can cause nucleosomes to be ‘statistically positioned’ relative to such barriers , introducing a periodic pattern of nucleosome occupancy on both sides of the barrier [7] . Given the fact that nucleosomes may cover more than of the genome [1] , it is therefore also conceivable that a relatively small number of barriers on the DNA , in combination with statistical positioning relative to these barriers , determines most of the observed nucleosome positioning . For example , recent work suggests that nucleosome occupancy patterns around TSSs could at least partly be explained by such statistical positioning [8] . Probably the most obvious class of candidate molecules that could introduce condition-specific barriers on the DNA are sequence-specific transcription factors ( TFs ) . Indeed , for some specific promoters in S . cerevisiae it has been established that binding of TFs is a major determinant of nucleosome positioning in the promoter region , e . g . [9]–[11] . Moreover , the resulting nucleosome positioning has major effects on gene regulation from these promoters . In addition , for a few TFs it has been established that their binding induces local nucleosome exclusion genome-wide [1] , [12]–[14] . Although it is thus clear that both intrinsic sequence preferences of nucleosomes and competitive binding of other DNA binding factors play a role in nucleosome positioning , the relative importance of these factors have come under intense debate in recent years . For example , it has been proposed that the positioning of nucleosomes , in particular in S . cerevisiae , is mainly determined by intrinsic sequence preference of the nucleosomes , i . e . [15] . In this view , nucleosomes are mainly positioned by a ‘code’ in the DNA sequence and the accessibility of the DNA to TFs is downstream of this sequence-guided nucleosome positioning . However , these conclusions were challenged by several studies which suggested nucleosome sequence specificity can only explain a modest fraction of nucleosome positioning , and that statistical positioning likely also plays an important role [1] , [2] , [16] , [17] . More recently , several groups have undertaken further experimental investigations into this question , in particular by experimentally comparing nucleosome positioning in vivo and in vitro [18] , [19] . Although there is general agreement that these experimental studies confirmed that both intrinsic sequence preferences and the competitive binding of TFs play a role in nucleosome positioning , different authors came to strikingly different , and often seemingly contradictory conclusions regarding which of these factors play a dominant role [20]–[24] . It is thus clear that , rather than lacking sufficient experimental data , the current challenge in furthering our understanding of the determinants of nucleosome positioning lies in the quantitative interpretation of this data . Here we show that , by analyzing existing experimental data in combination with rigorous computational modeling , important novel insights can be gained that reconcile previous seemingly contradictory observations , and that suggest a new picture of the mechanisms regulating nucleosome positions . In particular , we use a biophysical model to quantitatively assess the role of TFs in determining nucleosome positioning in S . cerevisiae , to assess which aspects of nucleosome positioning TFs contribute to most , and to identify whether there are subsets of TFs that play a predominant roles in this process . S . cerevisiae is a particularly attractive system for such an analysis because extensive nucleosome positioning data are available , and because it is essentially the only organism in which sequence-specificities are available for the very large majority of TFs . Rather than assuming that intrinsic sequence preferences determine nucleosome positioning and that TF binding occurs preferentially at those regions not covered by nucleosomes , or vice versa , assuming that TF binding sets boundaries in the DNA against which nucleosomes are statistically positioned , in our model the TF binding and nucleosome positioning patterns are determined by a dynamic competition of all TFs and nucleosomes for binding to the DNA . Our model incorporates both the sequence preferences of the nucleosomes and of all TFs in a thermodynamic setting , and rigorously calculates the resulting equilibrium occupancies genome-wide as a function of the concentrations of all TFs and the nucleosomes . Using this model in combination with experimental data we find that TF binding makes a substantial contribution to nucleosome positioning but only at a specific subset of genomic positions . In particular , the linker regions between nucleosomes can be clearly divided into two classes based on their size: the large majority of linkers is small ( bp ) and occurs within large nucleosome arrays in gene bodies , whereas a minority of linkers is large ( bp ) and occurs predominantly in promoters . Our results show that the phasing of the small linkers within nucleosome arrays , and thereby the majority of nucleosome positioning genome-wide , is mainly determined by sequence preferences of nucleosomes . In contrast , the larger nucleosome free regions in promoters , which are likely most relevant for effects on gene expression , are mainly determined by competitive binding of TFs . By applying our model to data on nucleosome positioning in vitro we also confirm that the ability of TFs to explain nucleosome positioning in promoters is restricted to in vivo data . Thus , our model provides a quantitative and mechanistic explanation for the observed discrepancies between in vivo and in vitro nucleosome positioning . Most strikingly , our results also show that , rather than all TFs contributing roughly equally to the competition with nucleosomes , the effect of TFs on nucleosome positioning is restricted to a relatively small set of about TFs . Although one might expect that these TFs are simply the highest expressed TFs with the largest number of TFBSs genome-wide in the conditions in which the experiments were performed , we find this not to be the case . Instead , we find that these TFs are highly enriched for having known protein-protein interactions with chromatin remodeling complexes , histones , and chromatin modification enzymes . Thus , the mechanistic picture suggested by our results is that there is a specific class of TFs who , upon binding to the DNA , recruit chromatin modifiers that then mediate local expulsion of nucleosomes .
To rigorously investigate the competition between TFs and nucleosomes for binding to DNA , and the role of TFs in nucleosome positioning , we take a statistical mechanics approach in which we explicitly consider all possible non-overlapping binding configurations to the genome for nucleosomes and a large set of TFs , assigning a probability to each configuration using standard Boltzmann-Gibbs statistics . The basic approach , which uses dynamic programming to efficiently sum over all possible binding configurations , has been used in computational methods for analysis of transcription regulation for over a decade , e . g . [17] , [25]–[28] , and has been used more recently to specifically investigate the effect of competitive binding of nucleosomes and TFs [29] , [30] . Here we use this approach to comprehensively investigate the role of TFs in determining nucleosome positioning . We employ an unprecendented complete set of TF binding models , we investigate the dependence on the concentrations of these TFs , and we also introduce tunable sequence-specificities for all TFs and nucleosomes . The model is explained in detail in the Materials and Methods . Briefly , each TF is assumed to bind DNA segments of a fixed length and , for any length- DNA segment , a binding energy is determined . The energies are calculated from a weight matrix representation of the TF's binding sites [31] and involve a tunable scale parameter which controls the sequence-specificity of the TF . To obtain energy matrices for the large majority of sequence-specific TFs in S . cerevisiae we used a collection of WMs that we curated previously [32] and that are based on a combination of ChIP-chip and in vitro binding data . Notably , while the WMs allow us to determine how the binding energy ( measured in units ) varies across positions in the genome for each TF , the WMs do not allow us to determine the sequence-independent contribution to binding energy , i . e . the overall ‘stickines’ of each TF for DNA . To compare binding energies across TFs we set the sequence-independent contribution to the binding energy such that all TFs have equal overall affinity for the DNA ( see Materials and Methods ) . Of the computational work done on nucleosome positioning , probably most effort has been invested in developing models for nucleosome sequence-specificity based on data from both in vivo and in vitro nucleosome binding , e . g . [15] , [18] . Exploiting analytical results from statistical mechanics , Locke et al . [24] rigorously inferred the energies of nucleosome binding from high-throughput data and used these to evaluate several models of different complexity for the sequence specificities of nucleosomes . The results from this study suggested that the sequence specificity of nucleosomes can be captured by fairly simple models . As we discuss below , our own analysis suggests that the performance of different models of nucleosome sequence specificity depends on the precise data-set and performance evaluation method used , but that all models make highly correlated predictions ( Figure 1A ) . Of the models analyzed , the model of [18] gave robustly high performance across data-sets and we use this model in our study . In particular , we assume that nucleosomes bind to DNA segments of nucleotides and determine an energy of binding for any length segment using a generalization of the model of [18] , involving a scale parameter that controls the sequence specificity of the nucleosomes , analogous to the scale parameters for the TFs ( see Materials and Methods ) . The parameter allows us to investigate the effect of enhancing or decreasing the nucleosome sequence specificity . For example , when setting , the variation in nucleosome binding energies across different sequences is reduced to of the energy variations predicted by the model of [18] . As mentioned above , the model assumes that any DNA segment can only be bound by a single TF or a nucleosome at a time . Although it is likely that there are exceptions to this simplification , it is generally accepted that TFs and nucleosomes compete for binding to DNA . In absence of specific information as to which TFs compete with nucleosomes and which can co-bind with nucleosomes , we make the simplifying assumption that all TFs compete with nucleosomes , as has been done previously by others [29] , [30] . Like previous approaches , e . g . [8] , [15] , [22] , [29] , our model also assumes that the average occupancy profiles across a population of cells are well approximated by their thermodynamic equilibrium averages . Notably , given that there are many ATP-driven processes that cause nucleosome turnover and displacement by chromatin remodelers , it is not a priori clear that this equilibrium assumption holds . Ours and previous computational approaches thus essentially assume that these ATP-driven processes act mainly to affect kinetics , i . e . to allow nucleosomes to resample their positions , without systematically biasing their positioning . Some recent evidence appears to support this assumption [33] . The model considers all possible non-overlapping configurations of TFs and nucleosomes bound along the genome . For each configuration , a total energy is calculated . This energy depends on the concentrations of nucleosomes and all TFs , which we collectively denote as , and also on all energy scale factors that determine sequence-specificity ( Materials and Methods ) . The probability to find a cell in configuration is then given by the standard Boltzmann-Gibbs formalism as ( 1 ) where is the inverse temperature , is the partition sum , and we have explicitly indicated that these probabilities depend on the concentrations and scale factors . As explained in Materials and Methods , both the partition sum and the fractions of the time each TF is bound at each genomic position can be calculated efficiently using standard dynamic programming techniques . In summary , given a set of input concentrations for all TFs and nucleosomes , the model efficiently calculates the equilibrium binding frequencies of all TFs and nucleosomes across the entire genome . Note that , because all TFs and nucleosomes are in competition for binding to the DNA , the occupancy of any factor to a sequence segment of the genome in principle depends , not only on the concentration of this factor and its affinity to the sequence segment , but on the concentrations of all other factors and their affinities to all other locations in the genome . Thus , the TF and nucleosome occupancy profiles across the genome can be changed by varying the concentrations and scale factors . In particular , these parameters can be optimized to maximize the agreement with experimentally determined nucleosome occupancy profiles . Many experimental studies have been carried out to map nucleosome positions in eukaryotic species , e . g . [34]–[37] , and in Saccharomyces cerevisiae in particular , e . g . [1]–[3] , [18] , [19] , [38] , [39] , so that several data-sets of nucleosome positions in S . cerevisiae are available . In order to determine how to meaningfully compare computational predictions with these experimental data , we first performed a comparative analysis of several experimental data sets . Patterns of nucleosome positioning that are typically highlighted in publications , such as the nucleosome-depleted regions upstream of the transcription start sites ( TSSs ) and well-positioned nucleosomes immediately downstream of TSS , involve genome-wide averages of nucleosome occupancy across a class of positions . Such average patterns are robust to fluctuations and are shared by all data-sets . Previous works have assessed the performance of models of nucleosome sequence specificity by determining both the predicted and experimentally observed nucleosome occupancies across individual regions of the genome , and by calculating the Pearson correlation of these nucleosome occupancy profiles . To assess the validity of such an approach , we calculated Pearson correlations between observed occupancy profiles of several experimental data-sets ( both in vivo and in vitro ) as well as several models of nucleosome sequence specificity ( Figure 1A ) . This shows that , unfortunately , the occupancy profiles correlate only weakly across different experimental data-sets , with Pearson correlation coefficients typically ranging from to for in vivo data-sets , and only marginally higher for in vitro data-sets . This large variability across data-sets may to some extent be due to biases of the technological platforms . For example , it is well known that the nucleotide composition and propensity to form secondary structures of the reads can systematically bias the read counts in ChIP-seq by more than 10-fold [20] , [40] . Variations in details of the ChIP protocol are likely also responsible for some of the variation across data-sets , and previous studies have indicated that MNase digestion bias may also systematically affect nucleosome positioning data [23] , [24] . Since all experiments were performed in YPD , true biological variation is likely only a minor source of variation in these data . In contrast to the experimental data , the occupancy profiles predicted by the different computational models are all highly correlated . Moreover , the correlations across models for a given data-set vary much less than the correlations for a given method vary across data-sets . For example , all models consistently perform better on in vitro than on in vivo data . Among the in vivo data-sets , all methods perform by far best on the in vivo data of Kaplan et al . [18] ( which is also far more correlated with in vitro data than any other in vivo data-set ) and far worst on the in vivo data of Shivaswamy et al . [3] . Thus , comparison of different models with existing data supports the conclusions of [24] that different models of nucleosome-specificity perform similarly in explaining nucleosome positioning . Since the model of Kaplan et al . [18] exhibits highest performance for the majority of in vivo and in vitro data-sets , we chose to use this model in our analysis . However , the weak correlation of nucleosome occupancy profiles across data-sets shows that assessing the performance of computational predictions by directly comparing predicted and observed nucleosome occupancies is highly problematic . A meaningful comparison of computational models requires that one first extracts those features of the nucleosome positioning that are reproducible across experimental data-sets . In contrast to the absolute value of the ChIP signal , we observed that the positions of local maxima and minima in nucleosome occupancy are much better reproduced across data-sets . This reproducibility of the ‘peaks and troughs’ in the nucleosome occupancy profile has been observed previously [41] , and has been used to create a reference set of ‘nucleosome’ and ‘linker’ segments . In this procedure , local maxima and minima are used to annotate nucleosomes and linkers in each data-set . These annotations are then intersected , with reference nucleosomes placed at the consensus positions of regions annotated as nucleosomes in all data-sets , and reference linkers the regions free of nucleosomes in all annotations . That the positions of annotated nucleosomes are highly reproducible across data-sets , especially compared to raw coverage and compared to nucleosome maps based on randomized data , is illustrated in Figure 1B . The annotated positions of individual nucleosomes across different data-sets typically vary by less than base pairs from the reference position ( blue curve in Figure 1B ) and the vast majority of annotated nucleosome positions vary by less than bp from the reference position . In contrast , on randomized data positions of annotated nucleosomes typically vary by roughly bp from the reference position ( dotted curve in Figure 1B ) . In summary , although ideally we would like to test whether computational models can predict relative nucleosome occupancies across the genome , it is not possible to meaningfully perform such an assessment given the variability observed in the experimental data . We thus evaluate the performance of different models by assessing their ability to predict nucleosome and linkers that occur consistently across different data-sets . We use the reference set annotated by [41] consisting of roughly annotated linker regions and annotated nucleosomes , that together cover about of the genome , to assess the performance of the model in predicting in vivo nucleosome positioning . In addition , we have applied a similar annotation procedure ( Materials and Methods ) to produce a reference set of nucleosomes and linkers from in vitro data-sets , which we use to assess the performance of the model in predicting nucleosome positioning in vitro . To assess the model's performance we compare the predicted nucleosome coverage at annotated linker and nucleosome segments . That is , instead of comparing the predicted and observed absolute occupancies , we assess the model's ability to predict local maxima and minima in nucleosome occupancy , that occur consistently across data-sets . As described in Materials and Methods , based on the predicted nucleosome coverage , we classify each segment as either nucleosome or linker , and then calculate the mutual information between the predicted and experimentally measured classification . Finally , we normalize this mutual information by the entropy of the experimental classification to obtain the fraction of information that is captured by the model's predictions , i . e . runs from ( random predictions ) to ( perfect predictions ) . An value of means that the model captures of all the information needed to specificy which of the genomic segments correspond to nucleosomes and which to linkers . We will refer as the ‘quality score’ . As mutual information is the fundamental measure of dependence between two distributions [42] , [43] , we consider the quality score the most rigorous quantification of model performance . However , as we show below , highly similar results are obtained with other performance measures that are popular in machine learning , such as area under the ROC curve ( AUC ) . We first tested what quality score can be obtained by the intrinsic sequence specificity of the nucleosomes , i . e . leaving all TFs out of the model , and how the quality of the fit depends on the sequence specificity of the nucleosomes . Figure 2A shows the quality scores that are obtained for different scale factors on nucleosome sequence specificity ( with representing no sequence preference whatsoever and representing the specificity used in Kaplan et al . [18] ) . The optimal fit is obtained for , which corresponds to significantly lower nucleosome sequence specificity than those used in Kaplan et al . [18] . That is , for the model of [18] , the standard deviation of nucleosome binding energies is approximately across the genome ( ) , whereas we observe optimal fits for roughly -fold lower variations in binding energies ( roughly ) . Moreover , the quality score depends weakly on and becomes small only for extremely small sequence specificities . These results may seem contradictory , given that the sequence-specificity model of Kaplan et al . was developed specifically with the aim of explaining nucleosome positioning . However , Kaplan et al . optimized the overall Pearson correlation between predicted and observed nucleosome coverage , which depends strongly on the variation in absolute nucleosome occupancies . In contrast , the quality score depends mainly on the locations of local maxima and minima in the occupancy , and much less on the absolute amount of variation in nucleosome occupancy . To investigate this further , we compared the distribution of nucleosome occupancies for the model with different values of with the distribution of nucleosome occupancies for the model of Kaplan et al . and the experimentally observed distribution of nucleosome occupancies for the data of Lee et al . [1] ( Materials and Methods , and note that very similar distributions are obtained from other experimental data-sets; Figure S1 in Text S1 ) . As shown in Figure 2B , the model of Kaplan et al . [18] predicts an overall nucleosome coverage that is dramatically lower than our fits , i . e . with a median nucleosome coverage of about . Such a coverage distribution is strongly at odds with the experimental data which shows that , rather than , about of the genome is covered by nucleosomes , e . g . [1] , [3] , [44] , [45] . It is likely that the unrealistically low nucleosome occupancy of Kaplan et al . [18] is an artefact of optimizing the Pearson correlation in nucleosome coverage , since this objective function favors high variance in predicted nucleosome coverage , and does not penalize the mismatch in the average nucleosome coverage . For our model , the coverage distribution indeed strongly depends on the nucleosome specificity . Strikingly , by far the best fit between the observed and predicted coverage distribution occurs precisely at the specificity that maximizes our quality score ( i . e . at ) . This demonstrates that , in contrast to the predictions of Kaplan et al . [18] , our fits produce realistic nucleosome coverage profiles , in spite of not specifically optimizing these coverage profiles . In fact , at the optimal nucleosome specificity , the predicted and experimentally observed nucleosome coverage distribution is virtually identical for the of base pairs in the genome with highest nucleosome coverage ( blue and red curves in Figure 2B ) . The main deviation between model and experimental data is that the model fails to predict regions with low nucleosome coverage that are observed experimentally . Indeed , as we will see below , whereas the model correctly predicts almost all nucleosomes , the model fails to correctly predict a substantial fraction of linker regions as nucleosome free . In summary , optimizing the quality score produces much more realistic fits to the nucleosome coverage distribution than previous models , and shows that the best fits are obtained with only weak nucleosome sequence-specificity . We next investigated to what extent competition with TFs improves the predicted nucleosome positioning . We first considered models in which , besides the nucleosomes , there is only a single TF . For each of these models we fitted the parameters ( i . e . the concentrations and sequence specificity of both nucleosomes and the TF ) using simulated annealing , and calculated the quality score obtained with this model using cross-validation ( Materials and Methods ) . We ranked TFs by the -statistic they obtained in cross-validation ( Materials and Methods ) , and then investigated what quality scores can be obtained using the top , , and top TFs , refitting all concentrations and sequence specificity parameters . We find that adding the TFs clearly increases the quality of the predictions on the test-sets , although the improvement is relatively small , i . e . from to , Figure 3A . Given this modest increase in and the large number of parameters involved when including many TFs in parallel , one may wonder whether these results are affected by overfitting . However , as shown in Figure S2 in Text S1 , the observed scores on train and test sets are essentially identical . In addition , adding the TFs to the model further improves the match between the observed and predicted nucleosome occupancy distribution ( Figure S1 in Text S1 ) . As already observed in [41] , the length distribution of linkers is bimodal . The large majority of linkers is short , around on average bps in length , corresponding to short linkers within arrays of nucleosomes . There is a second class , corresponding to roughly of all annotated linkers , that are much longer , i . e . each more than bps long . We will refer to these longer linkers as ‘nucleosome free regions’ ( NFRs ) . We next asked whether TFs contribute more to explaining the positioning of the short linkers or the longer NFRs . Moreover , as TFs are expected to bind predominantly to promoter regions , we also investigated whether the contribution of the TFs to explaining nucleosome positioning is most significant in promoters ( defined as running from bp upstream to bp downstream of TSS ) . We find that , generally , inclusion of the TFs leads to a substantially larger increase in performance for promoter regions , and TFs contribute much more to explaining NFRs than explaining small linkers ( Figure S3 in Text S1 ) . In particular , considering NFRs and nucleosomes in promoter regions , inclusion of TFs almost doubles the quality score , i . e . from to , Figure 3A , red bars . As an aside , we note that these observations do not depend on assessing the model's performance by the quality score . As shown in Figure S4 in Text S1 , we find essentially the same results when assessing the model's performance using ROC curves , and the area under the curve ( AUC ) is almost perfectly correlated ( ) with the quality score . It is also noteworthy that , both when predicting all linkers genome-wide or NFRs in promoters , even though up to TFs can be incorporated , the model essentially reaches its optimal performance after adding the first TFs . We investigate this in more detail below . It thus appears that TFs contribute not so much to explaining positioned nucleosomes , but rather explain the location of longer NFRs , especially in promoters . Further supporting this observation , the rightmost pair of bars in Figure 3A shows the performance of the model including all TFs but with nucleosome sequence specificity removed , i . e . . We see that removing nucleosome sequence specificity only modestly affects the ability of the model to predict NFRs in promoters . In contrast , the performance on predicting all linkers genome-wide drops significantly when nucleosome sequence specificity is removed , even falling clearly below the performance of the model without TFs . This is further confirmed by closer examination of the errors that the fitted models make ( Figure 3B ) . For all models , the large majority of nucleosomes is correctly predicted and the fraction of correctly predicted nucleosomes is most strongly affected by removing the sequence specificity of the nucleosomes , i . e . from correct for the model with only nucleosome sequence specificity to for the model with all TFs and no nucleosome specificity . The fraction of correctly predicted linkers is much smaller , e . g slightly below for the model without TFs . Adding the TFs to the model consistently increases the fraction of correctly predicted linkers , and this increase does not require nucleosome sequence specificity . When considering all linkers genome-wide , the increase in correctly predicted linkers is relatively modest , i . e . from to . However , for NFRs in promoters the fraction of correctly predicted NFRs increases from to around . In summary , correctly predicting the phasing of nucleosome arrays over gene bodies crucially depends on nucleosome sequence specificity and is only weakly affected by including TFs , whereas correctly predicting NFRs is strongly dependent on inclusion of the TFs and is almost independent of nucleosome sequence specificity . To characterize the biophysical properties of the fitted model we first determined the overall statistics of nucleosome and TF occupancies ( Figure 4A ) . Nucleosomes cover more than of the genome , and most of the remaining regions of the genome are uncovered , with all TFs combined covering less than of the genome . The top TFs with the highest genomic coverage occupy between and of the genome , corresponding to roughly and binding sites genome-wide . For the nucleosomes and the top TFs with highest genomic coverage in the fitted model we also determined the mean and standard-deviation of the binding energies at their binding sites , and the entropy of the distribution of binding probabilities per site ( Materials and Methods ) . The latter quantity is low whenever the TF's coverage results from strong sites with high frequencies of binding , and is high when the TF's coverage comes from a large set of weak sites with lower binding frequencies . The results ( Figure 4 ) show , first of all , that the binding sites of nucleosomes have both the lowest binding energy and the lowest variation in binding energies , i . e . they are the least sequence specific . Interestingly , the top TFs clearly fall into classes: a set of TFs ( ABF1 , REB1 , ORC1 , and RSC30 ) that are highly sequence specific and have strong binding sites , and a class of much less sequence specific TFs ( PHO2 , NHP6A , etcetera ) that bind at a much larger number of weaker sites . As has been observed previously , e . g . [1] , [2] , averaged nucleosome coverage profiles show a characteristic pattern relative to the starts of genes with a nucleosome depleted region immediately upstream of TSS , followed by a well-positioned nucleosome immediately downstream of TSS and a periodic pattern of nucleosome coverage downstream into the gene body . Although the nucleosome sequence specificity by itself , i . e . without including TFs , reproduces some of this pattern at the 5′ end of genes ( Figure 5A ) , the observed nucleosome depleted region and the oscillatory pattern into the gene body is much weaker than observed experimentally . As an additional test of the validity of our model , we checked whether inclusion of the TFs improves this average coverage profile relative to gene starts and ends . We find that adding TFs to the model significantly improves the match between the theoretically predicted and experimentally observed nucleosome coverage pattern at the 5′ ends of genes ( Figure 5A ) . It is noteworthy that the nucleosome-depleted region immediately upstream of TSS coincides with a peak in the overall predicted binding of TFs ( Figure S5C in Text S1 ) , further illustrating the role of TFs in establishing nucleosome depletion in these regions . A local peak in TF binding is also predicted immediately downstream of the 3′ ends of genes ( Figure S5D in Text S1 ) . Although at the 3′ ends of genes , the inclusion of the TFs also improves the match between the theoretical predictions and the experimentally observed nucleosome coverage , the experimental data and predictions clearly disagree ( Figure 5B ) . First , the width of the experimentally observed NFR is twice as big as the width of the predicted NFR . Second , the oscillations exhibited by the experimentally-determined distribution are not as pronounced as predicted by the model . This lack of a match can likely be attributed to the role of RNA polymerase . Our model considers only TFs and , in particular , does not consider the effects of binding of general transcription factors and RNA polymerase . Experimental data on the positioning of the largest subunit of Pol II - Rpo21 , and the general transcription factor Sua7 shows that these factors localize at 3′ ends of genes [46] , suggesting that they may contribute to the nucleosome free region observed at the 3′ ends of genes ( Figure S6 in Text S1 ) . This is further supported by the analysis in [47] , which shows that rapid removal of Polymerase from 3′ end regions increases local nucleosome occupancy . As another validation of the model , we investigated whether the predicted TF binding matches experimental observations . For example , we compared the intergenic regions predicted to be targeted by the TFs Abf1 , Reb1 , and Sum1 , with the observed target intergenic regions according ot the ChIP-chip data of [48] . This shows that , in spite of the fact that the model was only optimized to fit nucleosome positioning , the fitted model also accurately predicts which regions are targeted by these TFs ( Figure S7 in Text S1 ) . It is important to stress that , although we assess the model's performance by these global statistics , it predicts the precise locations of individual nucleosomes , NFRs , and TF binding sites . The full genome-wide nucleosome and TF coverage predictions obtained with the model including the TFs are made available through our SwissRegulon server www . swissregulon . unibas . ch/ozonov , allowing users to investigate in detail which NFRs at which promoters are explained by the binding of particular TFs . To illustrate the detailed comparison of the model's predictions and observed nucleosome occupancies Figure 6 shows the measured nucleosome coverage , the predictions of the model with and without TFs , and the predicted coverage of TFs , in two genomic regions . As the figure shows , whereas the locations of small peaks and troughs in occupancy across arrays of nucleosomes are reasonably well captured by nucleosome sequence specificity alone , competition with TF binding is needed to explain the occurrence of larger nucleosome free regions , which occur predominantly in promoters . Importantly , it is likely precisely this latter class of regions that are crucial for the effects of nucleosome positioning on gene expression . However , this detailed comparison also reveals that , whereas the locations of TF binding typically matches the centers of observed NFRs , the predicted shape of these NFRs differs considerably between the model and the experimental observations . In particular , NFRs tend to be much narrower in the model's predictions than in the experimental data . This suggests that , although TF binding determines the genomic location where nucleosome depletion is observed , the observed nucleosome exclusion is more substantial than predicted from the steric hindrance between TFs and nucleosomes . This suggests that TF binding may recruit aditional factors involved in nucleosome exclusion . We return to this observation below . Our model incorporates the role of TFs through a simple competition for binding DNA and one might thus naively expect that all TFs that are expressed in YPD would contribute similarly to explaining nucleosome positioning , maybe in proportion to the number of their binding sites in the genome . However , we observed above ( Figure 3A ) that when consecutively adding more TFs to the model , the performance already assymptotes after TFs . This could be due to redundancies in the contributions of the TFs , i . e . if sites for different TFs cluster in particular genomic regions , then binding by only a subset of the TFs will suffice to explain the occurrence of NFRs in these regions , and adding more TFs to the model would not further improve performance . Alternatively , it may be that there is a specific class of TFs that contribute much more to nucleosome positioning than other TFs . To investigate this , we used cross-validation on independent training and test sets to assess , for each of the TFs , whether a model containing only nucleosomes and the single TF statistically significantly outperforms the model with only nucleosome specificity , quantifying the significance by a -statistic ( Materials and Methods ) . Figure 7A shows the distribution of -statistics obtained for the TFs ( blue dots ) , together with the distribution of -statistics expected by chance ( brown dotted curve ) . As the figure shows , only of the TFs significantly improve the predictions , indicating that there is indeed a specific class of TFs that dominate in explaining NFRs . Indeed , the large majority of all other TFs obtain quality scores on the test sets that are either the same or worse than the model without any TFs ( Figure S8 in Text S1 ) . As another validation , we checked whether the ability of this subset of TFs to explain nucleosome positioning is a specific property of the sequence specificities of yeast's TFs . That is , it is in principle conceivable that among any set of WMs with similar information content and sequence composition , a few will be able to help explain nucleosome positioning . To test this we constructed a set of synthetic WMs by randomly shuffling the columns of the original WMs , and fitted models with these TFs in exact analogy to our fits with the original WMs . As shown in Figure 7A ( green dots ) , none of the shuffled WMs perform better than expected by chance , confirming that the ability to explain nucleosome positioning is unique to the specific set of yeast WMs that we identified . As a final test , we also evaluated whether the real WMs can explain the nucleosome positioning that is observed in vitro ( Materials and Methods ) . On the one hand , since no TFs are present in the conditions at which the in vitro experiments are performed , the TFs should in principle not contribute to nucleosome positioning . On the other hand , as the raw in vivo and in vitro occupancies are significantly correlated ( Figure 1A ) , one might expect that the TF WMs can still positively contribute to explaining in vitro nucleosome positioning . It is thus striking that none of the real yeast WMs performs better than expected by chance in explaining in vitro nucleosome positioning ( Figure 7A , red dots ) , i . e . including TFs does not help explaining in vitro nucleosome positioning . This shows that the actions of a specific set of TFs are crucial for explaining the differences between in vivo and in vitro nucleosome occupancies . Figure 7B lists the top TFs and shows their quality scores on the test sets ( results for all TFs are shown in Table S1 ) . The fact that only around TFs contribute significantly to nucleosome positioning raises the question of what distinguishes these TFs from the others and we investigated a number of hypotheses . One might hypothesize that the top TFs are simply those that are highest expressed in YPD , or those which occupy most sites genome-wide . However , expression data indicates that these TFs are not particularly highly expressed in YPD compared to other TFs ( Figure S9 in Text S1 , data from [49] ) . Consistent with this , the genome-wide number of binding sites , as observed in genome-wide ChIP-chip experiments ( Figure S10 in Text S1 ) , is not generally higher for these TFs . Thus , the role of these TFs in nucleosome positioning is not simply the result of increased binding or expression in YPD . Notably , for a considerable number of TFs our model predicts essentially no binding sites , and not all of these TFs are low expressed in YPD . It is conceivable that the low number of predicted sites for these TFs indicates that these TFs do not compete with nucleosomes but can bind to DNA which is wrapped around a nucleosome . We also investigated whether the top TFs have particularly high or low information content and found that this is not the case ( Figure S11 in Text S1 ) . However , when we manually inspected the functional annotation of the top TFs , we noticed that roughly half of these TFs are known to be involved in chromatin remodeling ( Table S1 ) . Since , among our TFs only have been previously implicated in chromatin remodeling or nucleosome positioning , this amounts to a highly significant enrichment among our top TFs ( p-value , see Materials and Methods ) . This suggested that the top TFs may be characterized by interacting directly with chromatin modification machinery . To investigate this more systematically we investigated the occurrence of known direct protein-protein interactions between TFs and ( see Materials and Methods ) . As detailed in Table 1 , we find that our top TFs are highly significantly enriched for direct protein-protein interactions with all categories , showing the strongest enrichment for interacting directly with proteins in chromatin remodeling complexes . These results strongly suggest that our top TFs are characterized by their ability to locally recruit chromatin modifiers . The fact that only those TFs that interact directly with chromatin modifiers contribute significantly to explaining NFRs has interesting implications for the mechanisms of nucleosome positioning . It suggests that the creation of NFRs depends on the actions of chromatin modifiers whose activities lead to local expulsion of nucleosomes from the DNA . That is , the mechanistic picture that emerges is that , initially , the competition between TFs and nucleosomes for binding DNA , as implemented in our model , determines where TFs will end up binding DNA . Subsequently , in those places where TFs from the specific class that can recruit chromatin modifiers are bound , the recruitment of these modifiers will lead to local expulsion of the nucleosomes , leaving a larger region depleted of nucleosomes . This mechanistic picture also explains our previous observation that the predicted NFRs tend to be much narrower than those observed in the data .
It is generally accepted that the packaging of DNA by nucleosomes in eukaryotes can modulate the accessibility of TFs to their cognate sites and thereby have major effects on gene regulation . In recent years there have been significant experimental efforts to determine nucleosome positioning patterns genome-wide , and to analyzing how these nucleosome-positioning patterns are established . As we discussed in the introduction , there has been a considerable debate as to whether nucleosome positioning in Saccharomyces cerevisiae is predominantly controlled by intrinsic sequence specificity of the nucleosomes , or that statistical positioning around barriers introduced by other DNA binding factors is more important for nucleosome positioning , and different researchers have presented seemingly contradictory results in this regard . We feel that these apparent contradictions may be reconciled by the results presented here . The large majority of annotated nucleosomes and linkers genome-wide concern the phasing of short linkers within dense arrays of nucleosomes , mainly inside genes . We find that the positioning of these nucleosomes and short linkers crucially depends on the sequence specificity of the nucleosomes , and that TFs contribute relatively little to their positioning . Therefore , predicting all linkers and nucleosomes on a genome-wide scale , the sequence specificity of the nucleosomes provides the main contribution to explaining their positions . In contrast , we find that nucleosome specificity contributes little to explaining larger nucleosome free regions , especially those within promoter regions . As our modeling shows , NFRs in promoters are predominantly explained by the DNA binding of a specific class of transcription factors . Thus , while genome-wide locations of nucleosomes and short linkers are predominantly determined by nucleosome sequence-specificity , the large nucleosome free regions in promoters that likely contribute much more significantly to gene regulation , are determined mainly through the competitive binding of TFs . Importantly , the fact that competition with TFs can not help explain the in vitro nucleosome positioning shows that the contributions of the TFs is restricted to in vivo positioning . Thus , the competitive binding of TFs provides a quantitative and mechanistic explanation for the differences between in vivo and in vitro nucleosome occupancies . That nucleosome free regions in promoters result from a competition between TF and nucleosome binding is supported by a number of recent studies of individual promoters , e . g . [9]–[11] , [50] . In these studies the interplay of TF and nucleosome binding determines positions of NFRs and the resulting accessibility pattern has major consequences for gene expression . Our results suggest that this mechanism is not restricted to a few promoters , but is the typical situation genome-wide . Thus , whereas nucleosome sequence specificity does have a major impact on genome-wide nucleosome positioning , precisely those aspects of nucleosome positioning that have most impact on gene regulation are rather determined by the competition between nucleosomes and TF binding . Another major result from our study is that less than of the TFs that we analyzed appear to have a significant effect on nucleosome positioning . As we have shown , these TFs are not characterized by particularly high expression or large numbers of binding sites in YPD , nor do they possess particular sequence specificities or DNA binding domains . Instead , our analysis suggests that these TFs engage in specific protein-protein interactions with chromatin remodelers , thereby effecting nucleosome eviction much more dramatically than other TFs . Although the final predictions of our statistical mechanical model are quite competent , i . e . in promoters of all nucleosomes and of all NFRs are correctly identified , they are still far from perfect . This raises the question as to what additional elements are missing from the model . The main error the model makes is failing to identify roughly one third of nucleosome free regions as nucleosome free . This suggests that the model misses additional factors that promote displacement of nucleosomes . As most sequence-specific TFs in yeast are already represented in the model , and our results suggest that only a small fraction of these TFs significantly affect nucleosome positioning , it seems unlikely that the missing sequence-specific TFs play a major role in the overall quality of the results . In contrast , as shown in Figure S6 in Text S1 , general TFs including the RNA polymerase itself may play an important role in nucleosome positioning . In this context it has also been suggested [19] that the well-positioned nucleosome immediately downstream of TSS may result from a direct interaction between general transcription factors and the RNA polymerase with this nucleosome . Thus , including the recruitment and binding of general TFs and RNA polymerase will likely further improve the model . In addition , TF binding can recruit chromatin modifying enzymes that displace nucleosomes and alter histone tails . The fact that experimentally observed NFRs are typically wider than the theoretically predicted ones suggest that the TF binding recruits chromatin modifiers which lead to a larger region of nucleosome exclusion than given by the TF binding itself . Thus , feed-back from TF binding to nucleosome modification and ejection as mediated by chromatin remodelers is a major feature that could improve the model's predictions . In summary , the picture that emerges from our study is that the binding of a specific class of TFs determines local recruitment of chromatin remodelers , which then mediate local expulsion of nucleosomes . The latter may further positively feed-back on TF binding and thereby expand and stabilize the nucleosome-free regions . Although this work has focused on yeast , the competition between nucleosomes and TFs for binding DNA may even be more crucial for transcription regulation in higher eukaryotes . For example , in multi-cellular eukaryotes many gene regulatory elements occur in distal enhancers , i . e . local clusters of TF binding sites a few hundred base pairs in length , to which a combination of TFs binds to effect transcription at a promoter that can be hundreds of kilobases away . Recent mapping of enhancers based on chromatin marks has suggested that these enhancers are bound and activated in a highly tissue- and condition-specific manner [51] , [52] . An attractive simplified model for such tissue-specific binding is that nucleosomes by default cause DNA to be inaccessible and that TF binding is too weak to access individual TF binding sites . Only in areas where a cluster with many binding sites for precisely that subset of TFs that is highly expressed in the condition will these TFs jointly outcompete the nucleosomes and create a region of DNA accessibility and TF binding , i . e . similar to the qualitative model presented in [53] . We believe that the statistical mechanics model that we have used here , might also be useful to quantitatively investigate such models of enhancer function .
Based on a combination of ChIP-chip data , in vitro binding data , and computational analysis [12] , [54] , [55] , we previously curated [32] a collection of position specific weight matrices ( WMs ) representing the sequence-specificities of S . cerevisiae TFs . We let denote the WM probability that position in a binding site for TF contains nucleotide . Consequently , the probability that a binding site for TF has sequence is given by ( 2 ) where is the length of the WM for TF and is the nucleotide at position in sequence segment . For our statistical mechanical model we wish to determine energies for the binding of sequence segment to TF . We make the standard assumption that the binding energy is a sum of individual contributions from different nucleotides in the site , i . e . ( 3 ) where is a sequence-independent contribution to the binding energy . Under this assumption , the sequence-specific energy components can be shown [27] , [31] to be related to the WM components through ( 4 ) where is a scale parameter , and the binding energy is expressed in units of . There has been a significant amount of effort into modeling the sequence specificity of nucleosomes using data from both in vivo and in vitro experiments , e . g . [1] , [15] , [18] , [24] . As shown in Figure 1A , different models of nucleosome sequence-specificity give predicted occupancies that are very highly correlated , and the model of [18] exhibits the most robustly high performance . We thus took the model of [18] as the basis for calculating binding energies of the nucleosome to each possible bp stretch . Specifically , the raw probability of a 147 bp long sequence segment under Kaplan et al's model can be obtained using the “nucleosome_prediction . pl” script , that is provided by the authors on their website , with default parameters and using the option “raw_binding” . Using this we define a binding energy under the Kaplan model as ( 5 ) In order to allow us to tune the sequence specificity of the nucleosomes , we introduce a similar scale parameter to obtain ( 6 ) Note that , at , the sequence-specificity of this model will be equal to that of Kaplan et al's model , whereas at nucleosomes will have no sequence preferences whatsoever . For notational simplicity , in the following we will consider the nucleosome as just another member of the set of all DNA binding factors . Let denote a ( non-overlapping ) configuration of TFs and nucleosomes bound to the genome and let denote all segments in the genome where a binding site for factor occurs . Using the standard Gibbs-Boltzmann approach , the probability of finding the cell in configuration is given by ( 7 ) where is the concentration of TF , is the inverse temperature , and is the partition function ( 8 ) Note that the probability depends on the scale factors through the dependence of the binding energies on the scale factors . Note that , since we will be fitting the scale factors , we can define ( 9 ) and fit the . For notational simplicity , we will drop the tilde and refer to these rescaled gammas as simply . Note that this is equivalent to measuring the energy in units of . Using only information about known binding sites , i . e . the WM entries , we cannot determine the sequence-independent contribution for each TF , which essentially controls how generally ‘sticky’ the TF is to DNA . To allow the comparison of binding energies of different TFs on a common scale we set such that , in the limit of low TF concentrations , each TF has equal binding to the yeast genome . Specifically , we set such that the average , when averaging over all sequence segments in the genome . Using this reparametrization the probability of a configuration becomes simply ( 10 ) Figure 8 shows a cartoon illustrating various configurations and the factors contributing to their probabilities . The partition function can be calculated efficiently using recursion relations variously known as transfer matrices or dynamic programming , and this has been routinely used in the field to sum over non-overlapping configurations of hypothesized binding sites , e . g . [25]–[27] , [29] , [30] . Let denote the partition sum for all configurations up to position in a given chromosome . We then have ( 11 ) Similarly , we can calculate the ‘backward’ partition sums from position to the end of the chromosome . Finally , the probability that a binding site for factor covers positions through is given by ( 12 ) where is the chromosome length . The occupancy of factor to position is then given by . Thus , given a set of scale factors and concentrations , we can efficiently calculate the occupancies of all TFs and the nucleosomes across the entire yeast genome . To compare the ‘raw’ occupancies as predicted by various models of nucleosome specificity and measured across several in vivo and in vitro experiments , we first downloaded the per base occupancy predictions provided by [18] and [24] and used these predicted occupancies directly . We also obtained raw data from the experiments [1] , [3] , [18] , [38] , [56] . To obtain per-base nucleosome occupancies we calculated , for the ChIP-seq data , the number of reads overlapping each position and log-transformed these read counts . For the ChIP-chip data we log-transformed the chip signal . We observed that there is a very small number of positions for which sometimes aberrantly high or low signals are reported . To avoid having these outliers skew the observed correlations we removed the of genomic positions with highest signal and with lowest signal . We then directly calculated Pearson correlation coefficients between all data-sets and all predictions . For the in vivo data , we make use of the reference map of nucleosomes and linkers for S . cerevisiae growing in YPD that was constructed by combining different experimental data-sets in [41] . We only retained nucleosomes that were observed in all datasets and have occupancy bigger then ( according to the authors' annotation ) . This set contained nucleosomes covering of the S . cerevisiae genome , and covers approximately of all annotated nucleosomes in [41] . Linkers were defined as regions lying in between segments that were annotated as nucleosomes in any of the data-sets . This set contained linkers covering of the S . cerevisiae genome . As observed in [41] the distribution of linker lengths is bimodal and we separately considered ‘short linkers’ ( less than bps long ) and ‘nucleosome free regions’ ( longer than bps ) in our analysis . There were short linkers and nucleosome free regions , covering and of the genome , respectively . We also separately considered the quality of the predicted nucleosome positions in promoter regions , defined as running from bps upstream to bps downstream of the TSS for each gene . The TSS definitions , as well as the definitions of the 3′ ends of genes , were taken from [57] . To assess the reproducibility of annotated nucleosome positions across the experimental data-sets we calculated , for every nucleosome in the reference annotation , the standard-deviation in the positions of the associated annotated nucleosomes in each of the data-sets . To compare the reproducibility of the annotated nucleosomes with what may be expected by chance , given the annotation procedure , we created randomized data-sets in which each sequencing read is mapped to a randomly chosen location in the genome . We then applied the same annotation procedure to this randomized data and calculated standard-deviations of the positions of annotated nucleosomes in the same way . We constructed a reference map of in vitro nucleosome positioning using independent data-sets from [18] , [19] , [58] using a procedure analogous to the one used in [41] . To annotate nucleosomes for every data-set we first run the GeneTrack software [59] using parameters ( width of the exclusion zone corresponding to configurations with non-overlapping nucleosomes ) , ( width of the smoothing gaussian kernel ) , ( half-width of the peak ) and ( cut-off for peak height ) . The values of parameters and and are dictated by the bp width of the nucleosome footprint . Since the width of the smoothing kernel is much smaller than the nucleosome width , the final nucleosome annotation is insensitive to the precise width of this kernel . Similarly , raising the cut-off by -fold or -fold would only slightly reduce the number of called nucleosomes ( i . e . and respectively ) and not substantially affect the results presented in the paper . We use the annotated nucleosomes as input to GeneTrack ( with the same settings ) , i . e . as if each annotated nucleosome were a read , to produce annotated reference nucleosomes . We retained the roughly of annotated reference nucleosomes that occur in all data-sets , leaving reference nucleosome genome-wide . Reference linkers were defined as regions not covered by nucleosomes in any of the annotations . There were such linkers genome-wide . To compare the experimentally annotated linker and nucleosome regions with the predicted nucleosome coverage we proceeded as follows . For a given set of parameters , i . e . concentrations and scale parameters , we first calculate the median of the predicted nucleosome occupancy across each annotated linker and nucleosome region . Given a critical median occupancy level , we then classified each region as either ‘nucleosome’ when its median occupancy was larger than and ‘linker’ when its median occupancy was less than or equal to . We then determined the fraction of regions both predicted and annotated as nucleosome , the fraction of regions predicted as nucleosome and annotated as linker , the fraction of regions predicted as linker and annotated as nucleosome , and the fraction both predicted and annotated as linkers . Using these we determined the mutual information between the predictions and the annotations based on the experimental data: ( 13 ) where is the fraction of all regions predicted as , is the fraction of regions annotated as , and we have explicitly indicated that this mutual information depends on the concentrations and scale factors used in the predictions . We then define the mutual information as the maximal mutual information that can be obtained varying the critical occupancy , i . e . ( 14 ) Finally , to normalize the mutual information on a more intuitive scale , we divide by the maximal possible mutual information , i . e . the entropy of the experimentally observed distribution: ( 15 ) to obtain ( 16 ) Thus , is the fraction of the information regarding nucleosome and linker positioning that is captured by the predictions , which we refer to as the quality score . We calculate the mutual informations and quality score in an entirely analogous manner when considering a particular subset of experimentally annotated nucleosomes and linkers , i . e . excluding short linkers and/or focusing only on promoter regions . To obtain predicted nucleosome coverage distributions we simply calculate the predicted occupancy at each position in the genome as described above . To obtain nucleosome coverage distributions from different experimental data-sets we proceeded as follows . As has been observed previously [20] , especially for ChIP-seq data-sets , the variance in read coverage along the genome is too large to be consistent with the known overall nucleosome coverage of roughly . Consequently , a naive normalization in which one assumes read-coverage to be directly proportional to nucleosome occupancy would lead to unrealistically low overall nucleosome coverage . To address this , we normalize the data by rescaling log read-coverage , similar to the normalization procedure we developed previously for next-generation sequencing data [60] . Specifically , for ChIP-chip data ( from a tiling array with 4 bp resolution ) we obtain a signal corresponding to the log-ratio of signal from the nucleosome and background sample for each probe along the genome . Similarly , for ChIP-seq data we extend each read to length bp and defined the ‘signal’ at each genomic position as the logarithm of the number of reads overlapping position . We assume that the signal is proportional to the logarithm of the probability that a nucleosome is bound to the corresponding segment in the genome , i . e ( 17 ) where and are unknown constants . We determine and by demanding that the average coverage probability matches the experimentally observed average nucleosome coverage of , and that all coverage probabilities must lie in the interval . Finally , there is a small number of probes ( percent of all probes ) with an abnormally high signal and we removed these outliers before fitting and . As shown in Figure S1 in Text S1 , this procedure leads to highly similar coverage distributions for different data-sets . Predicted average nucleosome coverage profiles around transcription starts and ends were obtained by simply averaging the predicted nucleosome coverage at different positions relative to TSS and transcription end over all genes . We similarly averaged the experimental coverage profiles relative to transcription starts and ends . To optimize the concentration and specificity scaling parameters we used the Melder-Mead algorithm in combination with a simulated annealing algorithm that is implemented in the GNU Scientific Library ( GSL ) . To avoid over-fitting when fitting different models with varying numbers of parameters we used a cross-validation scheme for each model and data-set . That is , for each data-set and model , we randomly divide the data-set of annotated nucleosomes and linkers into equally sized sub-sets . We then perform the parameter fitting independent times , each time optimizing the parameters on of the data and then evaluating the final quality score of the model on the ‘test-set’ containing the remaining of the data . Whereever quality scores are shown we show the average quality score and its standard-error across the test-sets . For the in vivo reference set of nucleosomes and linkers , we first performed optimizations of the nucleosome-only model with different ( fixed ) values of the specificity scaling parameter , i . e . optimizing only the concentration . For both the in vivo and in vitro reference sets we optimized the two-parameter nucleosome-only model ( obtaining an optimal for the in vivo data , and for the in vitro data ) . After this we fixed the nucleosome specificity and concentration to their optimal values and , for the in vivo data , fitted the model with all TFs , fitting the concentrations and scale parameters for all TFs . For the biophysical characterization of the fitted model , we first averaged the fitted concentrations and scale parameters over the training sets . We then calculated the predicted posterior binding probabilities for every factor ( i . e . the nucleosomes and all TFs ) at every position in the yeast genome . For each factor , we then calculated the fraction of the genome covered by this protein: , where is the length of the footprint of protein and is the length of the yeast genome . We also calculated the average binding energy of the binding sites of each protein , i . e . , and its standard deviation . Here is the binding energy of protein at position , measured in units . Finally , we calculated the average entropy per binding site: ( 18 ) To calculate the information content for a TF , as shown in Figure S11 of Text S1 , we used the standard formula ( 19 ) where the are background probabilities ( which we chose uniform ) and the are the weight matrix entries . Note that , to incorporate the scaling parameter , the weight matrix entries are rescaled according to: ( 20 ) To assess the contribution of different TFs we fitted , for each TF , the model with nucleosomes and this single TF . For each TF we calculated , on each of the test-sets , the difference between the quality score using only the nucleosome , and the quality score with the TF added , and determined the mean and standard error over the test-sets . We then ranked the TFs by the -statistic . These fits and statistics were obtained separately for both the in vivo and the in vitro data . Finally , we also created a set of randomized WMs by , for each WM , randomly shuffling the columns of the WM . Note that this randomization conserves both the sequence composition and the information scores of the WMs . We then performed the fitting with these randomized WMs and obtained -statistics in the precise same way . For the in vivo data we then also fitted models including the top , , , and TFs from the list ranked by their -statistic , re-optimizing all parameters . Finally , to assess the contribution of the nucleosome specificity when TFs are added for the in vivo data , we fitted the model including all TFs , but without nucleosome sequence specificity , i . e . setting . To annotate TFs with known roles in chromatin dynamics we used the Gene Ontology ( GO ) annotations available from the Saccharomyces cerevisiae genome database . We considered a TF ‘chromatin related’ when its GO annotation included any of the following categories: Finally , we also added the TFs identified in [12] to this list . To calculate the over-representation of ‘chromatin related’ TFs among the top TFs effecting nucleosome positioning , we performed a simple hypergeometric test . We first annotated yeast proteins that are either ( 1 ) part of chromatin remodeling complexes , ( 2 ) histone modification enzymes , or ( 3 ) histones themselves . Subunits of chromatin remodeler complexes were taken from [61] , [62] . As subunits of histone modification enzymes we took genes that have GO annotation “covalent chromatin modification” and all children GO categories , i . e . histone methylation , acetylation etcera ( 108 genes in total ) . Information about protein-protein interactions were downloaded from the STRING database ( http://www . string-db . org , file ‘protein . links . detailed . v9 . 0 . txt . gz’ ) , using only experimental evidence with a cutoff of 400 . After determining all known protein-protein interactions between the TFs and the three classes of proteins ( histones , histone modification enzymes , and subunits of chromatin remodeling complexes ) we calculated enrichment of interactions between each class and the top TFs that significantly explain nucleosome positioning . To assess the significance of the enrichment we used a simple hypergeometric test . The results are listed in Table 1 . | The DNA of all eukaryotic organisms is packaged into nucleosomes , which cover roughly of the genome . As nucleosome positioning profoundly affects DNA accessibility to other DNA binding proteins such as transcription factors ( TFs ) , it plays an important role in transcription regulation . However , to what extent nucleosome positioning is guided by intrinsic DNA sequence preferences of nucleosomes , and to what extent other DNA binding factors play a role , is currently highly debated . Here we use a rigorous biophysical model to systematically study the relative contributions of intrinsic sequence preferences and competitive binding of TFs to nucleosome positioning in yeast . We find that , on the one hand , the phasing of the many small spacers within dense nucleosome arrays that cover gene bodies are mainly determined by intrinsic sequence preferences . On the other hand , larger nucleosome free regions ( NFRs ) in promoters are explained predominantly by TF binding . Strikingly , we find that only 10–20 TFs make a significant contribution to explaining NFRs , and these TFs are highly enriched for directly interacting with chromatin modifiers . Thus , the picture that emerges is that binding by a specific class of TFs recruits chromatin modifiers which mediate local nucleosome expulsion . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"sequence",
"analysis",
"systems",
"biology",
"genomics",
"chromosome",
"biology",
"biophysic",
"al",
"simulations",
"regulatory",
"networks",
"biology",
"epigenomics",
"computational",
"biology",
"chromatin"
] | 2013 | Nucleosome Free Regions in Yeast Promoters Result from Competitive Binding of Transcription Factors That Interact with Chromatin Modifiers |
The simian malaria parasite , Plasmodium knowlesi , can cause severe and fatal disease in humans yet it is rarely included in routine public health reporting systems for malaria and its geographical range is largely unknown . Because malaria caused by P . knowlesi is a truly neglected tropical disease , there are substantial obstacles to defining the geographical extent and risk of this disease . Information is required on the occurrence of human cases in different locations , on which non-human primates host this parasite and on which vectors are able to transmit it to humans . We undertook a systematic review and ranked the existing evidence , at a subnational spatial scale , to investigate the potential geographical range of the parasite reservoir capable of infecting humans . After reviewing the published literature we identified potential host and vector species and ranked these based on how informative they are for the presence of an infectious parasite reservoir , based on current evidence . We collated spatial data on parasite occurrence and the ranges of the identified host and vector species . The ranked spatial data allowed us to assign an evidence score to 475 subnational areas in 19 countries and we present the results on a map of the Southeast and South Asia region . We have ranked subnational areas within the potential disease range according to evidence for presence of a disease risk to humans , providing geographical evidence to support decisions on prevention , management and prophylaxis . This work also highlights the unknown risk status of large parts of the region . Within this unknown category , our map identifies which areas have most evidence for the potential to support an infectious reservoir and are therefore a priority for further investigation . Furthermore we identify geographical areas where further investigation of putative host and vector species would be highly informative for the region-wide assessment .
The Plasmodium knowlesi parasite , found in wild monkey populations , is a serious public health concern yet almost nothing is known about its geographical extent . It is known to cause severe and fatal disease in humans [1]–[4] and is the most common cause of clinical malaria in high transmission regions of Malaysia [5] , [6] where it is three times more likely to cause severe malaria than P . falciparum [4] . However , costly P . knowlesi-specific molecular diagnostic techniques are only used to confirm diagnosis by microscopy in one area , Malaysian Borneo , whereas human cases have been reported from Brunei [7] , [8] , Cambodia [9] , Indonesia [10] , [11] , Myanmar [12]–[14] , the Andaman and Nicobar Islands of India [15] , the Philippines [16] , [17] , Singapore [18]–[20] , Thailand [12] , [21]–[24] and Viet Nam [25] , [26] as well as most parts of Malaysia [2]–[4] , [6] , [27]–[42] . The geographical limits of this disease and the spatial variation in disease risk within these limits are simply unknown . Malaria caused by P . knowlesi is a truly neglected tropical disease and there are substantial obstacles to defining the geographical extent and risk of this disease . The symptoms of the disease in humans overlap with those caused by other malaria parasites [43]and other diseases such as dengue [19] . Microscopy fails to distinguish P . knowlesi from P . malariae ( a more benign infection ) and P . falciparum ( the leading cause of severe malaria globally ) and in routine practice P . knowlesi is also misdiagnosed as P . vivax [44] , [45] . Currently , Rapid Diagnostic Tests are not only insufficiently sensitive for P . knowlesi [46] but can misidentify this species as P . falciparum or P . vivax ( summarised in [43] ) , and one set of primers used in molecular assays can mistake some P . vivax isolates for P . knowlesi [47] . The use of routine microscopy has led to large numbers of P . knowlesi cases being missed and the parasite is only correctly diagnosed when costly P . knowlesi-specific molecular techniques are used . Despite high rates of infection in parts of Malaysia and strong evidence from laboratory experiments that human-to-human transmission by mosquitoes is possible [48] , [49] , this transmission route is very difficult to demonstrate in nature and to-date no naturally occurring human cases have been definitively linked to human-to-human transmission [43] , but equally no barriers to natural human-to-human transmission have been demonstrated . In the absence of complete geographical data on this disease in humans , the presence of alternative hosts is a useful indicator of the potential presence of a disease reservoir . A competent anopheline vector species is also required for transmission from monkeys to humans ( or from humans to humans ) . These two factors provide an opportunity to map the potential reservoir of the parasite in the absence of human case data . Defining areas of risk , however , is further complicated by the fact that much of the potential parasite range is spread over a large archipelago of many thousands of islands separated by substantial distances; a biogeographical factor often neglected in global disease mapping exercises and of particular relevance to a zoonotic vector-borne disease with a reservoir in wild mammal populations . Previous studies have defined a range for neglected diseases such as dengue by reviewing the consensus of evidence for the presence/absence of the disease at each location [50] . These studies combined multiple reports of disease presence/absence and weighted them for diagnostic quality and reporting provenance . In the case of P . knowlesi malaria , however , there is insufficient direct evidence of disease presence/absence to replicate this approach . In this study , instead of assessing the consensus of evidence for disease presence/absence , evidence on locations of host and vector species , as well as human case data , were combined to obtain ranked scores for the capacity to support an infectious reservoir . We first reviewed the evidence on non-human primate hosts and transmission by different anopheline vector species and then gathered data on the ranges of these two groups , as well as the locations of known human cases of the disease . This information was used to assess the potential of each province or island to support an infectious reservoir . The final output is a comprehensive summary of the current state of evidence for a P . knowlesi reservoir . Importantly , it is not a map of the likelihood of a reservoir occurring within an area but it does highlight areas where evidence is lacking . The results of this study allow us to propose priorities for the new data that are urgently needed in order to understand the spatial variation in risk to humans from this disease .
Maps of human disease often use administrative divisions to subdivide countries . This is the structure in which much national health data are provided and is a useful format to feed results back to public health agencies . For zoonotic diseases , however , the distributions of wild host species will not necessarily map closely to administrative divisions . In this instance , the majority of cases reported to-date are located within a huge archipelago where administrative divisions can encompass multiple islands separated by large distances . For this study we took a mixed approach using administrative divisions to subdivide the mainland and the largest islands in the archipelago ( Papua , Borneo , Sumatra , Java and Sulawesi ) . The largest administrative division in the area of study , Xizang Zizhiqu ( the Tibet Autonomous Region ) in China , was further divided into level two divisions . Additionally , islands greater than 25 km from the mainland and greater than 200 km2 in area were defined as separate geographical units . Within the archipelago , islands within 10 km of each other were grouped together and islands less than 100 km2 and more than 10 km away from any other island were disregarded . Following these approximate guidelines we were able to divide this region of 19 countries spread over approximately 25 , 000 islands into 475 geographical units . We conducted a literature survey in Web of Knowledge using the terms ‘knowlesi’ , ‘zoono* and malaria’ , ‘monkey and malaria’ to collate journal articles on the parasite and then excluded studies conducted solely in the laboratory ( e . g . immunity studies using a rhesus-knowlesi model ) . The bibliography of each article was then searched for further published sources of information and authors working in locations of particular interest were contacted . The search was completed on 30 September 2013 . Molecular techniques that can distinguish the P . knowlesi parasite ( alone or in combination with microscopy ) have only been available for the last decade so this dictated the period reviewed ( 2004 to 2013 ) . All data on wild animals tested for P . knowlesi infection were extracted and used to determine which alternative host and vector species would contribute to the next stage of the work . Each subnational area was assigned a score based on three classes of evidence: the presence of the parasite; the presence of a monkey host species , and; the presence of a malaria vector species known to bite humans ( Figure 1 ) . Each area was scored independently and was unaffected by the scores of neighbouring areas to reflect , in part , the patchy nature of the disease and of the evidence . The scores assigned provide a simple ranking . In summary , confirmation of a human infection ranked highest with +9 overriding all other evidence , then confirmation of sporozoites in a human-biting vector scored +8 . Confirmation of a monkey infection was combined with the score for presence of a human malaria vector ( see below ) to give a maximum score of +7 . In the absence of the parasite itself , presence of both a known host species and a known vector species scored +6 . Combinations of known/putative host species presence and known/putative vector species presence ( see below ) scored from +2 to +5 . Combinations of host species absence , vectors species absence and absence of any malaria in humans ( see below ) scored values ranging from −1 to −9 . In locations where both presence and absence indicators were found , the respective scores were added together . All occurrences of the parasite , identified using either 1 ) P . knowlesi-specific molecular identification methods or 2 ) a combination of microscopy and molecular techniques that distinguish P . knowlesi from P . falciparum and P . malariae , were extracted from the library of published literature described above . The location of occurrence was defined as the location of infection , not the location of symptom onset or diagnosis , and studies that could not identify the location of infection ( to state/island level ) were excluded . For each occurrence , the date of study , diagnostic technique ( s ) and subnational location of infection were extracted . Only the most recent infection from each area/island was retained . In two studies we could not distinguish between adjacent administrative divisions so these areas were combined ( at the Myanmar/China border and at the Myanmar/Thailand border ) . Two reports of human cases from Brunei did not meet the inclusion criteria because one used microscopy only for diagnosis [8] and the other did not publish their diagnostic methods [7] . There were no survey results that provided clear evidence for absence of the parasite in an area . In countries that routinely report cases of the four human malarias , occurrence of these other species may mask P . knowlesi cases and , conversely , divisions within these countries that report no malaria cases are less likely to have undetected P . knowlesi cases . Areas within malaria endemic countries reporting no malaria cases were defined using the 2012 World Malaria Report [51] and assigned a score of −3 . For areas without data in the 2012 World Malaria Report , we used the 2010 limits of P . falciparum and P . vivax defined by the Malaria Atlas Project to classify each area [52] , [53] . Based on the data collected from published studies ( see Results ) , we made the decision to use the ranges of two monkey species . Macaca fascicularis ( the long-tailed or crab-eating macaque , also known as the cynomolgus or kra monkey ) and M . nemestrina ( the pig-tailed or Southern pig-tailed macaque ) . Two other species have been identified as hosts , Trachypithecus obscuras and Presbytis melalophus , however the ranges of these species fall entirely within the range of M . fascicularis , therefore , these areas already receive the maximum score for presence of a known non-human host species . The ranges for M . fascicularis and M . nemestrina were initially defined using the International Union for Conservation of Nature ( IUCN ) ranges [54] and a score of +3 assigned to all subnational areas that overlapped with one or both of these ranges . The IUCN ranges , however , estimate the natural range of each species and do not always include introduced populations , and new data may have been collected since the ranges were last revised . For these reasons we also included evidence for presence of each species outside the IUCN ranges from 1985 onwards and gave a score of +3 to records of a host species collected since 2000 and +2 for records collected between 1985 and 1999 . A score of +1 was assigned to areas where the published evidence indicated introduced populations have hybridised out with endemic species in the area . The published literature does not cover all islands in the Malay Archipelago so we also contacted conservation and wildlife organisations in Indonesia , Malaysia and the Philippines to request information on which islands support populations of these species and assigned a score of +3 to any new areas identified by these organisations . After published studies reported finding P . knowlesi infections in M . nemestrina monkeys , this monkey species was divided by taxonomists into M . nemestrina and M . leonina ( the Northern pig-tailed macaque ) . We made the decision to include both species because , although it is likely that the monkeys tested were M . nemestrina ( as currently classified ) , human cases have been found outside the ranges of M . nemestrina and M . fascicularis but within the range of M . leonina . A lower score of +2 was assigned to areas within the M . leonina range . The IUCN ranges were combined for all three macaque species and a score of −1 was assigned to areas outside the combined host species range ( excluding locations with introduced populations ) and −2 for those areas more than 100 km outside this range . The maximum possible negative score was not assigned because we do not have a definitive list of primate species that can host a reservoir of P . knowlesi parasites in the wild , and laboratory studies have shown that other species can be infected by this parasite [55] . Based on evidence from the published literature on which Anopheles species are capable of transmitting P . knowlesi ( see Results ) and evidence for which vectors transmit human malaria [56] , we assigned the highest vector score of +3 to the Leucosphyrus Complex and the Dirus Complex . This score was assigned to areas where a human malaria vector belonging to either of these two Complexes was recorded as present . Specifically we used published ranges for the Dirus Complex , Anopheles leucosphyrus and An . latens combined , and An . balabacensis . The species were grouped in this way because studies publishing vector species occurrence frequently do not distinguish individual species within these groupings . In the absence of these species , the presence of other sylvatic vector species ( forest/margins dwelling , and therefore more likely to encounter macaques ) known to transmit malaria to humans but of unknown P . knowlesi vector status was assigned a lower score of +2 . The species in this category were the Fluviatilis Complex , the Minimus Complex , An . koliensis , An . aconitus , An . annularis , the Culicifacies Complex and An . flavirostris . Finally , where no vector species from either of the above two classes were present , presence of any of the other human malaria vectors was assigned a lower score of +1 to reflect the fact that these species are known to have the capacity to transmit malaria parasites to humans [56] and have not been ruled out as vectors of P . knowlesi . To assess the presence of all three vector classes , we used the predicted distributions generated by the Malaria Atlas Project [56] and defined all points with a probability of occurrence of >0 . 5 as presence locations . Presence of any one of the species from a vector class within an administrative division or island was considered sufficient to record that vector class as present . A score of −4 was assigned to areas outside the combined range of the vector species and −6 to areas 100 km outside this range . This score ( smaller than the maximum negative score but greater than the negative score assigned to absence of known monkey host species ) reflected the fact that there is a lack of evidence for the definitive list of vectors transmitting P . knowlesi but much stronger evidence for the definitive list of vectors that transmit malaria to humans . The scores were combined as shown in Figure 1 and the overall scores , providing a relative ranking of the cumulative evidence for each subnational area , were displayed on a map of the region . A second simplified map was then created , to aid visualisation of the results , by grouping the scores into four classes: scores of +7 to +9 were classed as ‘confirmed infectious reservoir’; scores of +6 were classed as ‘confirmed reservoir prerequisites’; scores of +1 to +5 were classed as ‘weak evidence for a reservoir’; and scores of −9 to 0 were classed as ‘absence of reservoir prerequisites’ . To test the scores generated , the scores that would have been obtained if evidence for presence of the parasite itself was excluded were compared between areas with confirmed parasite presence and those of unknown parasite status . A jackknife approach was then used to assess the dependence of the final scores on each individual factor . Each individual factor was excluded and the scores were re-calculated . The results were compared between areas with confirmed parasite presence and those of unknown parasite status , and the relative ranking of all areas before and after each factor was removed were compared . To assess the predictive power of the scores , the area under the receiver operating characteristic curve ( AUC ) was calculated for each version of the scoring system created when single factors were removed in turn ( with parasite presence excluded ) [57] .
The review of published studies of P . knowlesi infection in wild monkey populations is summarised in Table 1 . It is immediately clear that only a few species and populations have been tested in a few countries . High infection prevalences have been found in M . fascicularis and M . nemestrina populations in Sarawak in Malaysian Borneo and lower prevalences in Singapore , Kuala Lumpur and Pahang States in Malaysia , Narathiwat and Ranong Provinces in Thailand , and North Sulawesi Province in Indonesia . Older studies ( pre-2004 ) have also found infected M . fascicularis monkeys in Cebu , Philippines [58] . The review of published studies of P . knowlesi in wild mosquito populations is summarised in Table 2 . The most striking result is that published studies have only been conducted in Khanh Hoa Province in Vietnam and Pahang State and Kapit Division of Sarawak State in Malaysia . Other countries have very different vectors that are known to transmit malaria to humans but their role in P . knowlesi transmission is unknown . In the areas studied , there is evidence that Anopheles latens from the Leucosphyrus Complex and members of the Dirus Complex transmit P . knowlesi . Members of both Complexes are known to transmit human malarias . Earlier studies ( pre-2004 ) have implicated members of the Hackeri Subgroup in transmission of P . knowlesi within monkey populations in Peninsular Malaysia [59] , however , these mosquito species are not known to bite humans . Laboratory studies have shown that a wider range of species may be able to transmit P . knowlesi , however , these studies also confirmed that the most effective vectors , of those tested , were members of the Leucosphyrus Group [60] , [61] . The information from the reviews of monkey hosts and of vectors was used to generate parasite , host and vector evidence scores for each geographical area and these were combined with the evidence for parasite presence to give an overall score representing the evidence for potential presence of a parasite reservoir that is infectious to humans ( shown in Figure 2A ) . The individual evidence scores assigned to each subnational area ( for evidence of human infection , parasite occurrence , known and potential host occurrence , and known and potential vector occurrence ) are given in Table S1 . Figure 2A shows the full range of scores generated . The variation in cumulative evidence for presence of the prerequisites required to support an infectious reservoir can be seen , from a complete absence of all prerequisites and thus evidence for absence of a reservoir ( −9 , ) to a lack of evidence and high uncertainty ( 0 ) , to presence of a full set of prerequisites but unknown parasite status ( +6 ) , to confirmation of human cases ( +9 ) . Figure 2B shows a simplified version of the same information with the scores grouped into four classes: areas where both the parasite itself and a vector able to transmit it to humans have been found; areas with known monkey hosts , known vectors of P . knowlesi and no factors indicating absence of a reservoir ( presence of the parasite itself is unknown ) ; areas of weak evidence for the presence of a full set of reservoir prerequisites; and areas where there is evidence for an absence of reservoir prerequisites . It is important to note that Figure 2 is not a map of the likelihood of a reservoir occurring within each area , for example , an area may receive a zero score because evidence is lacking or it may in fact be less likely to support an infectious reservoir . Figure 3A provides a histogram of the full range of scores assigned to the 475 subnational areas with scores +7 to +9 exclusively assigned to areas with confirmed parasite presence . Figure 3B shows the range of scores assigned when evidence for parasite presence was excluded from the scoring system . The scores assigned to areas that are known to support the parasite ranged from +1 to +6 , i . e . the parasite has been found in areas outside the known monkey and/or vector ranges , or areas with factors that indicate absence of a reservoir prerequisite . The area that scored +5 was the northern part of Myanmar ( Shan State North and East ) bordering China , and the evidence for parasite presence here came from two independent studies [13] , [14] . The known monkey host species ( M . fascicularis and M . nemestrina ) have not been found in this area but M . leonina is present . Studies that have investigated malaria parasites in the monkey populations in this area have not yet found evidence of P . knowlesi infection in any of the species present ( Qijun Chen , unpublished data ) . Two neighbouring areas with confirmed parasite presence scored only +1 when the evidence for parasite presence itself was excluded . These were two islands in the north of the Andaman and Nicobar Islands; Smith Island and Car Nicobar . The southern islands fall within the range of M . fascicularis but there is no evidence of any known or putative monkey host species populations on the northern islands , including Smith Island and Car Nicobar [62] . The evidence for human P . knowlesi infections in these locations ( and also on Great Nicobar and Teressa , two of the southern islands with known hosts and known vectors ) comes from a single study of human malaria cases in the Andaman and Nicobar Islands [15] . A total of 15 cases were found on Smith Island and 25 on Car Nicobar , which rules out a one-off imported case . Further work is required to investigate the possibility of a P . knowlesi reservoir existing on the northern islands of the Andaman and Nicobar Islands , including the possibility of human-to-human transmission and the possibility of a parasite reservoir in the captive long-tailed macaques at Port Blair's zoo [63] . Finally , Figure 2 shows that many areas in the region have weak evidence for their ability to support an infectious reservoir , but cannot be ruled out altogether . A small number of areas fall outside the ranges of all known or putative hosts or vectors , which provides evidence that these areas could not support a P . knowlesi reservoir . If this study had covered a broader geographic area , the number of these areas would be much higher . Table 3 provides the AUC values calculated when evidence for parasite presence was excluded , and each time the scoring system was adjusted to remove a single factor in turn . The AUC value obtained when parasite presence only is excluded was 0 . 7979 , indicating the scoring system has very good predictive power . No set of factors modelled the known locations of the parasite perfectly but the result was similar when different factors were removed and was always ≫0 . 5 , indicating that the accuracy of the scoring system is always good and not heavily dependent on any single factor . Figure S1 shows the full range of scores obtained when individual factors were excluded . In each case , scores for the subnational areas with confirmed parasite presence ( 31 subnational areas; 29 with confirmed human cases and 2 confirmed in monkey only ) can be visualised compared to the scores for the 444 other areas . Figure S2 shows the relative ranking of the 475 areas after a single factor has been removed , against the ranking using all factors . The relative ranking does not appear to be strongly affected by the removal of any single factor , i . e . the individual factors are highly correlated as indicated by the consistently high AUC values shown in Table 3 . Table S2 provides the full set of scores for each area including the scores achieved after each individual factor had been removed .
By assessing the evidence in a systematic way based on the current state of knowledge , we have been able to map subnational areas where a P . knowlesi reservoir capable of infecting humans has been confirmed and those that support known hosts and vectors . In the absence of routine confirmation of P . knowlesi in human cases , and of definitive lists of host and vector species , it is harder to map areas of known disease absence . We have , however , been able to classify the rest of the region into areas that range in the evidence for their capacity to sustain a parasite reservoir that is infectious to humans , based on the current state of knowledge . Both the review of species shown to host and transmit P . knowlesi , and the ranking of the evidence for a parasite reservoir , highlight the urgent need for more evidence in large parts of the region of study and provide information on the types of data that are needed . The results of this study highlight priority geographical areas for future study that would enable us to build a more precise map . Areas of Indonesia ( Kalimantan , Sumatra , part of Java and parts of Sulawesi ) , parts of the Philippines , Cambodia , S . Thailand , S . Myanmar and S . Vietnam support both the known hosts and the known vectors , and are obvious targets for studies investigating new locations of parasite infections and disease prevalence . Locations with high disease potential could be targetted further by identifying areas that report cases of P . malariae malaria when using microscopy for routine species confirmation . The blank cells in Tables 1 and 2 indicate the regions that have not been tested for parasite presence in alternative hosts and vectors , and the species that have not been tested . In this case , data on absence of the parasite will be as important as presence data and will help to refine the disease limits . When parasite presence was excluded from the scoring system , the predictive power of the scores generated from the evidence on hosts , vectors and human malarias was very good ( AUC = 0 . 8146 ) . It is important , though , not to assume that the factors used in this scoring system give the full picture . It is likely that the researchers who designed the P . knowlesi studies conducted outside of Malaysia used the same assumptions about host and vector species as this study , when choosing their study locations , leading to a bias in locations where the parasite has been found . Evidence from human cases in returning travellers , however , may not be subject to the same biases for presence of presumed host and vector species . All of the published cases of P . knowlesi infection in returning travellers , diagnosed outside the region , involve patients that had spent time in one or more subnational areas where both the known monkey hosts and the known vectors are present [7] , [10] , [17] , [22] , [30] , [31] , [34] , [35] , [37] , [64]–[66] providing corroborating evidence for the assumptions made in this study . Absence of a parasite is harder to prove and negative results are harder to publish , but there is a limited amount of unpublished data that provides further corroboration of our approach . Investigation of 349 human malaria cases from across Laos ( average score 4 . 25 = weak evidence ) found no P . knowlesi ( M . Mayxay , unpublished data ) while surveys of macaque populations in Nepal ( average score −1 = weak evidence/absence of reservoir prerequisites ) and Bangladesh ( average score 1 . 67 = weak evidence ) also found no evidence of P . knowlesi infection ( Ananias Escalante , unpublished data ) . The sensitivity analysis presented here suggests that some of the factors included in this study could be removed and the scores would still perform as well , however , the areas with confirmed parasite presence are a potentially biased sample and so it would be unwise to remove any potential factors until we are closer to a definitive list of vectors and alternative hosts . This would help to refine the map presented here , enabling us to assign higher positive or negative scores for either presence or absence , and therefore to delineate more accurately the outer limits of the disease reservoir . This is necessary in order to provide precise information to public health agencies , and to provide a contemporary baseline to monitor future changes in the disease distribution . Longitudinal studies in Sabah , Malaysia have shown that P . knowlesi incidence has increased at this location over the last decade [6] but further research is required to assess whether this is linked to factors such as changing land use , changes in human behaviour and/or changes in the behaviour of the alternative hosts or vectors , including the possibility that human-to-human transmission is a factor [67] . In the past a lack of diagnostics meant that data on human cases was lacking and high population movement further complicated the picture . We are now in a better position to obtain human case data and this study has highlighted the regions to target . Further studies of the monkey species able to host this parasite would also be particularly informative , particularly in Northern Myanmar where M . fascicularis and M . nemestrina are absent but M . leonina , M . assamensis and T . phayrei are present [54] . Studies of any monkey populations on Smith Island in the Andamans would also be informative although no monkey species are endemic to this island or nearby Car Nicobar [54] and there are no confirmed reports of domestic or introduced primate species on these islands . Port Blair Zoo on Smith Island has long-tailed macaques in captivity [63] but there is no evidence for presence of captive monkeys on Car Nicobar . The report of human cases of P . knowlesi malaria on Smith Island and Car Nicobar [15] , and the primate status of these islands , certainly merits further investigation . Both the monkey and vector species involved in P . knowlesi transmission are a complex and dynamic mix of subspecies and sibling species [56] , [68] . No studies to-date have considered the ability of the full range of macaque species to host malaria parasites nor have the many hybrids occurring in areas where these species are co-endemic [69] been investigated for their parasite status . As well as their susceptibility to P . knowlesi , the social organisation of these primates differs , in terms of ranging patterns , relationships to humans and time spent on the ground versus the canopy . These factors may have an important influence on their relevance as a reservoir for transmission of P . knowlesi to humans . These factors also differ between populations; for example , in areas with extensive primate hunting , primate reservoir populations may be pushed far from humans reducing the probability that humans will intersect with primate-vector cycles . This work also highlights the importance of understanding the role of introduced populations when the ultimate goal is to map a disease reservoir . Plasmodium knowlesi has been found on Sulawesi [70] where none of the host species are endemic but where M . fascicularis and M . nemestrina are kept as pets and have escaped into the wild [71]–[73] . Future studies of parasites in introduced populations would increase our understanding of the likelihood of a founder population being infected and of persistence of the parasite within a new population following introduction . This would inform the criteria used to decide which populations are included in mapping studies . When monkey host species have been introduced to areas where other macaque species are endemic [74] , hybrids have been found outside the range of the host species and further interbreeding may lead to genes from these host species being introgressed into the native species population [75] . This again raises the question of the infection status of hybrids and of non-host species populations with introgressed genes from M . fasciularis or M . nemestrina . Hybrids are likely to occur in the narrow contact zone between M . fascicularis and M . mulatta in Thailand , Vietnam , Laos and Myanmar [76] , [77] . Hybrids found within the range of known P . knowlesi host species will not impact the geographical limits of the disease , but if hybrids are found beyond the range of the known hosts this could affect the disease risk in these locations . The disease status of hybrids between known hosts , whose populations can sustain high P . knowlesi infection prevalences , and rhesus monkeys , that may not be able to survive in the presence of P . knowlesi , is particularly interesting and currently unknown . The impact on P . knowlesi host status following introgression of genes from one species into populations of another is also unknown; we simply do not know whether introgression ( contemporary or ancient ) of M . fascicularis genes into M . mulatta populations at the southern end of their range [78]–[80] increases their ability to host this parasite or whether introgression of genes from M . mulatta into M . fascicularis populations north of peninsular Thailand reduces their ability to act as a reservoir for this parasite , or whether more complex genetic interactions occur . Which genes are important in P . knowlesi infection and their status in any of these species or populations is unknown . Taking a pragmatic approach for the purpose of producing a map of disease risk for public health use , however , the most important questions are 1 ) what is the prevalence of human infection at precise locations and 2 ) what is the prevalence of infection in species/hybrids of monkeys and mosquitoes at precise locations across the region ? A recent preliminary finding reports a P . knowlesi infection in either a M . mulatta monkey or a M . mulatta/M . fascicularis hybrid in Vietnam close to the location of known human and vector infections [81] . Further investigation of P . knowlesi in wild M . mulatta populations , and in populations of hybrids , is needed to provide evidence for their role in hosting a parasite reservoir . Large numbers of laboratory experiments have shown that P . knowlesi readily infects and is usually fatal to M . mulatta ( ∼70% of individuals are killed ) but the surviving animals have the ability to pass the parasite on to mosquito vectors [82] , leading to contrasting hypotheses that either M . mulatta populations cannot co-exist with the parasite [83] and could therefore be used as a negative indicator for a disease reservoir , or M . mulatta could be a natural host for the parasite [81] and therefore be used as a positive indicator . Alternatively , populations of M . mulatta in different locations may differ in their level of immunity to P . knowlesi , which would mean both hypotheses could be true depending on location . The overlap of the P . knowlesi parasite and the rhesus monkey found in two subnational areas ( N Myanmar and NW Thailand ) suggests the parasite and the rhesus macaque may co-exist , however , within these two areas the precise locations of the parasite and of the macaque may still differ . A finer scale approach will help to resolve the question of whether M . mulatta can co-exist with P . knowlesi . In addition , a second monkey species , Presbytis ( Semnopithecus ) entellus , which is also known to have a high fatality rate when infected in the laboratory [84] , could be used as an indicator of P . knowlesi absence or alternatively this species could also provide a parasite reservoir . Inclusion of P . entellus as a negative indicator would reduce the scores for areas of Southern India and Sri Lanka on the edges of the area of this study . More data on the naturally-occurring malaria infections found in the full range of species in the region is needed and further data to back up the finding of a P . knowlesi infection in either a M . mulatta monkey or a M . mulatta/M . fascicularis hybrid in Vietnam [81] would resolve the issue of whether M . mulatta presence is a useful indicator for a potential disease reservoir . The approach used in this paper has the limitation that presence of a single isolated host population in one part of a state/island increases the score assigned to the whole state/island . One example of this is Papua , a Province of 320 , 000 km2 which is not endemic for any of the known or potential host species but which supports a single isolated population of M . fascicularis near Jayapura [85] . Surveys of the malaria parasites found in isolated introduced populations would provide an evidence base for the score assigned to these populations . Furthermore surveys which show that an isolated population is P . knowlesi-free can be used to exclude the population from the scoring system . A surveyed population that is found to be infected will still affect the score for the whole state/island , as will an isolated human case or infected vectors at a single discrete location , and a finer resolution mapping approach is needed to address this limitation of the current map . The map presented here has divided the region into 475 areas and provides good subnational resolution , but it is not a fine resolution map and cannot distinguish the large variation that may exist within a province or island . Specifically , there will be areas within most provinces/islands that are less likely to support a reservoir . Point-located data for the parasite , hosts and vectors is available , which opens up the possibility of using ecological niche modelling techniques to produce a finer resolution map . Niche models will identify areas suitable for disease transmission that fall outside the actual disease range , unless constrained by information on the geographical extent of the disease . The study published here has used an evidence-based approach to examine the putative range of the disease reservoir and can be used to delineate outputs from studies that use a niche modelling approach to map this disease on a fine scale . Furthermore , the methods developed in this study are broadly applicable and could usefully be extended to other severely neglected vector-borne and/or zoonotic diseases such as scrub typhus or chikungunya . The goal of the map presented here is to provide a comprehensive summary of the current state of evidence for a P . knowlesi reservoir . It is not a map of the likelihood of a reservoir occurring within each area and an area may receive a zero score because the evidence available is lacking or it may in fact be less likely to support an infectious reservoir . This issue is particularly apparent within the Malay Archipelago . Smaller islands are less likely to have evidence for parasite and/or host and/or vector presence , but they may also differ in their underlying ability to support a parasite reservoir . The map shows where evidence is strong and is inherently biased to areas where studies have been conducted . When considering methods to model the probability of occurrence of a reservoir , on a fine-scale , it will be essential to address the issue of sample bias . In order to produce such fine scale maps , more data is needed and the current study has highlighted the types of data and the geographical areas of study that would be most informative , based on our current state of knowledge . | Plasmodium knowlesi is a malaria parasite found in monkeys which can infect humans via mosquito bites . People infected with the P . knowlesi parasite can suffer severe disease and death yet this disease has often been misdiagnosed as a different malaria type and its geographical distribution is largely unknown . The lack of data on human infections in much of Southeast Asia means a simple map of reported cases would likely misrepresent the extent of the disease . Instead we evaluated and ranked a range of evidence types according to how informative they are about the presence of an infection risk to humans and we mapped this ranked information . This highlighted those geographical areas where new data on the monkey and mosquito species involved in the infection of humans would add most to our knowledge of the full range of factors involved in disease risk . The resulting map highlights known locations of the parasite , and areas where presence of the disease in humans is unknown but possible . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"epidemiology",
"global",
"health",
"biology",
"and",
"life",
"sciences",
"microbiology"
] | 2014 | Defining the Geographical Range of the Plasmodium knowlesi Reservoir |
Retroviruses have evolved multiple means to counteract host restriction factors such as single-stranded DNA-specific deoxycytidine deaminases ( APOBEC3s , A3s ) . These include exclusion of A3s from virions by an A3-unreactive nucleocapsid or expression of an A3-neutralizing protein ( Vif , Bet ) . However , a number of retroviruses package A3s and do not encode apparent vif- or bet-like genes , yet they replicate in the presence of A3s . The mode by which they overcome deleterious restriction remains largely unknown . Here we show that the prototypic betaretrovirus , mouse mammary tumor virus ( MMTV ) , packages similar amounts of A3s as HIV-1ΔVif , yet its proviruses carry a significantly lower level of A3-mediated deamination events than the lentivirus . The G-to-A mutation rate increases when the kinetics of reverse transcription is reduced by introducing a mutation ( F120L ) to the DNA polymerase domain of the MMTV reverse transcriptase ( RT ) . A similar A3-sensitizing effect was observed when the exposure time of single-stranded DNA intermediates to A3s during reverse transcription was lengthened by reducing the dNTP concentration or by adding suboptimal concentrations of an RT inhibitor to infected cells . Thus , the MMTV RT has evolved to impede access of A3s to transiently exposed minus DNA strands during reverse transcription , thereby alleviating inhibition by A3 family members . A similar mechanism may be used by other retroviruses and retrotransposons to reduce deleterious effects of A3 proteins .
Reverse transcription ( RTN ) is an essential step in the life cycle of all retroviruses and retrotransposons . It is catalyzed by retroviral reverse transcriptase ( RT ) that converts single-stranded ( ss ) plus-sense viral RNA genomes to double-stranded viral DNA . RTN is a vulnerable step in the retrovirus life cycle . It can be inhibited by host restriction factors including ( i ) TRIM5α , which destabilizes post-entry viral capsids thereby compromising timely onset/completion of RTN [1]; ( ii ) SAMHD1 , which interferes with RTN by depleting cellular dNTP pools [2] and ( iii ) APOBEC3 protein family members ( A3s ) . A3 genes show profound copy number and amino acid variation in mammals . The mouse genome contains one A3 gene ( mA3 ) , whereas the human A3 locus encodes seven A3 proteins that exhibit various degrees of antiviral potency . The most widely studied human restriction factor , A3G , interacts with nucleocapsid protein and viral RNA , thereby ensuring its packaging into viral cores , transmission to target cells and finally its presence in reverse transcription complexes . During RTN , A3G extensively deaminates deoxycytidine to deoxyuridine residues ( C-to-U ) in the negative strand of retroviral DNA , resulting in G-to-A hypermutation in the newly synthesized plus DNA strand [3–5] . As both human A3G and murine mA3 deaminate exclusively ssDNA intermediate products of RTN [6–8] , their antiviral function can be exerted only during a finite period of time when the viral minus DNA strand remains single-stranded . This is determined by the time between synthesis of minus DNA strand , followed by degradation of the RNA template by the RT-associated RNase H activity , and synthesis of the plus DNA strand . As various regions of the minus DNA strand remain single-stranded for a different amount of time , the retroviral genomic DNA contains A3-induced mutational gradient peaking just 5’ ( when considering plus strand sequence ) to the polypurine tract ( PPT ) sequence [6 , 8 , 9] . Given that antiviral activity via detrimental hypermutation is limited in time , it is conceivable that differences in the kinetics of RTN among retroviruses determine their sensitivity to inhibition by A3s . Retroviral RTs are known to substantially differ in their structures and subunit composition as well as in their enzymatic properties [10] . For example , the RT of lentiviruses , including HIV-1 , functions as a heterodimer composed of large and small subunits and exhibits a low processivity [10] . Conversely , RT of the prototypic betaretrovirus , mouse mammary tumor virus ( MMTV ) is active as a monomer and its processivity is substantially greater than that of the HIV-1 RT [11] . Although differences in the rate of DNA polymerization between retroviruses have not been extensively studied , it has been proposed that the rate of DNA synthesis correlates with the RT processivity [12 , 13] . Therefore , we sought to investigate whether retroviruses with markedly distinct RT processivities differ in their sensitivity to inhibition by ssDNA-specific deoxycytidine deaminases . MMTV , which was discovered in the 1930s as a milk-transmitted , infectious agent causing mammary tumors in adult female mice , is one of the best studied oncogenic viruses [14] . The virus is only partially sensitive to inhibition by mA3 and human A3G proteins [15 , 16] . In mA3 knockout mice , MMTV replicates with slightly accelerated kinetics compared to wild-type ( WT ) littermates [15] . Viral particles obtained from mammary glands of MMTV-infected WT mice contain mA3 that is packaged into the cores of virions and retains its deaminase activity . However , the encapsidated mA3 does not hypermutate the MMTV genome [17] . Lack of hypermutation was also reported for MMTV produced in cells expressing human A3G . Although the producer cells expressed A3G at the levels that efficiently repressed infectivity of Vif-deficient HIV-1 ( HIV-1ΔVif ) , only moderate levels of G-to-A mutations of the MMTV genome were observed [16] . These results suggested that MMTV has evolved a mechanism to counteract the deamination activity of A3 proteins allowing replication of the virus in the presence of the restriction factor . This mode of A3 evasion seems to be different from the mechanisms used by other retroviruses to neutralize A3 proteins , such as A3 avoidance or expression of A3-inhibiting accessory proteins ( Vif , Bet ) [18–22] . Here , we aimed to elucidate how MMTV evades accumulation of destructive levels of APOBEC3-induced G-to-A mutations . Direct comparison between MMTV and HIV-1ΔVif revealed that although MMTV does not encode an APOBEC3-neutralizing protein and encapsidates the same amounts of mA3 and A3G as the lentivirus , its genome contains lower levels of A3-mediated G-to-A mutations than HIV-1ΔVif . A potential explanation for the resistance to APOBEC3-induced mutagenesis could be the difference in kinetics of RTN . We tested this hypothesis by directly comparing RTs from the two viruses . We find that the MMTV RT is indeed more processive than HIV-1 RT [11] and also that it exhibits a faster rate of DNA polymerization during RTN . When the rate of DNA polymerization is reduced by mutating the F120 residue in the active center of the DNA polymerase domain of MMTV RT , the mutant virus becomes more sensitive to inhibition by mA3 and A3G and accumulates more G-to-A mutations in its genome . Similar APOBEC3-sensitizing effect can be also observed when the rate of DNA polymerization is reduced by other means including decreased concentrations of deoxyribonucleotides in the cytoplasm of infected cells ( induced by treatment with hydroxyurea ) or the presence of sub-optimal concentrations of an RTN inhibitor that reduces but does not abolish infectivity of MMTV . Collectively , these data provide insight into how the kinetics of RTN may be related to the sensitivity of MMTV to A3s . These findings may point towards a general mechanism used by retroviruses lacking an A3-neutralizing accessory protein to reduce their vulnerability to A3-mediated deamination: evolution of an RT that limits access of APOBEC3s to the RTN intermediate ( -ssDNA ) .
Previous studies suggested that in the presence of mA3 and A3G MMTV accumulates less G-to-A mutations and is inhibited to a lesser extent than other retroviruses , including Vif-deficient HIV-1 ( HIV-1ΔVif ) [15 , 16 , 23] . To test the ability of MMTV to withstand restriction by various A3 proteins , we performed a dose-response analysis in a single-round infection assay using an enhanced green fluorescent protein ( GFP ) -expressing MMTV [24] . Reduction of the MMTV infectivity was compared to the inhibition detected for a control recombinant gfp gene-containing virus derived from HIV-1ΔVif , which is known to be potently inhibited by A3s . The viruses were produced in 293T cells by co-transfecting a packaging plasmid expressing the HIV-1 or MMTV Gag and Pol proteins together with: an HIV-1- or MMTV-based reporter vector ( Fig 1A ) ; a construct expressing the VSV-G envelope protein; a plasmid encoding an RNA export-promoting factor; and with one of two A3 expression construct . We used HA-tagged versions of a potent mouse A3 allelic variant from C57BL/6 lacking exon five ( mA3 ) and a human A3 protein ( A3G ) , the two A3s used in previous studies with MMTV [15 , 16 , 23 , 25] . Initial experiments revealed that transfection of equal amounts of HIV-1 and MMTV packaging plasmids ( 0 . 8 μg ) resulted in more efficient production of lentiviral compared to betaretroviral particles as determined by an RT-qPCR with egfp-specific primers ( both viruses contain egfp gene ) . We anticipated that unequal virus production may affect the comparison of the two viruses with respect to their sensitivity to A3 . Therefore , we titrated the HIV-1 packaging construct to decrease the amount of HIV-1 virions to the levels found in the MMTV preparations ( S1A Fig ) . Transfection of 0 . 2 μg and 0 . 8 μg of the lentiviral and MMTV packaging plasmid , respectively , led to the generation of an equal amount of virions in both preparations ( S1B Fig ) ; hence these amounts were used for further studies . As shown in Fig 1B and 1C , A3G and mA3 proteins were efficiently expressed in the virus-producing cells and their expression level was similar to the A3G levels in a human B-lymphoblastoid cell line , IM9 ( Fig 1C , cells transfected with 40 ng of A3 expression constructs ) . Further , dose-response analysis revealed that although the mouse and human A3s attenuated the infectivity of both viruses in dose-dependent manners , their antiviral potency against the two viruses markedly differed . Specifically , the anti-HIV-1ΔVif effect of the A3G was greater than the anti-viral effect detected for MMTV ( Fig 1D ) . For example , transfection of 40 ng of A3G plasmid to HIV-1ΔVif-producers reduced the HIV-1ΔVif infectivity by approximately fourfold . In contrast , the infectivity of MMTV was reduced by only ~1 . 2 fold . A similar difference in sensitivity to inhibition between the two viruses was also detected for the mA3 ( Fig 1D ) . Thus , this result supports the concept that MMTV is not as potently inhibited as HIV-1ΔVif and suggests that the betaretrovirus has evolved a broadly acting anti-A3 mechanism . Inhibition of retroviral infectivity by A3 proteins usually correlates with an extensive deamination of deoxycytidine in the nascent reverse transcripts . However , previous results with MMTV suggested that the newly synthesized DNA is deaminated to a lesser extent , although the virion-associated mA3 retains its deamination capacity [16 , 17 , 23] . To test whether the lower suppression of MMTV infectivity correlates with a lower frequency of G-to-A mutations in MMTV DNA compared to HIV-1ΔVif DNA , we determined the rate of G-to-A editing in reverse transcripts of both viruses . We took advantage of the fact that both vectors carried an identical 2 . 9 kb-long region spanning the internal part of the vectors ( RRE-egfp-WPRE ) . The use of such vectors eliminates the possibility that the differences in deamination frequency could be attributed to differences in RNA structure , which may cause pausing of RT at sites with a complex RNA folding , resulting in a more frequent RNA cleavage and a higher rate of deamination at these sites . We inspected proviruses generated by virions produced in cells either lacking A3s or expressing one of the A3s , mA3 or A3G ( Fig 1D ) . A ~500 bp-long portion of the viral reverse transcripts derived from the woodchuck hepatitis virus post-transcriptional regulatory element ( WPRE ) present in both viral DNAs in the vicinity of the polypurine tract ( PPT ) was amplified from infected cells and subjected to high throughput sequencing . A comparison of the sequences recovered from infected cells revealed a significantly higher rate of G-to-A editing for HIV-1ΔVif than for MMTV , regardless of whether the viruses were produced in cells expressing mA3 or A3G ( Fig 1E ) . The level of mutations observed in HIV-1ΔVif DNA in the presence of A3G ( 1 . 3/100bp ) and mA3 ( 1 . 4/100bp ) was consistent with previous reports [26 , 27] . The MMTV DNA made in the presence of A3G and mA3 showed a mutation frequency of 0 . 3/100bp and 0 . 32/100bp , respectively . The four-fold difference in deamination rates between MMTV and HIV-1ΔVif reverse transcripts correlated with approximately four-fold less potent inhibition of the MMTV infectivity compared with that of HIV-1ΔVif ( Fig 1 ) . Consistent with previous reports the A3G-induced mutations were preferentially detected in the GG dinucleotide context , whereas the mouse A3-induced changes were more frequently found in the GA dinucleotide-containing substrates ( Fig 1F , S2 Fig ) . Thus , these results suggest that the modest deamination of MMTV reverse transcripts underlies relative resistance of MMTV to A3s . To inhibit infectivity , A3 proteins have to be packaged into the cores of virions , enter target cells within viral cores and exert their inhibitory effect during reverse transcription . Previous reports showed that MMTV packages A3s into the cores [15 , 17 , 23] , however , the levels of packaged proteins was not compared with other retroviruses . Different amounts of packaged A3s may influence the sensitivity of viruses to inhibition by A3s . Therefore , we tested whether the less potent inhibition of MMTV infectivity relative to HIV-1ΔVif results from a less efficient packaging of A3s by MMTV . We produced both viruses in the presence of A3G or mA3 proteins and examined their amounts in the MMTV and HIV-1 virus particles . Equivalent virus levels ( verified by RT-qPCR ) were used as an input for Western blot analysis . As demonstrated in Fig 1B , MMTV incorporated approximately the same amounts of A3G as HIV-1ΔVif . A similar result was obtained with mA3 , which was also efficiently packaged into both betaretroviral and lentiviral virions . Thus , we conclude that poor restriction of MMTV infectivity cannot be explained by a less efficient incorporation of A3 proteins into MMTV particles . Retroviruses are known to express several gene products to antagonize A3s . These include Vif of HIV-1 , which prevents incorporation of A3G into virions by inducing A3G proteasomal degradation [28–32] , or Bet protein of foamy viruses that sequesters A3s away from the virus assembly site , thereby preventing their incorporation into virions [20 , 21 , 33] . To investigate whether MMTV encodes a factor neutralizing the deaminase activity of A3s we established a trans-complementation assay aiming to rescue the infectivity of HIV-1ΔVif produced in the presence of A3G and mA3 , respectively . The virus was generated in mA3- or A3G-expressing 293T cells together with either HIV-1 Vif ( as a positive control ) , MMTV gene products expressed from a complete infectious molecular clone of MMTV ( pGR102 [34] ) or no trans-activating factor ( pcDNA3 ) . We anticipated that if MMTV produces a factor that interacts with A3s and neutralizes their deaminase activity , the presence of the factor should enhance the infectivity of HIV-1ΔVif produced in cells expressing the innate immunity protein . In accordance with previous data [30] , the presence of Vif reduced the A3G expression levels in cells and resulted in an enhanced infectivity of HIV-1ΔVif compared to the virus produced in the absence of trans-complementing factor ( pcDNA3 ) . This effect was specific to A3G as the levels of mA3 protein and the HIV-1ΔVif infectivity remained unchanged when Vif protein was expressed in mA3-producing cells ( Fig 2A and 2B ) . Further , as also shown in Fig 2 , complementation in trans between HIV-1ΔVif and MMTV did not restore the infectivity of HIV-1ΔVif produced in the presence of either A3G or mA3 , even though the MMTV gene products were readily detectable in the transfected cells . These results suggest that the betaretrovirus does not encode a gene product with an A3-counteraction activity . Next , we hypothesized that the poor deamination of the MMTV RTN intermediates can be attributed to an intrinsic property of the MMTV RT . During RTN the deaminase activity of A3s is restricted to deoxycytidines on the ( - ) ss DNA intermediate [6 , 7] . The minus DNA strand is available only for a finite period of time determined by the time span between the minus DNA strand synthesis ( with concomitant degradation of the viral RNA template ) and the generation of the plus DNA complement . Thus , the kinetics of RTN defines the amount of time for which the minus DNA strand is vulnerable to A3s-mediated deamination . Thus , it seems reasonable to assume that differences in the rate of DNA polymerization between viruses may contribute to differences in the magnitude of inhibition by A3s . To analyze differences between the HIV-1 RT and MMTV RT we first conducted a processivity assay with the two RTs . The processivity is defined as the number of deoxynucleotides incorporated into nascent DNA before the enzyme dissociates from the template . The DNA polymerization reaction is performed with an excess of a trap ( heterologous DNA:DNA primer complex ) ensuring that only a single polymerization event is allowed . Virus stocks were produced in the absence of A3s as described for Fig 1 and concentrated by ultracentrifugation . The RT enzyme was released from the virions by a detergent treatment and the virion-extracted enzymes were incubated with a pre-formed MS2 RNA template:MS2 DNA primer complex before addition of a saturating concentration of dNTPs ( 200 μM ) and the trap . Next , the 3’end of synthesized cDNA was dA-tailed using a terminal transferase and amplified by a PCR with dT-anchor- and MS2-specific primers . Length distribution of the amplicons was analyzed by agarose gel electrophoresis . Consistent with previously published data [11] , we observed a marked difference between the two RTs . Whereas the MMTV RT was capable of producing cDNA with a maximal length of ~1 . 5 kb , the HIV-1 RT extended the primer only up to ~0 . 4 kb ( Fig 3A ) . Based on the observed difference in processivity it is tempting to speculate that the two viruses differ in the rate of DNA synthesis . To directly investigate the kinetics of RTN catalyzed by the MMTV RT and HIV-1 RT we set up a DNA synthesis rate assay . The MS2 cDNA was produced by RT extracted from equal amounts of virions ( equal RNA genome equivalents ) for each virus . For this assay electron microscopy was also used to verify equivalent amounts of virions in the preparations ( S3 Fig ) . The advantage of the method is that it quantifies RT activity per a virion rather than the activity of equimolar amounts of recombinant enzymes . The MS2 cDNA synthesis was carried out in the absence of the trap ( to enable multiple rounds of RT binding to the nascent DNA ) and with 50 μM dNTP . The reactions were stopped at various time points following initiation ( 0 , 1 , 2 . 5 , 5 and 10 min ) and the presence of cDNA was analyzed by PCR with MS2-specific primer pairs designed to amplify a ~0 . 4 kb- and 1 . 4 kb-long MS2 cDNA . As shown in Fig 3B ( upper panel ) a low amount of the 0 . 4 kb MS2 cDNA synthesized by the MMTV RT was detectable already 1 min after the initiation of RTN , suggesting that some cDNA synthesis was completed in less than 1 min . The time required by the HIV-1 RT to synthesize the MS2 cDNA was consistently longer compared to that needed by MMTV RT . Approximately 2 . 5 min were required for the extension of the 400 nt from the RTN initiation site to the PCR primer . The difference in the rate of DNA synthesis was more pronounced when the production of the longer cDNA was analyzed . While it took the HIV-1 RT 10 min to synthesize the 1 . 4 kb-long cDNA , the MMTV RT needed only approximately 5 min to complete the cDNA synthesis ( Fig 3B; lower panel ) . Collectively , the presented evidence demonstrates that MMTV RT has a higher processivity and a faster rate of DNA synthesis than HIV-1 RT , further supporting the hypothesis that variations in the DNA polymerization rate during RTN are responsible for differences in the frequency of A3s-mediated mutagenesis . To further investigate whether the faster rate of DNA synthesis catalyzed by the MMTV RT relative to the HIV-1 RT is responsible for the lower frequency of A3-mediated mutagenesis , we generated MMTV RT mutants carrying amino-acid substitutions in the dNTP binding pocket of the enzyme . We hypothesized that , analogously to HIV-1 RT mutants [35 , 36] , reduced dNTP incorporation kinetics reduces the rate of DNA synthesis . As a result of the kinetic interference in DNA synthesis the amount of time required for minus DNA strand synthesis lengthens resulting in a greater frequency of A3-mediated editing of the nascent DNA chain . We targeted the phenylalanine-119 of the MMTV RT that is homologous with Y-115 in HIV-1 RT and F-155 in MLV RT , and is located at the dNTP-binding site ( S4A Fig ) [37 , 38] . Previous work showed that an F119V RT mutant exhibited a reduced dNTP binding and a lower processivity compared with the wild-type ( WT ) RT [37] . However , this mutation had a deleterious effect on the MMTV infectivity that precluded its usage in A3 sensitivity studies . Therefore , we generated a series of mutants carrying either another amino acid substitution at position 119 or a mutation at another residue homologous with the amino acid in HIV-1 RT interacting with incoming dNTPs ( K65 , Q151 , F116 ) [38 , 39] . Whereas infectivity of mutants F119L , F119W , F119P , F119Y , F120W , F120P , F120Y , F120A , D117A , D117E , Q155N and K70R was greatly reduced , a mutant carrying leucine instead of phenylalanine at position 120 retained approximately 20% of the WT virus infectivity ( S4B Fig ) . The mutation did not have a negative effect on the expression of viral proteins nor on A3 packaging into virions and the mutant viral particles were also efficiently released from the virus-producing cells ( Fig 4A–4D ) . We then tested whether the F120L mutation decreased the rate of DNA elongation . First , we compared the mutant with the WT virus in a direct repeat deletion assay that quantifies the frequency of template switching during RTN . Previous reports showed a correlation between the rate of polymerization and the template switching frequency . Genetic alterations of HIV-1 RT that interfered with dNTP binding , such as Q151N , V148I , K65R , Y115F , and F116Y , impeded RT elongation and promoted template switching [35 , 40] . Therefore , we postulated that if the F120L mutation delays minus DNA strand synthesis , we should detect an increased rate of template switching in cells infected with the mutant compared with those infected with WT virus . As described before for other retroviruses [40 , 41] we generated an MMTV reporter vector with a modified egfp gene ( egffp ) containing two directly repeated fragments ( denoted as “f”; 269 bp ) separated by a 24-bp linker . Deletion of the repeat ( f ) , which is mediated by RT and facilitated by base pairing between the synthesized minus DNA strand and a complementary template RNA region upstream of the polymerizing RT , results in reconstitution of an egfp gene and expression of a fluorescent EGFP protein in infected cells . As the amount of time needed for the expression of the 269 bp long repeat region defines the likelihood of hydrogen bonding between the acceptor RNA template and donor cDNA , delayed DNA synthesis increases the frequency of template switching . Using this assay , we found that the F120L mutant recombines significantly more frequently than the WT virus . This was evidenced by two to three-fold increased frequency of gfp reconstitution detected for the F120L mutant relative to WT virus ( Fig 4E ) . The result suggests that the F120L mutation in the active site of the DNA polymerase domain of MMTV RT decreased the rate of DNA elongation during RTN . To provide further evidence for kinetic interference of the mutation on DNA synthesis , we measured the rate of the MS2 cDNA elongation catalyzed by RTs extracted from the same amount of F120L and WT virions as described above . We focused on the longer cDNA product as the difference was more pronounced than for the shorter cDNA . Using the primer pair ( 1252R and 512F ) designed to detect the 1 . 4 kb-long MS2 cDNA we observed that the RT from WT virus needed ~5 min for progression of RTN from the initiation site to the 512F primer . In contrast , RTN products generated by the F120L RT accumulated with a slower kinetics than the cDNA elongated by WT RT . More than 10 min was required by the mutant RT for completion of elongation of the 1 . 4 kb cDNA ( Fig 4F ) . Next , we determined the kinetics of accumulation of MMTV cDNA in infected cells . We performed this analysis by measuring the amount of time required for synthesis of the late RTN products with an MMTV-specific primer pair designed in the U3 ( 1176F ) and the 5’untranslated region ( 1495R ) . As viral RNA is expressed in transfected , virus-producing cells from a hybrid 5’LTR containing the U3 derived from Rous Sarcoma Virus and the R-U5 regions derived from MMTV ( Fig 1A ) , an authentic MMTV 5’LTR that allows amplification of a 321 bp-long fragment of viral DNA is formed after the plus-strand DNA transfer [24] . As shown in Fig 4G , synthesis of the late WT and F120L RT products peaked at 16 h and 32 h post-infection , respectively . Thus , this analysis confirmed the in vitro results obtained with the MS2 RNA and established that the mutation delays the authentic MMTV DNA synthesis in cells . Of note , we also observed that although we used the same amount of viral particles for infection ( egfp gene levels normalized ) , an approximately four-fold lower amount of F120L DNA compared with WT DNA was produced . Mechanistic nature underlying the defective synthesis of the late viral DNA products in the cytoplasm of infected cells is not known . We are favoring the possibility that the delayed completion of DNA synthesis may allow recognition of viral DNA as “non-self” followed by its degradation by intrinsic cellular restriction factors . Collectively , the in vitro MS2 cDNA synthesis assay and the authentic MMTV late RT synthesis assays showed a slower rate of DNA synthesis for the virus containing mutated RT . Next , we tested whether the F120L mutation has an effect on the sensitivity to inhibition by A3s . Dose-response analyses presented in Fig 4H showed that both A3G and mA3 more effectively inhibit the F120L mutant compared to the WT virus . For example , the A3G expression level , resulting from transfection of 40 ng of A3G plasmid to virus-producing cells ( which is similar to the endogenous A3G level expressed in IM9 lymphocytes ( Fig 1C ) ) , inhibited the F120L infectivity approximately three-fold . In contrast , the infectivity of wild-type virus was reduced by only <1 . 3 fold ( Fig 4H , left ) . Similar results were obtained with viruses produced in the presence of mA3 ( Fig 4H , right ) . Next , we recovered the newly synthesized proviral DNA from infected cells and subjected it to high throughput sequencing . As shown in Fig 4I and 4J , we observed a significantly higher frequency of G-to-A editing in the F120L proviral genomes than in the wild-type virus ( 0 . 77 vs 0 . 18 / 0 . 1kb ) . Whereas G-to-A transitions in sequences derived from the WT virus were distributed over 14 guanosine residues , sequences derived from the F120L mutant carried G-to-A mutations at 25 guanosine positions of the 270 nt-long sequenced region . Furthermore , of the 14 guanosine positions mutated in both proviruses all were mutated in the F120L proviruses with a greater frequency than in the WT viruses ( Fig 4J , upper panel , S5 Fig ) . As expected the vast majority ( 93% for F120L and 85% for WT ) of the mutations caused by A3G were in GG dinucleotide context ( Fig 4I , right ) . Further , similar to A3G , analysis of sequences derived from viruses containing mA3 revealed that the mutation in the active center of RT predisposed MMTV to the more frequent accumulation of G-to-A mutations ( 0 . 41 vs 0 . 16 / 0 . 1 kb ) . Although the overall mutation frequency was lower compared to A3G , more guanosine positions in the analyzed region were mutated ( 38 for F120L and 24 for WT ) , suggesting that mA3 is less strict in the selection of substrate than A3G ( Fig 4J , lower panel; S5 Fig ) . This was also reflected in the dinucleotide preference of mA3 , which , when compared to A3G , showed a greater deamination frequency of GT , GA or GC substrates ( Fig 4I , right ) . Taken together , these results demonstrate that the F120L mutation in RT renders MMTV more susceptible to inhibition by A3s and that this phenotype results from elevated G-to-A editing frequency of the mutant . To further test whether the rate of DNA synthesis influences the sensitivity of MMTV to A3s we analyzed the A3-mediated inhibition in cells grown in the presence or absence of an inhibitor of cellular ribonucleotide reductase , hydroxyurea ( HU ) . We hypothesized that a reduction of intracellular dNTP levels resulting from HU treatment [42] reduces the rate of reverse transcription [43 , 44] . This leads to a prolonged exposure of the minus DNA strand to A3s and more efficient neutralization of MMTV infectivity compared with the infection of untreated cells . To test this hypothesis , 293T target cells were treated with 0 . 2 mM HU 4 h prior to infection , 3 h during infection and 17 h post-infection [40] resulting in a reduction of intracellular dNTPs levels and a reversible change of cellular morphology ( S6 Fig ) . Furthermore , the treatment delayed the onset of eGFP expression following infection , indicating that the duration of reverse transcription was prolonged as anticipated ( S6B Fig ) . The inhibition of infectivity of MMTV produced in the presence of increasing amounts of A3G was analyzed as described for Fig 1D and Fig 4H . The data presented in Fig 5A show that the presence of HU sensitized MMTV to inhibition by A3G as evidenced by a steeper decline of the virus infectivity in the presence of HU . Further , MMTV was also more sensitive to inhibition by mA3 as evidenced by significantly lower virus infectivity in target cells that were exposed to HU compared to unexposed cells ( Fig 5B ) . Next , we tested whether other means of inhibition of the kinetics of reverse transcription , such as the presence of an inhibitor of RT , affects the sensitivity of MMTV to inhibition by A3s . As shown in Fig 5C and 5D , similar infectivity profiles as those seen with HU-treated vs . HU-untreated cells were detected when differences in infectivity in the absence or presence of an inhibitor of RT ( 3’-azido-3’deoxythymidine , AZT ) were followed ( Fig 5C and 5D ) . For this analysis , we worked on the presumption that the nucleoside RT inhibitors including AZT act as chain terminators following their incorporation to the DNA during RTN . However , they may be , under suboptimal non-lethal concentrations , excised from the 3’end of nascent DNA by a pyrophosphorolysis allowing continuation of the DNA strand elongation [45] . We assumed that the termination of elongation followed by pyrophosphorolysis delays DNA synthesis allowing a more potent inhibition by A3G . To perform this analysis we infected cells with either no drug or with a suboptimal concentration of AZT ( 70nM ) , which reduced the infectivity of MMTV by ~ two-fold , and tested whether the treatment has an effect on the sensitivity of MMTV to A3G . We found that the presence of AZT resulted in a significant increase of sensitivity to inhibition by A3s ( Fig 5C and 5D ) further confirming our the concept that the rate of RTN is an important determinant of the sensitivity of MMTV to A3s .
Previous reports showed that MMTV can replicate in cells producing mA3 or A3G and that proviruses recovered from these cells do not carry an extensive deamination signature of the innate immunity factors [15–17] . The mechanism underlying the ability of MMTV to escape detrimental G-to-A mutagenesis remained unknown . To shed more light on the interplay between MMTV and A3 proteins ( A3s ) we initially compared a Vif-deficient HIV-1 with the betaretrovirus with respect to their sensitivity to inhibition by various A3s . We found that MMTV is less sensitive to inhibition by mA3 and A3G than the lentivirus lacking Vif . The observation that MMTV partially evades A3s derived from several mammalian species suggested that the virus uses a broadly acting mechanism to escape the lethal effect of A3s . Furthermore , sensitive high-throughput sequencing confirmed the previously reported low level of G-to-A editing of the MMTV reverse transcripts produced from virions derived from cells expressing mA3 or A3G . Specifically , side-by-side comparison between MMTV and HIV-1ΔVif revealed a four-fold lower magnitude of deamination for the RT intermediates of the betaretrovirus . As both viruses carry an identical RRE-egfp-WPRE RNA segment , it is unlikely that the differences result from a different composition of cellular RNA binding proteins deposited on the viral RNA , a variation in RNA structure and/or thermodynamic stability that may affect RT pausing during cDNA synthesis . Furthermore , the data presented here do not support the concept that an inefficient encapsidation of A3 proteins or the expression of a gene product that counteracts the antiviral activity of the innate immunity factors is responsible for the low editing levels . Instead , the observations that a cell culture condition ( e . g . a limiting dNTP concentration or the presence of a RT inhibitor ) or a mutation in the RT coding region that slows down RT polymerization but does not affect A3G encapsidation , significantly increases G-to-A mutation levels and sensitizes MMTV to A3G support the view that MMTV has evolved RT to catalyze DNA polymerization with a rate that does not allow a high level of A3-mediated deamination of reverse transcription ( RTN ) intermediates . Given that A3s can deaminate only single-stranded ( ss ) DNA , the amount of time for which DNA remains single-stranded determines the period of time the ssDNA is exposed to A3s and , in turn , the sensitivity to editing by A3s . The key features of RTN which affect sensitivity to deamination are diagrammed in Fig 6 and summarized as follows . After minus strand strong-stop DNA strand transfer to the 3’ end of the viral genomic RNA , elongation of the minus DNA strand continues on a polypurine tract region ( PPT ) towards a primer binding site ( PBS ) . Concomitant with the synthesis of the minus DNA strand , the RNase H activity of RT cleaves the newly copied RNA genome from the RNA/DNA heteroduplexes to short oligonucleotides , many of which may dissociate from the nascent DNA and expose it to A3s . Later , when the synthesis of the minus DNA strand reaches the PBS region at the 5‘end of viral RNA , a second strand transfer occurs . This allows the continuation of synthesis of the minus and plus DNA strands resulting in the generation of the double-stranded DNA , which is protected from A3s . The time span between degradation of template RNA and production of plus DNA strand determines the time for which a region remains single-stranded and hence vulnerable to deamination . We propose that the fast rate of DNA polymerization detected for the WT MMTV RT shortens the time for which viral DNA remains single-stranded and hence available for A3-mediated deamination ( Fig 6 ) . This model is reminiscent of previously reported correlation between the frequency of template switching and the rate of DNA polymerization during RTN [40 , 41] . Similar to our results , a cell culture condition or mutation that slows down the rate of RTN increased the frequency of template switching . The template switching events are necessary for the completion of RTN , therefore retroviral reverse transcriptases have evolved to possess low template affinity and processivity [46] . Most of the recombination events occur during minus DNA strand synthesis and rely on base pairing between the acceptor RNA sequences downstream of recombination crossover and the nascent complementary minus DNA strand sequences unmasked by RNase H activity ( dynamic copy-choice model , [41] ) . Thus , the RNase H function of RT is essential for the production of ssDNA required for the template switching . Two different modes of RNase H activity are involved in the degradation of RNA and the generation of ssDNA . The first mode is carried out by the same RT molecule performing DNA synthesis . This mode is not sufficient to completely remove RNA from the RNA/DNA duplexes , because the polymerization rate of RTs is faster than the RNase H rate [47 , 48] . The residual RNA fragments are cleaved by a polymerase-independent mode of hydrolysis catalyzed by free RTs , which are also present in reverse transcription complexes [49–52] . Conditions that reduce the rate of polymerization permit more efficient degradation of the template RNA and provide more time for hydrogen bonding between ( - ) ssDNA and the acceptor template , resulting in a greater frequency of template switching [40 , 41] . Analogously , a mutation in the DNA polymerase domain or a cell culture condition that prolongs the elongation of the minus DNA strand increases the time during which the RNase H can degrade the RNA template and expose the nascent ( - ) ssDNA to A3s . In fact , both the polymerase-dependent and polymerase-independent RNase H activities may be important for the sensitivity to inhibition by A3s . Relative contribution of the two RNase H modes is not known . Previous studies showed that the magnitude of the primary RNA cleavage by RNase H during DNA synthesis differs among reverse transcriptases . For instance , the avian myeloblastosis virus reverse transcriptase cleaves RNA strand only once for every 100–200 nt , whereas the HIV-1 reverse transcriptase generates RNA fragments that are 100–120 nt long [47 , 48] . Therefore , it may be possible that viruses containing reverse transcriptase with more active DNA polymerase domain relative to the RNase H domain are inhibited by A3 proteins to a lesser extent than viruses with a slower DNA polymerase domain because they generate fewer primary RNA cuts per dNTP addition . The lower frequency of RNA degradation by reverse transcriptase , which is at the same time polymerizing ( polymerase-dependent RNase H activity ) , creates fewer primary cuts that are accessed by A3 proteins as soon as short DNA single strands are exposed . The immediate association of A3 proteins with target sites may be facilitated by nucleocapsid proteins that interact with A3 proteins and function as chaperones during reverse transcription [53] . It may also be conceivable that the primary RNA cuts do not create single-stranded DNA regions long enough to be targeted by A3 proteins . In such a scenario , the decreased frequency of deamination results from a delayed formation of sufficiently large single-stranded DNA regions by RNase H , which is not coupled to DNA synthesis ( polymerase-independent RNase H activity ) . Virions contain an excess of reverse transcriptase molecules ( 50–100 ) that do not participate in DNA synthesis [54] . They are believed to degrade the RNA segments that remain bound to minus sense DNA following DNA synthesis accompanied with the primary RNA degradation by polymerase-dependent RNase H activity [55] . The two distinct modes of this polymerase-independent RNase H activity are referred to as RNA 5’ end-directed cleavage and internal cleavage . Internal cleavage does not involve positioning by either a DNA 3’ end or an RNA 5’ end and the most important determinant appears to be the RNA sequence in the vicinity of the cleavage site [56] . For RNA 5’ end-directed cleavage , which is kinetically favored over internal cleavage , the polymerase active site of reverse transcriptase is positioned on the DNA strand near the 5’ RNA end such that cleavages occur 13–19 nt from the RNA 5’ end [48 , 57 , 58] . It is conceivable that a lower frequency of the primary RNA cuts mediated by the polymerase-dependent RNase H provides fewer 5’ RNA ends accessible for reverse transcriptase . This leads to less efficient degradation of RNA by polymerase-independent RNase H . Thus , decreased polymerase-dependent RNase H activity leads to less efficient degradation of viral RNA and in turn to a reduction of the length of single-stranded DNA segments . The shorter single-stranded DNA regions narrow the window of opportunity for A3 proteins to bind and deaminate their substrate . Although it has not been explicitly shown here , the F120L mutation in DNA polymerase domain likely decreased the ratio of DNA polymerase to RNase H activity , resulting in an increase of the number of primary RNA cleavages catalyzed by the polymerase-dependent RNase H activity [41 , 48 , 59 , 60] . Additionally , a slower kinetics of the minus DNA strand synthesis increases the amount of time during which the polymerase-independent RNase H can further degrade the template RNA . Our results and the proposed model are strongly supported by a recent study of the Harris group , which demonstrated that the Vif-mediated proteasomal degradation of A3G may not be the only mechanism by which HIV-1 counteracts the innate immunity factor [61] . Strikingly , they detected three Vif-null HIV-1 variants capable of replication in T cells expressing restrictive amounts of A3G with kinetics that were not markedly different from the kinetics of Vif-proficient parental HIV-1 virus . Importantly , all three variants carried mutations that facilitated packaging of Gag-Pol polyproteins resulting in elevated amounts of RT molecules in virions . The higher RT levels lead to a faster accumulation of viral DNA in infected cells , decreased opportunities for A3G-catalyzed deamination and to lover levels of G-to-A mutations and restrictions [61] . Interestingly , the study also revealed that only relatively small changes in RT activity ( ~3-fold increase of accumulation of the late RT products ) resulting in ~2-4-fold decrease in the G-to-A levels may have large phenotypic consequences such as enabling virus replication in the presence of A3G . Taken together , our results support the concept that the MMTV RT has evolved to protect the viral genome from excessive A3-mediated deamination and at the same time to govern template switching with an efficiency that is sufficient for the production of viral DNA at the level ensuring MMTV replication in the host . Our findings combined with a recent study from the Harris lab points towards an emerging and potentially general mode of evasion from deleterious A3-driven hypermutation that may be employed by other RT-containing viruses and mobile elements . This may apply especially , but not exclusively , to viruses that do not encode a Vif- or Bet-like gene product or that do not escape restriction by evolving an A3-unreactive nucleocapsid . The mechanism described here suggests that some retroviruses might have evolved to coexist and perhaps even benefit from the “tolerated” sublethal mutagenic activity of A3s to fuel viral heterogeneity and immune escape .
Plasmids encoding HA-tagged hA3G , hA3F , rhA3G and mA3ΔE5 ( Balb/c ) in a pcDNA3-based vector were kindly provided by B . Cullen [62] . The HA-tagged version of mA3ΔE5 ( CL57BL/6 ) was generated by exchanging A3 coding region in the mA3ΔE5 ( Balb/c ) plasmid [63] . Non-HA tagged versions of the A3-encoding plasmids were generously donated by M . Malim . The MMTV-based expression plasmids pRRpCeGFPWPRE25 and pCMgpRRE17 have been described previously [24] . The HIV-1 packaging ( pMDLg/pRRE ) and vector constructs ( pRRL-cPPT-GFP ) , as well as the VSV-G ( pHCMV-G ) and Rev ( pRSV-Rev ) expression plasmids , have been described previously [64 , 65] . To generate the MMTV packaging construct mutants PCR-based molecular cloning approaches were used ( primers are shown in S7 Fig ) . 293T , HeLa and NMuMG cells ( all obtained from ATCC ) were cultured in Dulbecco’s modified Eagle’s medium containing 10% fetal calf serum ( FCS ) . IM9 cells ( obtained from ATCC ) were cultured in RPMI-1640 medium with 10% FCS . For virus production , a total of 2 x 106 293T cells ( each well of a six-well plate ) were transfected using polyethylenimine ( lin . PEI; 25 kDa ) and 10 ng , 40 ng or 160 ng of an A3 expression plasmid . In addition , cells were transfected with pCMgpRRE17 ( 0 . 8 μg ) and pRRpCeGFPWPRE25 ( 1 μg ) ( for MMTV production ) or with pMDLg/pRRE ( 0 . 2 μg ) and pRRL-cPPT-GFP ( 1 μg ) ( for HIV-1 production ) [24] . Initially , we observed that the production of HIV-1 virions is more efficient compared to MMTV . Therefore , we titrated the amount of the HIV-1 packaging construct ( pMDLg/pRRE ) to obtain an equivalent number of viral particles in the cell culture supernatants of cells producing both viruses . Lowering of the pMDLg/pRRE amount to 0 . 2 μg resulted in equivalent amounts of MMTV and HIV-1 virions in cell culture media as determined by a gfp-specific RT-qPCR and by EM ( see below; S1 and S3 Figs ) . All viruses were pseudotyped by addition of 0 . 3 μg of VSV-G expression plasmid ( pHCMV-G ) to transfection mixture . An efficient virus production was supported by adding Rev-encoding plasmid pRSV-Rev ( 0 . 6 μg ) to the transfection cocktail . pcDNA3 filler plasmid was used to equalize DNA content . Sixteen hours post-transfection DNase I ( 10 U / ml ) and MgCl2 ( 4 mM ) were added to the cell culture medium . After one-hour incubation at 37 wC , the nuclease-containing medium was replaced with a fresh DMEM supplemented with 10% FCS . Forty-two hours post transfection the virus-containing was medium filtered ( 0 . 45 μm ) , serially diluted and used to infect naïve cells seeded at a density of 3 x 105 cells per well in a six well plate . Polybrene ( 8 μg / ml ) was used to facilitate the attachment of viruses to the cell membrane . The GFP-positive cells were quantified by flow cytometry forty-eight hours post infection . The virus dilution infecting up to 20% of target cells was used to calculate the virus titer and infectivity . To determine processivity of RTs , virions were harvested from transfected cells , concentrated by ultracentrifugation and viral loads determined by RT-qPCR and verified by EM as described above . Equivalent amounts of viral particles ( adjusted to a total volume of 5 μl ) were mixed with 2 x concentrated lysis buffer ( 40 mM Tris-HCl ( pH 8 ) , 50 mM KCl , 20 mM DTT , 0 , 2% Triton-X100 ) and incubated for 10 min at room temperature to release RTs from virions . Next , the liberated RTs were diluted ( 10 x ) in a dilution buffer ( 20 mM Tris-HCl ( pH 7 , 5 ) , 50 mM KCL , 0 , 025% Triton X-100 , 0 , 2 mM DTT , 10% glycerol ) and added to a pre-formed MS2_1684R primer/MS2 bacteriophage RNA complex ( in a molar excess , 250 pmol ) . The complex was formed by mixing the equimolar amount of the MS2 RNA ( Roche ) and 20-mer oligonucleotide ( MS2_1684R: AGA GAA AGA TCG CGA GGA AG ) , heating the mixture at 65°C for 5 min and cooling it down on ice for 1 min . The final reaction mixtures contained 50 mM Tris-Cl , 75 mM KCL , 3 mM MgCl2 , 10 mM DTT . Reaction was initiated by adding four dNTPs at a final concentration of 200 μM followed by incubation at 37°C . A DNA trap ( 100 pmol; S7 Fig ) was added to the mixture one minute after the initiation of reaction when an RT/MS2 primer/MS2 RNA complex was formed . The reaction was carried out for an additional 15 min and terminated by heating to 95°C for 5 min . To determine the size of RT products , cDNA was purified using Monarch PCR clean up kit ( NEB ) and the 3’ends were A-tailed using a terminal transferase ( Roche ) . The reaction mixture contained the MS2 cDNA , 1 x terminal transferase buffer ( NEB ) , CoCl2 ( 250 μM ) and dATP ( 100 μM ) . Samples were primed at 94°C for 3 min followed by 1 min on ice prior to addition of the terminal transferase ( 1 U ) and incubation at 37°C for 30 min . Following inactivation of the terminal transferase ( 70°C for 10 min ) , 1/10 of the A-tailing reaction volume was used for a PCR with oligo ( dT ) -anchor- ( GAC CAC GCG TAT CGA TGT CGA C- ( T ) 30-V ) and MS2-specific ( 1583R: CGG CTA CAG GAA GCT CTA C ) primers . PCR products were visualized on 1 . 5% agarose gels . The rate of DNA polymerization during RTN was determined using virion-derived reverse transcriptases and the MS2 RNA as template . Concentrated viral stocks were prepared , quantified and the amount used for testing was normalized as described above . Lysed and diluted virus preparations were added to pre-annealed MS2_1934R primer/MS2 RNA ( 25pmol ) ( MS2_1934R: GGA TCC CAT GAC AAG GAT TT ) . RTN that was carried out in the absence of the trap in a reaction mixture containing 50 mM Tris-Cl , 75 mM KCL , 3 mM MgCl2 , 10 mM DTT and 10 μM dNTPs was terminated 0 min , 2 . 5 min , 5 min , 10 min or 40 min after the RTN initiation ( 95°C , 5 min ) . Next , a PCR was used to detect the presence of 1 . 4 kb- and 0 . 4 kb-long cDNA products with primer pairs MS2_1252R ( TTC CGC CAT TGT CGA CGA G ) plus MS2_512F ( TCC GCT ACC TTG CCC TAA AC ) and MS2_1934R plus 1523F ( TTA AGG CAA TGC AAG GTC TC ) , respectively . Unconcentrated virus stocks were used for infection of target 293T cells seeded in 6 well plates . Virus inoculum was removed two hours post-infection , cells rinsed with PBS and fresh DMEM supplemented with 10% FCS was added . Total cellular DNA was harvested from the infected cells at desired time points using DNeasy Blood and Tissue kit ( Qiagen ) and used in a qPCR with MMTV-specific primers designed to amplify the late RT products ( 1176F: TTC CTG ACT TGG TTT GGT ATC AAA TG and 1495R: AGA CAA GGG TCA CTT ATC CGA G ) using a GoTaq Probe qPCR master mix ( Promega ) . A 321 bp-long PCR product can be detected only after the second DNA strand transfer because the viral RNA is produced from a hybrid LTR consisting of the U3 region derived from RSV and RU5 derived from MMTV . Amplification results were normalized to actin DNA levels determined in the samples with actin5F ( CTT CTG CCG TTT TCC GTA GG ) and actin3R ( TGG GAT GGG GAG TCT GTT CA ) primers . Oligonucleotides designed to introduce individual mutations ( S7 Fig ) to the RT-encoding portion of the pol gene in the MMTV packaging construct , pCMgpRRE17 , were used for PCR ( Q5 High-fidelity DNA polymerase , NEB ) . The products generated using F119_R and one of the F119X_F ( X = V , L , W , P or Y ) or F120Z_F ( Z = V , L , W , P , Y or A ) primers were digested with Bgl II and ligated with a large fragment generated from the packaging construct by Bgl II digestion . Mutations Q155N and K70R were first introduced to a long PCR product ( 2 . 6 kb ) by an overlap extension PCR . Next , the amplicon was digested with XhoI and EcoRI and ligated with a large fragment resulting from XhoI-EcoRI digestion of the packaging construct . The presence of desired mutations was verified by DNA sequencing . The packaging of A3 proteins into MMTV and HIV-1 virions was analyzed as follows . 293T cells were transfected as described above . Virus-containing supernatant was collected , clarified by low-speed centrifugation , filtered through a 0 . 45 μm filter and layered onto a 20% sucrose cushion . Virions were pelleted by ultracentrifugation ( 50 000 g , 2h , 4°C ) , re-suspended in PBS ( 50 μl ) and stored at -80°C . An aliquot was used for RNA extraction as described above . DNase-treated ( Turbo DNA-free kit; Ambion ) viral RNA was quantified using the TaqMan RT-PCR as described above . The determined virus loads were used to normalize inputs for an immunoblot analysis . Aliquots containing equal amounts of total proteins from cell lysates ( 10 μg of protein , DC protein assay , BioRad ) and virion lysates ( egfp levels normalized ) were subjected to gel electrophoresis . After blotting to a PVDF membrane , proteins were detected using an antibody specific to HA-tag ( Roche ) , hA3G ( Abcam ) , HIV-1 p24 ( Polymun ) , MMTV CA ( p27 ) ( a gift from A . Mason ) , actin ( Sigma-Aldrich ) , HSP90 ( Santa Cruz ) . Antibody-reactive proteins were detected with horseradish peroxidase-conjugated secondary antibody ( DAKO ) and ECL plus substrate ( GE Healthcare ) . HIV-1 vector was produced by transient transfection as described above . Transfection cocktail contained an A3G-expression plasmid ( 40 ng ) or parental pcDNA3 ( 40 ng; no A3G control ) . In addition , the transfection mixture was in respective cases supplemented with the complete molecular clone of MMTV pGR102 [34] ( 0 . 2 μg; a gift from B . Salmons ) or with pcDNA3 ( 0 . 2 μg ) . As a positive control Vif-expression plasmid ( 0 . 2 μg; a gift from M . Malim ) was used . The infectivity of viral vectors was assessed on naïve 293T cells . The genomic DNA was extracted from cells ( 2dpi ) infected with MMTV WT , MMTV F120L or HIV-1 produced in the absence or presence of hA3G or mA3 ( 40 ng ) . Segments of proviral DNA common for all viruses were amplified using the Q5 High-Fidelity DNA polymerase ( NEB ) and the primers: WPRE_OUT_EcoRI_F1 ( 5'-GCC CCG GAA TTC TGC CCG ACA ACC ACT ACC T-3' ) or and WPRE_OUT_XhoI_R1 ( 5'-AAG GGA CTC GAG ACT CGT CTG AGG GCG AAG-3' ) . The amplicons were digested with XhoI and EcoRI , cloned into pcDNA3 linearized with the same enzymes , transformed into TOP 10 cells ( Invitrogen ) and plated onto twenty agar plates . Plasmid DNA was extracted from colonies scraped from the plates . The resulting library was sequenced by GS FLX system using pcDNA3-specific primers ( S7 Fig ) . High-quality reads were aligned to vector sequence using DNASTAR mapper , exported to Microsoft Excel and manually curated . The frequency of G-to-A transitions was calculated using VBA scripts . The frequency of G-to-A transitions in the sequenced segments of the MMTV provirus was compared to the frequency of the G-to-A mutations in the HIV-1 genomes and the P values of any differences were determined using the χ2 test . Sequence analysis of the F120L mutant vs WT MMTV proviruses was performed using amplicons generated with WPRE-specific primers shown in S7 Fig . The amplicons were subjected to high throughput sequencing ( MiSeq , 300 bp ) . The sequences were demultiplexed mapped to the reference genome using a Bowtie2 algorithm and 2500 randomly selected sequences were used for a Hypermut analysis . Raw MiSeq sequencing files are publicly available at the European Nucleotide Archive ( ENA; www . ebi . ac . uk/ena ) under accession numbers: ERS2953115—ERS2953118 . The direct repeat deletion assay was performed as described previously [41] . Briefly , an MMTV-based vector ( pRRpCeWPRE25_GFFP ) containing an egfp gene carrying a 269 bp-long duplication of an internal segment and a 25 bp-long spacer between the repeats ( egffp ) was constructed using a PCR-based approach with the following primer pairs: GFP_F1_BamHI ( 5’-CGA CTC TAG AGG ATC CAC CG-3’ ) and GFP_R1_Xho ( 5’-GAA TTC CTC GAG TTG AAG TCG ATG CCC TTC AG-3’ ) ; GFP_F2_link_Xho ( 5’-GAA TTC CTC GAG GAT ATC TAA GTG ACT CCT GAC CCT GAA GTT CAT CTG-3’ ) and GFP_R2_BsrGI ( 5’-CCG CTT TAC TTG TAC AGC TC-3’ ) . Amplicons were ligated and introduced into BamH I-BsrG I-digested pRRpCeGFPWPRE25 vector . Next , an IRES-puro cassette was amplified and introduced into BsrG I-Not I-cleaved pRRpCeWPRE25_GFFP . The resulting vector plasmid carrying egffp and pac ( puromycin resistance ) genes was named pRRpCgW_GFFPiresPURO . Virus-containing egffp-IRES-puro cassette was produced from 293T cells transfected with the vector plasmid , helper plasmids pCMgpRRE17 and pRSV-Rev and finally with plasmid encoding VSV-G Env . Virus-containing supernatant was filtered , diluted to achieve the multiplicity of infection of < 0 . 01 and then used for infection of 293T target cells . Cells were selected for resistance to puromycin ( 2 μg/ml ) and the resulting colonies were pooled and analyzed by flow cytometry . To quantify viral particles produced from transfected cells , the cell culture supernatants were treated with DNase I ( 10 U /ml , Roche ) in the presence of MgCl2 ( 4 mM ) for 1 h at 37°C prior to the extraction of viral RNA using QIAamp viral RNA kit ( Qiagen ) . For some applications ( immunoblots , in vitro reverse transcriptase assays ) , the virus preparations were first concentrated by ultracentrifugation ( see below ) prior to RNA extraction . The extracted RNA was treated with TURBO DNA-free ( Ambion ) then incubated with MuLV RT at 50°C for 45 min ( RT+ samples ) . Parallel samples were incubated in the absence of MuLV RT to determine carryover contamination from plasmid DNA ( RT- samples ) . The cDNA was used in a qPCR with egfp-specific primers ( 214F:GCA GTG CTT CAG CCG CTA C; 309R:AAG AAG ATG GTG CGC TCC TG ) , a TaqMan probe ( 6FAM-CCG ACC ACA TGA AGC AGC ACG ACT T-TAMRA ) and a Luna Universal qPCR Master mix ( NEB ) . The cycling conditions were: initial denaturation at 95°C for 1min followed by 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 30 s . For quantification by TEM , all wells of a six-well plate were transfected as described above . Medium was collected 42 h after transfection ( 12 ml per virus ) , clarified by a low speed centrifugation ( 1000 g , 10 min ) and by filtration ( 0 . 45 μm ) . Viral particles were pelleted by ultracentrifugation at 50 000 g for 2h ( SW41; 4°C ) and re-suspended in 1/200 of the initial volume ( PBS ) . Virions were deposited on Formvar carbon-coated copper grids and stained with 4% phosphotungstic acid ( 10 min at room temperature ) . Fifty grid spaces were inspected by TEM ( TE 900 , Carl Zeiss ) , any retrovirus-like particles were counted and a mean number of virions per slide was calculated . Treatment with hydroxyurea ( HU ) was performed as described previously ( Julias et al . , 1998 ) . Briefly , the target cells were incubated in the HU-lacking or HU-containing medium ( 0 . 2 mM HU ) 4 h before infection as well as 20 h after infection to ensure that reverse transcription is ongoing in the cells with altered dNTP pools . To verify that the treatment inhibited ribonucleotide reductase leading to a decrease of dNTP concentrations , the intracellular dATP levels were quantified as described previously ( Wilson et al . , 2011 ) . Briefly , 0 . 6 x 106 cells cultured in the HU-lacking or HU-containing medium were trypsinized , washed twice in PBS and pelleted by a low-speed centrifugation . The pellets were re-suspended in cold 60% methanol , incubated at 95°C for 3 min and sonicated for 30 s . The extracts were centrifuged ( 16 000g , 5min , 4°C ) and the supernatant passed through Amicon Ultra 0 . 5 ml ( MWCO 3 kDa ) . The filtrate was evaporated under centrifugal vacuum , the pellet re-suspended in nuclease-free water and used for a primer extension reaction in an AB 7500 Real-time PCR System with the NDP-1 primer ( CCG CCT CCA CCG CC ) , FAM-dATP probe ( 6FAM/TGG TCC GTG/ZEN/GCT TGT GCG TGC GT/IBFQ ) , dATP-DT2 template ( ACG CAC GCA CAA GCC ACG GAC CAA ATA AAT AAA GGC GGT GGA GGC GG ) , dNTPs ( excluding dATP ) , AmpliTaq Gold polymerase and 10 x PCR buffer II . The polymerase was activated at 95°C for 10 min the primer extension was carried out at 60°C for 30 min with raw fluorescence spectra for 6-FAM acquired every 5 min . For treatment with 3’-azido-3’-deoxythymidine ( AZT , 70 nM ) , target cells were infected with MMTV and at the same time , the drug was added to the inoculum . Fresh medium containing AZT was added 3 h post infection . Note that AZT was titrated and the concentration that was used ( 70 nM ) reduced the titer of MMTV to approximately 50% of the control virus titers ( S8 Fig ) . To quantify viral particles produced from transfected cells , the cell culture supernatants were treated with DNase I ( 10 U /ml , Roche ) in the presence of MgCl2 ( 4 mM ) for 1 h at 37°C prior to the extraction of viral RNA using QIAamp viral RNA kit ( Qiagen ) . For some applications ( immunoblots , in vitro reverse transcriptase assays ) , the virus preparations were first concentrated by ultracentrifugation ( see below ) prior to RNA extraction . The extracted RNA was treated with TURBO DNA-free ( Ambion ) then incubated with MuLV RT at 50°C for 45 min ( RT+ samples ) . Parallel samples were incubated in the absence of MuLV RT to determine carryover contamination from plasmid DNA ( RT- samples ) . The cDNA was used in a qPCR with egfp-specific primers ( 214F:GCA GTG CTT CAG CCG CTA C; 309R:AAG AAG ATG GTG CGC TCC TG ) , a TaqMan probe ( 6FAM-CCG ACC ACA TGA AGC AGC ACG ACT T-TAMRA ) and a Luna Universal qPCR Master mix ( NEB ) . The cycling conditions were: initial denaturation at 95°C for 1min followed by 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 30 s . For quantification by TEM , all wells of a six-well plate were transfected as described above . The medium was collected 42 h after transfection ( 12 ml per virus ) , clarified by a low-speed centrifugation ( 1000 g , 10 min ) and by filtration ( 0 . 45 μm ) . Viral particles were pelleted by ultracentrifugation at 50 000 g for 2h ( SW41; 4°C ) and re-suspended in 1/200 of the initial volume ( PBS ) . Virions were deposited on Formvar carbon-coated copper grids and stained with 4% phosphotungstic acid ( 10 min at room temperature ) . Fifty grid spaces were inspected by TEM ( TE 900 , Carl Zeiss ) , any retrovirus-like particles were counted and a mean number of virions per slide was calculated . | Retroviruses have evolved multiple means for evading host restriction factors such as APOBEC3 proteins that lethally deaminate the intermediate product of reverse transcription reaction–single-stranded cDNA . Mouse mammary tumor virus ( MMTV ) , although it does not encode an APOBEC3-neutralizing gene product and packages APOBEC3 proteins into the cores of virions , evades accumulation of the APOBEC3-mediated G-to-A mutations . Here , we show that a point mutation in the DNA polymerase domain of MMTV reverse transcriptase ( F120L ) , which reduced the rate of DNA synthesis , increased APOBEC3-mediated mutation levels and , in turn , sensitivity to inhibition by APOBEC3 proteins . A similar APOBEC3 sensitizing effect was detected for cell culture conditions that slow down the rate of reverse transcription reaction such as decreased dNTP levels within target cells or the presence of sub-optimal , non-lethal concentrations of reverse transcriptase inhibitors . Thus , our results support the concept that MMTV has evolved reverse transcriptase to catalyze virus DNA synthesis with a rate that alleviates APOBEC3-mediated hypermutation of the virus replication intermediates . A similar mechanism may be used by other reverse transcriptase-containing viral pathogens to escape APOBEC3 innate immunity factors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"nucleic",
"acid",
"synthesis",
"microbial",
"mutation",
"pathology",
"and",
"laboratory",
"medicine",
"nucleases",
"enzymes",
"pathogens",
"microbiology",
"dna-binding",
"proteins",
"viral",
"structure",
"enzymology",
"plasmid",
"construction",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"mutation",
"rna",
"viruses",
"dna",
"construction",
"dna",
"rna",
"synthesis",
"molecular",
"biology",
"techniques",
"dna",
"synthesis",
"chemical",
"synthesis",
"research",
"and",
"analysis",
"methods",
"proteins",
"medical",
"microbiology",
"hiv",
"ribonucleases",
"microbial",
"pathogens",
"hiv-1",
"molecular",
"biology",
"virions",
"biosynthetic",
"techniques",
"biochemistry",
"rna",
"hydrolases",
"nucleic",
"acids",
"virology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"lentivirus",
"organisms"
] | 2019 | A high rate of polymerization during synthesis of mouse mammary tumor virus DNA alleviates hypermutation by APOBEC3 proteins |
The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation . When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation . This approach , however , leads to a model with an infinite-dimensional state space and non-standard boundary conditions . Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator , the other is based on a cascade of two linear filters and a nonlinearity , which are determined from the Fokker-Planck equation and semi-analytically approximated . We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population . Particularly the cascade-based models are overall most accurate and robust , especially in the sensitive region of rapidly changing input . For the mean-driven regime , when input fluctuations are not too strong and fast , however , the best performing model is based on the spectral decomposition . The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback . The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants . Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software . The derived spike rate descriptions retain a direct link to the properties of single neurons , allow for convenient mathematical analyses of network states , and are well suited for application in neural mass/mean-field based brain network models .
There is prominent evidence that information in the brain , about a particular stimulus for example , is contained in the collective neuronal spiking activity averaged over populations of neurons with similar properties ( population spike rate code ) [1 , 2] . Although these populations can comprise a large number of neurons [3] , they often exhibit low-dimensional collective spiking dynamics [4] that can be measured using neural mass signals such as the local field potential or electroencephalography . The behavior of cortical networks at that level is often studied computationally by employing simulations of multiple ( realistically large or subsampled ) populations of synaptically coupled individual spiking model neurons . A popular choice of single cell description for this purpose are two-variable integrate-and-fire models [5 , 6] which describe the evolution of the fast ( somatic ) membrane voltage and an adaptation variable that represents a slowly-decaying potassium current . These models are computationally efficient and can be successfully calibrated using electrophysiological recordings of real cortical neurons and standard stimulation protocols [5 , 7–10] to accurately reproduce their subthreshold and spiking activity . The choice of such ( simple ) neuron models , however , does not imply reasonable ( short enough ) simulation durations for a recurrent network , especially when large numbers of neurons and synaptic connections between them are considered . A fast and mathematically tractable alternative to simulations of large networks are population activity models in terms of low-dimensional ordinary differential equations ( i . e . , which consist of only a few variables ) that typically describe the evolution of the spike rate . These reduced models can be rapidly solved and allow for convenient analyses of the dynamical network states using well-known methods that are simple to implement . A popular example are the Wilson-Cowan equations [11] , which were also extended to account for ( slow ) neuronal adaptation [12] and short-term synaptic depression [13] . Models of this type have been successfully applied to qualitatively characterize the possible dynamical states of coupled neuronal populations using phase space analyses [11–13] , yet a direct link to more biophysically described networks of ( calibrated ) spiking neurons in terms of model parameters is missing . Recently , derived population activity models have been proposed that bridge the gap between single neuron properties and mesoscopic network dynamics . These models are described by integral equations [14 , 15] or partial differential equations [16 , 17] Here we derive simple models in terms of low-dimensional ordinary differential equations ( ODEs ) for the spike rate dynamics of sparsely coupled adaptive nonlinear integrate-and-fire neurons that are exposed to noisy synaptic input . The derivations are based on a Fokker-Planck equation that describes the neuronal population activity in the mean-field limit of large networks . We develop reduced models using recent methodological advances on two different approaches: the first is based on a spectral decomposition of the Fokker-Planck operator under two different slowness assumptions [18–20] . In the second approach we consider a cascade of linear temporal filters and a nonlinear function which are determined from the Fokker-Planck equation and semi-analytically approximated , building upon [21] . Both approaches are extended for an adaptation current , a nonlinear spike generating current and recurrent coupling with distributed synaptic delays . We evaluate the developed low-dimensional spike rate models quantitatively in terms of reproduction accuracy in a systematic manner over a wide range of biologically plausible parameter values . In addition , we provide numerical implementations for the different reduction methods as well as the Fokker-Planck equation under a free license as open source project . For the derived models in this contribution we use the adaptive exponential integrate-and-fire ( aEIF ) model [5] to describe individual neurons , which is similar to the model proposed by Izhikevich [6] but includes biophysically meaningful parameters and a refined description of spike initiation . However , the presented derivations are equally applicable when using the Izhikevich model instead ( requiring only a small number of simple substitutions in the code ) . Through their parameters the derived models retain a direct , quantitative link to the underlying spiking model neurons , and they are described in a well-established , convenient form ( ODEs ) that can be rapidly solved and analyzed . Therefore , these models are well suited ( i ) for mathematical analyses of dynamical states at the population level , e . g . , linear stability analyses of attractors , and ( ii ) for application in multi-population brain network models . Apart from a specific network setting , the derived models are also appropriate as a spike rate description of individual neurons under noisy input conditions . The structure of this article contains mildly redundant model specifications allowing the readers who are not interested in the methodological foundation to directly read the self-contained Sect . Results .
The quantity of our interest is the population-averaged number of spikes emitted by a large homogeneous network of N sparsely coupled aEIF model neurons per small time interval , i . e . , the spike rate rN ( t ) . The state of neuron i at time t is described by the membrane voltage Vi ( t ) and adaptation current wi ( t ) , which evolve piecewise continuously in response to overall synaptic current Isyn , i = Iext , i ( t ) + Irec , i ( t ) . This input current consists of fluctuating network-external drive Iext , i = C[μext ( t ) + σext ( t ) ξext , i ( t ) ] with membrane capacitance C , time-varying moments μext , σ ext 2 and unit Gaussian white noise process ξext , i as well as recurrent input Irec , i . The latter causes delayed postsynaptic potentials ( i . e . , deflections of Vi ) of small amplitude J triggered by the spikes of K presynaptic neurons ( see Sect . Methods for details ) . Here we present two approaches of how the spike rate dynamics of the large , stochastic delay-differential equation system for the 2N states ( Vi , wi ) can be described by simple models in terms of low-dimensional ODEs . Both approaches ( i ) take into account adaptation current dynamics that are sufficiently slow , allowing to replace the individual adaptation current wi by its population-average 〈w〉 , governed by d ⟨ w ⟩ d t = a ( ⟨ V ⟩ ∞ - E w ) - ⟨ w ⟩ τ w + b r ( t ) , ( 1 ) where a , Ew , b , τw are the adaptation current model parameters ( subthreshold conductance , reversal potential , spike-triggered increment , time constant , respectively ) , 〈V〉∞ is the steady-state membrane voltage averaged across the population ( which can vary over time , see below ) , and r is the spike rate of the respective low-dimensional model . Furthermore , both approaches ( ii ) are based on the observation that the collective dynamics of a large , sparsely coupled ( and noise driven ) network of integrate-and-fire type neurons can be well described by a Fokker-Planck equation . In this intermediate Fokker-Planck ( FP ) model the overall synaptic input is approximated by a mean part with additive white Gaussian fluctuations , Isyn , i/C ≈ μsyn ( t , rd ) + σsyn ( t , rd ) ξi ( t ) , that are uncorrelated between neurons . The moments of the overall synaptic input , μ syn = μ ext ( t ) + J K r d ( t ) , σ syn 2 = σ ext 2 ( t ) + J 2 K r d ( t ) , ( 2 ) depend on time via the moments of the external input and , due to recurrent coupling , on the delayed spike rate rd . The latter is governed by d r d d t = r - r d τ d , ( 3 ) which corresponds to individual propagation delays drawn from an exponentially distributed random variable with mean τd . The FP model involves solving a partial differential equation ( PDE ) to obtain the time-varying membrane voltage distribution p ( V , t ) and the spike rate r ( t ) . The first reduction approach is based on the spectral decomposition of the Fokker-Planck operator L and leads to the following two low-dimensional models: the “basic” model variant ( spec1 ) is given by a complex-valued differential equation describing the spike rate evolution in its real part , d r ˜ d t = λ 1 ( r ˜ - r ∞ ) , r ( t ) = Re { r ˜ } , ( 4 ) where λ1 ( μtot , σtot ) is the dominant eigenvalue of L and r∞ ( μtot , σtot ) is the steady-state spike rate . Its parameters λ1 , r∞ , and 〈V〉∞ ( cf . Eq ( 1 ) ) depend on the total input moments given by μtot ( t ) = μsyn − 〈w〉/C and σ tot 2 ( t ) = σ syn 2 which closes the model ( Eqs ( 1 ) – ( 4 ) ) . The other , “advanced” spectral model variant ( spec2 ) is given by a real-valued second order differential equation for the spike rate , β 2 r ¨ + β 1 r ˙ + β 0 r = r ∞ - r - β c , ( 5 ) where the dots denote time derivatives . Its parameters β2 , β1 , β0 , βc , r∞ and 〈V〉∞ depend on the total input moments ( μtot , σ tot 2 ) as follows: the latter two parameters explicitly as in the basic model above , the former four indirectly via the first two dominant eigenvalues λ1 , λ2 and via additional quantities obtained from the ( stationary and the first two nonstationary ) eigenfunctions of L and its adjoint L * . Furthermore , the parameter βc depends explicitly on the population-averaged adaptation current 〈w〉 , the delayed spike rate rd , and on the first and second order time derivatives of the external moments μext and σ ext 2 . The second approach is based on a Linear-Nonlinear ( LN ) cascade , in which the population spike rate is generated by applying to the time-varying mean and standard deviation of the overall synaptic input , μsyn and σsyn , separately a linear temporal filter , followed by a common nonlinear function . These three components–two linear filters and a nonlinearity–are extracted from the Fokker-Planck equation . Approximating the linear filters using exponentials and damped oscillating functions yields two model variants: In the basic “exponential” ( LNexp ) model the filtered mean μf and standard deviation σf of the overall synaptic input are given by d μ f d t = μ syn - μ f τ μ , d σ f d t = σ syn - σ f τ σ , ( 6 ) where the time constants τμ ( μeff , σeff ) , τσ ( μeff , σeff ) depend on the effective ( filtered ) input mean μeff ( t ) = μf − 〈w〉/C and standard deviation σeff ( t ) = σf . The “damped oscillator” ( LNdos ) model variant , on the other hand , describes the filtered input moments by μ ¨ f + 2 τ μ ˙ f + ( 2 τ 2 + ω 2 ) μ f = 1 + τ 2 ω 2 τ ( μ syn τ + μ ˙ syn ) , ( 7 ) d σ f d t = σ syn - σ f τ σ , ( 8 ) where the time constants τ ( μtot , σtot ) , τσ ( μtot , σtot ) and the angular frequency ω ( μtot , σtot ) depend on the total input moments defined above . In both LN model variants the spike rate is obtained by the nonlinear transformation of the effective input moments through the steady-state spike rate , r ( t ) = r ∞ ( μ eff , σ eff ) , ( 9 ) and the steady-state mean membrane voltage 〈V〉∞ ( cf . Eq ( 1 ) ) is also evaluated at ( μeff , σeff ) . These four models ( spec1 , spec2 , LNexp , LNdos ) from both reduction approaches involve a number of parameters that depend on the strengths of synaptic input and adaptation current only via the total or effective input moments . We refer to these parameters as quantities below to distinguish them from fixed ( independent ) parameters . The computational complexity when numerically solving the models forward in time ( for different parametrizations ) can be greatly reduced by precomputing those quantities for a range of values for the total/effective input moments and using look-up tables during time integration . Changing any parameter value of the external input , the recurrent coupling or the adaptation current does not require renewed precomputations , enabling rapid explorations of parameter space and efficient ( linear ) stability analyses of network states . The full specification of the “ground truth” system ( network of aEIF neurons ) , the derivations of the intermediate description ( FP model ) and the low-dimensional spike rate models complemented by concrete numerical implementations are provided in Sect . Methods ( that is complemented by the supporting material S1 Text ) . In Fig 1 we visualize the outputs of the different models using an example excitatory aEIF network exposed to external input with varying mean μext ( t ) and standard deviation σext ( t ) . Here , and in the subsequent two sections , we assess the accuracy of the four low-dimensional models to reproduce the spike rate dynamics of the underlying aEIF population . The intermediate FP model is included for reference . The derived models generate population activity in response to overall synaptic input moments μsyn and σ syn 2 . These depend on time via the external moments μext ( t ) and σ ext 2 ( t ) , and the delayed spike rate rd ( t ) . Therefore , it is instrumental to first consider an uncoupled population and suitable variations of external input moments that effectively mimic a range of biologically plausible presynaptic spike rate dynamics . This allows us to systematically compare the reproduction performance of the different models over a manageable parameter space ( without K , J , τd ) , yet it provides useful information on the accuracy for recurrent networks . For many network settings the dominant effect of synaptic coupling is on the mean input ( cf . Eq ( 2 ) ) . Therefore , we consider first in detail time-varying mean but constant variance of the input . Specifically , to account for a wide range of oscillation frequencies for presynaptic spike rates , μext is described by an Ornstein-Uhlenbeck ( OU ) process μ ˙ ext = μ ¯ - μ ext τ ou μ + 2 τ ou μ ϑ μ ξ ( t ) , ( 10 ) where τ ou μ denotes the correlation time , μ ¯ and ϑμ are the mean and standard deviation of the stationary normal distribution , i . e . , lim t → ∞ μ ext ( t ) ∼ N ( μ ¯ , ϑ μ 2 ) , and ξ is a unit Gaussian white noise process . Sample time series generated from the OU process are filtered using a Gaussian kernel with a small standard deviation σt to obtain sufficiently differentiable time series μ ˜ ext ( due to the requirements of the spec2 model and the LNdos model ) . The filtered realization μ ˜ ext ( t ) is then used for all models to allow for a quantitative comparison of the different spike rate responses to the same input . The value of σt we use in this study effectively removes very large oscillation frequencies which are rarely observed , while lower frequencies [22] are passed . The parameter space we explore covers large and small correlation times τ ou μ , strong and weak input mean μ ¯ and standard deviation σext , and for each of these combinations we consider an interval from small to large variation magnitudes ϑμ . The values of τ ou μ and ϑμ determine how rapid and intense μext ( t ) fluctuates . We apply two performance measures , as in [21] . One is given by Pearson’s correlation coefficient , ρ ( r N , r ) ≔ ∑ k = 1 M ( r N ( t k ) - r ¯ N ) ( r ( t k ) - r ¯ ) ∑ k = 1 M ( r N ( t k ) - r ¯ N ) 2 ∑ k = 1 M ( r ( t k ) - r ¯ ) 2 , ( 11 ) between the ( discretely given ) spike rates of the aEIF population and each derived model with time averages r ¯ N = 1 / M ∑ k = 1 M r N ( t k ) and r ¯ = 1 / M ∑ k = 1 M r ( t k ) over a time interval of length tM − t1 . For comparison , we also include the correlation coefficient between the aEIF population spike rate and the time-varying mean input , ρ ( rN , μext ) . In addition , to assess absolute differences we calculate the root mean square ( RMS ) distance , dRMS ( rN , r ) ≔1M∑k=1M ( rN ( tk ) −r ( tk ) ) 2 , ( 12 ) where M denotes the number of elements of the respective time series ( rN , r ) . We find that three of the four low-dimensional spike rate models ( spec2 , LNexp , LNdos ) very well reproduce the spike rate rN of the aEIF neurons: for the LNexp model ρ > 0 . 95 and for the spec2 and LNdos models ρ ≳ 0 . 8 ( each ) over the explored parameter space , see Fig 2 . Only the basic spectral model ( spec1 ) is substantially less accurate . Among the best models , the simplest ( LNexp ) overall outperforms spec2 and LNdos , in particular for fast and strong mean input variations . However , in the strongly mean-driven regime the best performing model is spec2 . We observe that the performance of any of the spike rate models decreases ( with model-specific slope ) with ( i ) increasing variation strength ϑμ larger than a certain ( small ) value , and with ( ii ) smaller τ ou μ , i . e . , faster changes of μext . For small values of ϑμ fluctuations of rN , which are caused by the finite aEIF population size N and do not depend on the fluctuations of μext , deteriorate the performance measured by ρ ( see also [21] , p . 13 right ) . This explains why ρ does not increase as ϑμ decreases ( towards zero ) for any of the models . Naturally , the FP model is the by far most accurate spike rate description in terms of both measures , correlation coefficient ρ and RMS distance . This is not surprising because the four low-dimensional models are derived from that ( infinite-dimensional ) representation . Thus , the performance of the FP system defines an upper bound on the correlation coefficient ρ and a lower bound on the RMS distance for the low-dimensional models . In detail: for moderately fast changing mean input ( large τ ou μ ) the three models spec2 , LNexp and LNdos exhibit excellent reproduction performance with ρ > 0 . 95 , and spec1 shows correlation coefficients of at least ρ = 0 . 9 ( Fig 2A ) , which is substantially better than ρ ( rN , μext ) . The small differences between the three top models can be better assessed from the RMS distance measure . For large input variance σ ext 2 the two LN models perform best ( cf . Fig 2A , top , and for an example , 2C ) . For weak input variance and large mean ( small σext , large μ ¯ ) the spec2 model outperforms the LN models , unless the variation magnitude ϑμ is very large . For small mean μ ¯ , where transient activity is interleaved with periods of quiescence , the LNexp model performs best , except for weak variations ϑμ , where LNdos is slightly better ( see Fig 2A , bottom ) . Stronger differences in performance emerge when considering faster changes of the mean input μext ( t ) ( i . e . , for small τ ou μ ) , see Fig 2B , and for examples , Fig 2C . The spec1 model again performs worst with ρ values even below the input/output correlation baseline ρ ( rN , μext ) for large mean input μ ¯ ( cf . Fig 2B , left ) . The spec1 spike rate typically decays too slowly ( cf . Fig 2C ) . The three better performing models differ as follows: for large input variance and mean ( large σext and μ ¯ ) , where the spike rate response to the input is rather fast ( cf . increased ρ ( rN , μext ) ) , the performance of all three models in terms of ρ is very high , but the RMS distance measure indicates that LNexp is the most accurate model ( cf . Fig 2B , top ) . For weak mean input LNexp is once again the top model while LNdos and , especially noticeable , spec2 show a performance decline ( see example in Fig 2C ) . For weak input variance ( Fig 2A , bottom ) , where significant ( oscillatory ) excursions of the spike rates in response to changes in the mean input can be observed ( see also Fig 1 ) , we obtain the following benchmark contrast: for large mean drive μ ¯ the spec2 model performs best , except for large variation amplitudes ϑμ , at which LNexp is more accurate . Smaller mean input on the other hand corresponds to the most sensitive regime where periods of quiescence alternate with rapidly increasing and decaying spike rates . The LNexp model shows the most robust and accurate spike rate reproduction in this setting , while LNdos and spec2 each exhibit decreased correlation and larger RMS distances–spec2 even for moderate input variation intensities ϑμ . The slowness approximation underlying the spec2 model likely induces an error due to the fast external input changes in comparison with the rather slow intrinsic time scale by the dominant eigenvalue , τ ou μ = 5 ms vs . 1/|Re{λ1}| ≈ 15 ms ( cf . visualization of the spectrum in Sect . Spectral models ) . Note that for these weak inputs the distribution of the spike rate is rather asymmetric ( cf . Fig 2B ) . Interestingly the LNdos model performs worse than LNexp for large mean input variations ( i . e . , large ϑμ ) in general , and only slightly better for small input variance and mean input variations that are not too large and fast . We would like to note that decreasing the Gaussian filter width σt to smaller values , e . g . , fractions of a millisecond , can lead to a strong performance decline for the spec2 model because of its explicit dependence on first and second order time derivatives of the mean input . Furthermore , we show how the adaptation parameters affect the reproduction performance of the different models in Fig 3 . The adaptation time constant τw and spike-triggered adaptation increment b are varied simultaneously ( keeping their product constant ) such that the average spike rate and adaptation current , and thus the spiking regime , remain comparable for all parametrizations . As expected , the accuracy of the derived models decreases for faster adaptation current dynamics , due to the adiabatic approximation that relies on sufficiently slow adaptation ( cf . Sect . Methods ) . Interestingly however , the performance of all reduced models ( except spec1 ) declines only slightly as the adaptation time constant decreases to the value of the membrane time constant ( which means the assumption of separated time scales underlying the adiabatic approximation is clearly violated ) . This kind of robustness is particularly pronounced for input with large baseline mean μ ¯ and small noise amplitude σext , cf . Fig 3B . For perfectly balanced excitatory and inhibitory synaptic coupling the contribution of presynaptic activity to the mean input μsyn is zero by definition , but the input variance σ syn 2 is always positively ( linearly ) affected by a presynaptic spike rate–even for a negative synaptic efficacy J ( cf . Eq ( 2 ) ) . To assess the performance of the derived models in this scenario , but within the reference setting of an uncoupled population , we consider constant external mean drive μext and let the variance σ ext 2 ( t ) evolve according to a filtered OU process ( such as that used for the mean input μext in the previous section ) with parameters σ 2 ¯ and ϑσ2 of the stationary normal distribution N ( σ 2 ¯ , ϑ σ 2 2 ) , correlation time τ ou σ 2 and Gaussian filter standard deviation σt as before . The results of two input parametrizations are shown in Fig 4 . For large input mean μext and rapidly varying variance σ ext 2 ( t ) the spike rate response of the aEIF population is very well reproduced by the FP model and , to a large extent , by the spec2 model ( cf . Fig 4A ) . This may be attributed to the fact that the latter model depends on the first two time derivatives of the input variance σ ext 2 . The LN models cannot well reproduce the rapid spike rate excursions in this setting , and the spec1 model performs worst , exhibiting time-lagged spike rate dynamics compared to rN ( t ) which leads to a very small value of correlation coefficient ρ ( below the input/output correlation baseline ρ ( r N , σ ext 2 ) ) . For smaller mean input μext and moderately fast varying variance σ ext 2 ( t ) ( larger correlation time τ ou σ 2 ) the fluctuating aEIF population spike rate is again nicely reproduced by the FP model while the rate response of the spec2 model exhibits over-sensitive behavior to changes in the input variance , as indicated by the large RMS distance ( see Fig 4B ) . This effect is even stronger for faster variations , i . e . , smaller τ ou σ 2 ( cf . supplementary visualization S1 Fig ) . The LN models perform better in this setting , and the spec1 model ( again ) performs worst in terms of correlation coefficient ρ due to its time-lagged spike rate response . It should be noted that the lowest possible value of the input standard deviation , i . e . , σext ( plus a nonnegative number in case of recurrent input ) cannot be chosen completely freely but must be large enough ( ≳ 0 . 5 mV / ms ) for our parametrization . This is due to theoretical reasons ( Fokker-Planck formalism ) and practical reasons ( numerics for Fokker-Planck solution and for calculation of the derived quantities , such as r∞ ) . To demonstrate the applicability of the low-dimensional models for network analyses we consider a recurrently coupled population of aEIF neurons that produces self-sustained network oscillations by the interplay of strong excitatory feedback and spike-triggered adaptation or , alternatively , by delayed recurrent synaptic inhibition [16 , 23] . The former oscillation type is quite sensitive to changes in input , adaptation and especially coupling parameters for the current-based type of synaptic coupling considered here and due to lack of ( synaptic ) inhibition and refractoriness . For example , a small increase in coupling strength can lead to a dramatic ( unphysiologic ) increase in oscillation amplitude because of strong recurrent excitation . Hence we consider a difficult setting here to evaluate the reduced spike rate models–in particular , when the network operates close to a bifurcation . In Fig 5A we present two example parametrizations from a region ( in parameter space ) that is characterized by stable oscillations . This means the network exhibits oscillatory spike rate dynamics for constant external input moments μext and σ ext 2 . The derived models reproduce the limit cycle behavior of the aEIF network surprisingly well , except for small frequency and amplitude deviations ( FP , spec2 , LNdos , LNexp ) and larger frequency mismatch ( spec1 ) , see Fig 5A , top . For weaker input moments and increased spike-triggered adaptation strength the network is closer to a Hopf bifurcation [16 , 23] . It is , therefore , not surprising that the differences in oscillation period and amplitude are more prominent ( cf . Fig 5A , bottom ) . The bifurcation point of the LNexp model is slightly shifted , shown by the slowly damped oscillatory convergence to a fixed point . This suggests that the bifurcation parameter value of each of the derived models is not far from the true critical parameter value of the aEIF network but can quantitatively differ ( slightly ) in a model-dependent way . The second type of oscillation is generated by delayed synaptic inhibition [22] and does not depend on the ( neuronal ) inhibition that is provided by an adaptation current . To demonstrate this independence the adaptation current was disabled ( by setting the parameters a = b = 0 ) for the two respective examples that are shown in Fig 5B . Similarly as for the previous oscillation type , the low-dimensional models ( except spec1 ) reproduce the spike rate limit cycle of the aEIF network surprisingly well , in particular for weak external input ( see Fig 5B , top ) . For larger external input and stronger inhibition with shorter delay the network operates close to a Hopf bifurcation , leading to larger differences in oscillation amplitude and frequency in a model-dependent way ( Fig 5B , bottom ) . Note that the intermediate ( Fokker-Planck ) model very well reproduces the inhibition-based type of oscillation which demonstrates the applicability of the underlying mean-field approximation . We would also like to note that enabling the adaptation current dynamics ( only ) leads to decreased average spike rates but does not affect the reproduction accuracy . We would like to emphasize that the previous comprehensive evaluations for an uncoupled population provide a deeper insight on the reproduction performance–also for a recurrent network–than the four examples shown here , as explained in the Sect . Performance for variations of the mean input . For example , the ( improved ) reproduction performance for increased input variance in the uncoupled setting ( cf . Fig 2 ) informs about the reproduction performance for networks of excitatory and inhibitory neurons that are roughly balanced , i . e . , where the overall input mean is rather small compared to the input standard deviation . We have developed efficient implementations of the derived models using the Python programming language and by employing the library Numba for low-level machine acceleration [24] . These include: ( i ) the numerical integration of the Fokker-Planck model using an accurate finite volume scheme with implicit time discretization ( cf . Sect . Methods ) , ( ii ) the parallelized precalculation of the quantities required by the low-dimensional spike rate models and ( iii ) the time integration of the latter models , as well as example scripts demonstrating ( i ) – ( iii ) . The code is available as open source software under a free license at GitHub: https://github . com/neuromethods/fokker-planck-based-spike-rate-models With regards to computational cost , summarizing the results of several aEIF network parametrizations , the duration to generate population activity time series for the low-dimensional spike rate models is usually several orders of magnitude smaller compared to numerical simulation of the original aEIF network and a few orders of magnitude smaller in comparison to the numerical solution of the FP model . For example , considering a population of 50 , 000 coupled neurons with 2% connection probability , a single simulation run of 5 s and the same integration time step across the models , the computation times amounted to 1 . 1–3 . 6 s for the low-dimensional models ( with order–fast to slow–LNexp , spec1 , LNdos , spec2 ) , about 100 s for the FP model and roughly 1500 s for the aEIF network simulation on a dual-core laptop computer . The time difference to the network simulation substantially increases with the numbers of neurons and connections , and with spiking activity within the network due to the extensive propagation of synaptic events . Note that the speedup becomes even more pronounced with increasing number of populations , where the runtimes of the FP model and the aEIF network simulation scale linearly and the low-dimensional models show a sublinear runtime increase due to vectorization of the state variables representing the different populations . The derived low-dimensional ( ODE ) spike rate models are very efficient to integrate given that the required input-dependent parameters are available as precalulated look-up quantities . For the grids used in this contribution , the precomputation time was 40 min . for the cascade ( LNexp , LNdos ) models and 120 min . for the spectral ( spec1 , spec2 ) models , both on a hexa-core desktop computer . The longer calculation time for the spectral models was due to the finer internal grid for the mean input ( see S1 Text ) . Note that while the time integration of the spec2 model is on the same order as for the other low-dimensional models its implementation complexity is larger because of the many quantities it depends on , cf . Eqs ( 63 ) – ( 66 ) .
In addition to the work we build upon [18–21] ( cf . Sect . Methods ) there are a few other approaches to derive spike rate models from populations of spiking neurons . Some methods also result in an ODE system , taking into account ( slow ) neuronal adaptation [17 , 26 , 36–38] or disregarding it [39] . The settings differ from the work presented here in that ( i ) the intrinsic neuronal dynamics are adiabatically neglected [17 , 26 , 36 , 37] , ( ii ) only uncoupled populations [38] or all-to-all connected networks [17 , 36 , 39] are assumed in contrast to sparse connectivity , and ( iii ) ( fixed ) heterogeneous instead of fluctuating input is considered [39] . Notably , these previous methods yield rather qualitative agreements with the underlying spiking neuron population activity except for [39] where an excellent quantitative reproduction for ( non-adaptive ) quadratic integrate-and-fire oscillators with quenched input randomness is reported . Other approaches yield mesoscopic representations of population activity in terms of model classes that are substantially less efficient to simulate and more complicated to analyze than low-dimensional ODEs [14–17 , 40–42] . The spike rate dynamics in these models has been described ( i ) by a rather complex ODE system that depends on a stochastic jump process derived for integrate-and-fire neurons without adaptation [40] , ( ii ) by PDEs for recurrently connected aEIF [16] or Izhikevich [17] neurons , ( iii ) by an integro-PDE with displacement for non-adaptive neurons [42] or ( iv ) by integral equations that represent the ( mean ) activity of coupled phenomenological spiking neurons without [41] and with adaptation [14 , 15] . Furthermore , the stationary condition of a noise-driven population of adaptive EIF neurons [32 , 43 , 44] and the first order spike rate response to weak input modulations [43 , 44] have been analyzed using the Fokker-Planck equation . Ref . [32] also considered a refined approximation of the ( purely spike-triggered ) adaptation current including higher order moments . It may be interesting for future studies to explore ways to extend the presented methods and relax some of the underlying assumptions , in particular , considering ( i ) the diffusion approximation ( via shot noise input , e . g . , [45 , 46] ) , ( ii ) the Poisson assumption ( e . g . , using the concept from [47] in combination with results from [48] ) and ( iii ) ( noise ) correlations ( see , e . g . , [49] ) .
We consider a large ( homogeneous ) population of N synaptically coupled aEIF model neurons [5] . Specifically , for each neuron ( i = 1 , … , N ) , the dynamics of the membrane voltage Vi is described by C d V i d t = I L ( V i ) + I exp ( V i ) - w i + I syn , i ( t ) , ( 14 ) where the capacitive current through the membrane with capacitance C equals the sum of three ionic currents and the synaptic current Isyn , i . The ionic currents consist of a linear leak current IL ( Vi ) = −gL ( Vi − EL ) with conductance gL and reversal potential EL , a nonlinear term Iexp ( Vi ) = gL ΔT exp ( ( Vi − VT ) /ΔT ) that approximates the rapidly increasing Na+ current at spike initiation with threshold slope factor ΔT and effective threshold voltage VT , and the adaptation current wi which reflects a slowly deactivating K+ current . The adaptation current evolves according to τ w d w i d t = a ( V i - E w ) - w i , ( 15 ) with adaptation time constant τw . Its strength depends on the subthreshold membrane voltage via conductance a . Ew denotes its reversal potential . When Vi increases beyond VT , it diverges to infinity in finite time due to the exponentially increasing current Iexp ( Vi ) , which defines a spike . In practice , however , the spike is said to occur when Vi reaches a given value Vs—the spike voltage . The downswing of the spike is not explicitly modeled; instead , when Vi ≥ Vs , the membrane voltage Vi is instantaneously reset to a lower value Vr . At the same time , the adaptation current wi is incremented by a value of parameter b , which implements suprathreshold ( spike-dependent ) activation of the adaptation current . Immediately after the reset , Vi and wi are clamped ( i . e . , remain constant ) for a short refractory period Tref , and subsequently governed again by Eqs ( 14 ) and ( 15 ) . At the end of the Methods section we describe how ( optionally ) a spike shape can be included in the aEIF model , together with the associated small changes for the models derived from it . To complete the network model the synaptic current in Eq ( 14 ) needs to be specified: for each cell it is given by the sum of recurrent and external input , Isyn , i = Irec , i ( t ) + Iext , i ( t ) . Recurrent synaptic input is received from K other neurons of the network , that are connected in a sparse ( K ≪ N ) and uniformly random way , and is modeled by I rec , i = C ∑ j J i j ∑ t j δ ( t − t j − d i j ) , ( 16 ) where δ denotes the Dirac delta function . Every spike by one of the K presynaptic neurons with indices j and spike times tj causes a postsynaptic membrane voltage jump of size Jij . The coupling strength is positive ( negative ) for excitation ( inhibition ) and of small magnitude . Here it is chosen to be constant , i . e . , Jij = J . Each of these membrane voltage deflections occur after a time delay dij that takes into account ( axonal and dendritic ) spike propagation times and is sampled ( independently ) from a probability distribution pd . In this work we use exponentially distributed delays , i . e . , pd ( τ ) = exp ( −τ/τd ) /τd ( for τ ≥ 0 ) with mean delay τd . The second type of synaptic input is a fluctuating current generated from network-external neurons , I ext , i = C [ μ ext ( t ) + σ ext ( t ) ξ ext , i ( t ) ] , ( 17 ) with time-varying moments μext and σ ext 2 , and unit Gaussian white noise process ξext , i . The latter is uncorrelated with that of other neurons j ≠ i , i . e . , 〈ξext , i ( t ) ξext , j ( t + τ ) 〉 = δ ( τ ) δij , where 〈·〉 denotes expectation ( w . r . t . the joint ensemble of noise realizations at times t and t + τ ) and δij is the Kronecker delta . This external current , for example , accurately approximates the input generated from a large number of independent Poisson neurons that produce instantaneous postsynaptic potentials of small magnitude , cf . [48] . The spike rate rN of the network is defined as the population-averaged number of emitted spikes per time interval [t , t + ΔT] , r N ( t ) = 1 N ∑ i = 1 N 1 Δ T ∫ t t + Δ T ∑ t i δ ( s − t i ) d s , ( 18 ) where the interval size ΔT is practically chosen small enough to capture the dynamical structure and large enough to yield a comparably smooth time evolution for a finite network , i . e . , N < ∞ . We chose values for the neuron model parameters to describe cortical pyramidal cells , which exhibit “regular spiking” behavior and spike frequency adaptation [7 , 50 , 51] . For the complete parameter specification see Table 1 . All network simulations were performed using the Python software BRIAN2 [52 , 53] with C++ code generation enabled for efficiency . The aEIF model Eqs ( 14 ) and ( 15 ) were discretized using the Euler-Maruyama method with equidistant time step Δt and initialized with wi ( 0 ) = 0 and Vi ( 0 ) that is ( independently ) sampled from a Gaussian initial distribution p0 ( V ) with mean Vr − δV and standard deviation δV/2 where δV = VT − Vr . Note that the models derived in the following Sects . do not depend on this particular initial density shape but allow for an arbitrary ( density ) function p0 . In the following sections we present two approaches of how simple spike rate models can be derived from the Fokker-Planck mean-field model described in the previous section , cf . Eqs ( 20 ) , ( 21 ) and ( 23 ) – ( 32 ) . The derived models are described by low-dimensional ordinary differential equations ( ODEs ) which depend on a number of quantities defined in the plane of ( generic ) input mean and standard deviation ( μ , σ ) . To explain this concept more clearly we consider , as an example , the steady-state spike rate , which is a quantity required by all reduced models . The steady-state spike rate as a function of μ and σ , r ∞ ( μ , σ ) ≔ lim t → ∞ r ( t ; μ tot = μ , σ tot = σ ) , ( 37 ) denotes the stationary value of Eq ( 29 ) under replacement of the ( time-varying ) total moments μtot and σ tot 2 in the probability flux qp , Eq ( 25 ) , by ( constants ) μ and σ2 , respectively . Thus the steady-state spike rate r∞ effectively corresponds to that of an uncoupled EIF population whose membrane voltage is governed by dVi/dt = [IL ( Vi ) + Iexp ( Vi ) ]/C + μ + σξi ( t ) plus reset condition , i . e . , adaptation and synaptic current dynamics are detached . For a visualization of r∞ ( μ , σ ) see Fig 6 . When simulating the reduced models these quantities need to be evaluated for each discrete time point t at a certain value of ( μ , σ ) which depends on the overall synaptic moments μsyn ( t ) , σ syn 2 ( t ) and on the mean adaptation current 〈w〉 ( t ) in a model-specific way ( as described in the following Sects . ) . An example trajectory of r∞ in the ( μ , σ ) space for a network showing stable spike rate oscillations is shown in Fig 5 . Importantly , these quantities depend on the parameters of synaptic input ( J , K , τd , μext , σext ) and adaptation current ( a , b , τw , Ew ) only through their arguments ( μ , σ ) . Therefore , for given parameter values of the EIF model ( C , gL , EL , ΔT , VT , Vr , Tref ) we precalculate those quantities on a ( reasonably large and sufficiently dense ) grid of μ and σ values , and access them during time integration by interpolating the quantity values stored in a table . This greatly reduces the computational complexity and enables rapid numerical simulations . The derived low-dimensional models describe the spike rate dynamics and generally do not express the evolution of the entire membrane voltage distribution . Therefore , the mean adaptation dynamics , which depends on the density p ( V , t ) ( via 〈V〉 , cf . Eq ( 23 ) ) is adjusted through approximating the mean membrane voltage 〈V〉 by the expectation over the steady-state distribution , ⟨ V ⟩ ∞ = ∫ - ∞ V s v p ∞ ( v ) d v ∫ - ∞ V s p ∞ ( v ) d v , ( 38 ) which is valid for sufficiently slow adaptation current dynamics [48 , 58] . The steady-state distribution is defined as p∞ ( V ) = limt → ∞ p ( V , t; μtot = μ , σtot = σ ) , representing the stationary membrane voltages of an uncoupled EIF population for generic input mean μ and standard deviation σ . The mean adaptation current in all reduced models is thus governed by d ⟨ w ⟩ d t = a ( ⟨ V ⟩ ∞ - E w ) - ⟨ w ⟩ τ w + b r ( t ) , ( 39 ) where the evaluation of quantity 〈V〉∞ in terms of particular values for μ and σ at a given time t is model-specific ( cf . following Sects . ) . Note again that the calculation of 〈V〉∞ slightly changes when considering an ( optional ) spike shape extension for the aEIF model , as described at the end of the Methods section . The Fokker-Planck model does not restrict the form of the delay distribution pd , except that the convolution with the spike rate r , Eq ( 20 ) , has to be well defined . Here , however , we aim at specifying the complete network dynamics in terms of a low-dimensional ODE system . Exploiting the exponential form of the delay distribution pd we obtain a simple ordinary differential equation for the delayed spike rate , d r d d t = r - r d τ d , ( 40 ) which is equivalent to the convolution rd = r * pd . Note that more generally any delay distribution from the exponential family allows to represent the delayed spike rate rd by an equivalent ODE instead of a convolution integral [68] . Identical delays , rd ( t ) = r ( t − d ) , are also possible but lead to delay differential equations . Naturally , in case of no delays , we simply have rd ( t ) = r ( t ) . To simulate the reduced models standard explicit time discretization schemes can be applied–directly to the first order equations of the LNexp model , and for the other models ( LNdos , spec1 , spec2 ) –to the respective equivalent ( real ) first order systems . We would like to note that when using the explicit Euler method to integrate any of the latter three low-dimensional models a sufficiently small integration time step Δt is required to prevent oscillatory artifacts . Although the explicit Euler method works well for the parameter values used in this contribution , we have additionally implemented the method of Heun , i . e . , the explicit trapezoidal rule , which is second order accurate . Linear-Nonlinear ( LN ) cascade models of neuronal activity are often applied in neuroscience , because they are simple and efficient , and the model components can be estimated using established experimental procedures [21 , 74 , 75] . Here we use the LN cascade as an ansatz to develop a low-dimensional model and we determine its components from the underlying Fokker-Planck model . This section builds upon [21] and extends that approach for recurrently coupled aEIF neurons; specifically , by taking into account an adaptation current and variations of the input variance . Furthermore , we consider an improved approximation of the derived linear filters and include an ( optional ) explicit description of the spike shape , cf . [23] ( ch 4 . 2 ) . The cascade models considered here produce spike rate output by applying to the time-varying mean μsyn and standard deviation σsyn of the ( overall ) synaptic input , cf . Eq ( 21 ) , separately a linear temporal filter , Dμ and Dσ , followed by a common nonlinear function F . That is , r ( t ) = F ( μ f , σ f , ⟨ w ⟩ ) , ( 70 ) μ f ( t ) = D μ * μ syn ( t ) , ( 71 ) σ f ( t ) = D σ * σ syn ( t ) , ( 72 ) where μf and σf denote the filtered mean and filtered standard deviation of the input , respectively . D μ * μ syn ( t ) = ∫ 0 ∞ D μ ( τ ) μ syn ( t - τ ) d τ is the convolution between Dμ and μsyn . The filters Dμ , Dσ are adaptive in the sense that they depend on the mean adaptation current 〈w〉 and on the ( arbitrary ) baseline input in terms of baseline mean μ syn 0 and standard deviation σ syn 0 . For improved readability these dependencies are not explicitly indicated in Eqs ( 71 ) and ( 72 ) . Note , that the nonlinearity F also depends on 〈w〉 , which is governed by Eq ( 23 ) . Since the mean adaptation current depends on the mean membrane voltage 〈V〉 we also consider a nonlinear mapping H for that population output quantity , ⟨ V ⟩ ( t ) = H ( μ f , σ f , ⟨ w ⟩ ) . ( 73 ) For the derivation below it is instructive to first consider an uncoupled population , i . e . , the input moments do not depend on rd for now . In particular , the input statistics are described by μ syn ( t ) = μ syn 0 + μ syn 1 ( t ) and σ syn ( t ) = σ syn 0 + σ syn 1 ( t ) . In the following , we derive the components F , Dμ and Dσ from the Fokker-Planck model for small amplitude variations μ syn 1 , σ syn 1 and for a slowly varying adaptation current ( as already assumed ) . We then approximate the derived linear filter components using suitable functions such that the convolutions can be expressed in terms of simple ODEs . Finally , we account for time-varying baseline input ( μ syn 0 ( t ) , σ syn 0 ( t ) ) and for recurrent coupling in the resulting low-dimensional spike rate models . In this contribution the membrane voltage spike shape has been neglected ( typical for IF type neuron models ) by clamping Vi and wi during the refractory period , justified by the observation that it is rather stereotyped and its duration is very brief . Furthermore , the spike shape is believed to contain little information compared to the time at which the spike occurs . Nevertheless , it can be incorporated in the aEIF model in a straightforward way using the following reset condition , as suggested in [43]: When Vi reaches the spike voltage Vs from below we let Vi decrease linearly from Vs to Vr during the refractory period and increment the adaptation current wi ← wi + b at the onset of that period . That is , Vi and wi are not clamped during the refractory period , instead , Vi has a fixed time course and wi is incremented by b and then governed again by Eq ( 15 ) . This modification implies that the average membrane voltage in Eq ( 23 ) needs to be calculated over all neurons ( and not only the nonrefractory ones ) , that is , 〈V〉 is calculated with respect to p + pref , where p ref ( V , t ) = ∫ 0 T ref r ( t - s ) δ ( V - V sp ( s ) ) d s with spike trajectory Vsp ( t ) = Vs + ( Vr − Vs ) t/Tref , cf . [43] . The same applies to the steady-state mean membrane potential in Eqs ( 1 ) , ( 39 ) and ( 76 ) , i . e . , 〈V〉∞ is then given by ⟨ V ⟩ ∞ = ∫ - ∞ V s v p ∞ ( v ) d v + ( 1 - ∫ - ∞ V s p ∞ ( v ) d v ) V r + V s 2 , ( 93 ) instead of Eq ( 38 ) . Notably , the accuracy of the adiabatic approximation ( Eq ( 15 ) ) does not depend on the refractory period Tref in this case . That type of spike shape can therefore be considered in the FP model and the low-dimensional models in a straightforward way without significant additional computational demand . Note , however , that for the spec2 model a nonzero refractory period is not supported ( see above ) . For an evaluation of the spike shape extension in terms of reproduction accuracy of the LN models see [23] ( Fig . 4 . 15 in [23] ) . | Characterizing the dynamics of biophysically modeled , large neuronal networks usually involves extensive numerical simulations . As an alternative to this expensive procedure we propose efficient models that describe the network activity in terms of a few ordinary differential equations . These systems are simple to solve and allow for convenient investigations of asynchronous , oscillatory or chaotic network states because linear stability analyses and powerful related methods are readily applicable . We build upon two research lines on which substantial efforts have been exerted in the last two decades: ( i ) the development of single neuron models of reduced complexity that can accurately reproduce a large repertoire of observed neuronal behavior , and ( ii ) different approaches to approximate the Fokker-Planck equation that represents the collective dynamics of large neuronal networks . We combine these advances and extend recent approximation methods of the latter kind to obtain spike rate models that surprisingly well reproduce the macroscopic dynamics of the underlying neuronal network . At the same time the microscopic properties are retained through the single neuron model parameters . To enable a fast adoption we have released an efficient Python implementation as open source software under a free license . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"action",
"potentials",
"neural",
"networks",
"engineering",
"and",
"technology",
"signal",
"processing",
"membrane",
"potential",
"electrophysiology",
"neuroscience",
"signal",
"filtering",
"mathematics",
"algebra",
"network",
"analysis",
"computer",
"and",
"information",
"sciences",
"animal",
"cells",
"linear",
"filters",
"approximation",
"methods",
"cellular",
"neuroscience",
"cell",
"biology",
"linear",
"algebra",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"eigenvalues",
"neurophysiology"
] | 2017 | Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation |
There are many well-known examples of proteins with low sequence similarity , adopting the same structural fold . This aspect of sequence-structure relationship has been extensively studied both experimentally and theoretically , however with limited success . Most of the studies consider remote homology or “sequence conservation” as the basis for their understanding . Recently “interaction energy” based network formalism ( Protein Energy Networks ( PENs ) ) was developed to understand the determinants of protein structures . In this paper we have used these PENs to investigate the common non-covalent interactions and their collective features which stabilize the TIM barrel fold . We have also developed a method of aligning PENs in order to understand the spatial conservation of interactions in the fold . We have identified key common interactions responsible for the conservation of the TIM fold , despite high sequence dissimilarity . For instance , the central beta barrel of the TIM fold is stabilized by long-range high energy electrostatic interactions and low-energy contiguous vdW interactions in certain families . The other interfaces like the helix-sheet or the helix-helix seem to be devoid of any high energy conserved interactions . Conserved interactions in the loop regions around the catalytic site of the TIM fold have also been identified , pointing out their significance in both structural and functional evolution . Based on these investigations , we have developed a novel network based phylogenetic analysis for remote homologues , which can perform better than sequence based phylogeny . Such an analysis is more meaningful from both structural and functional evolutionary perspective . We believe that the information obtained through the “interaction conservation” viewpoint and the subsequently developed method of structure network alignment , can shed new light in the fields of fold organization and de novo computational protein design .
Proteins are amino–acid polymers capable of folding into unique three–dimensional functional states . The information for the structure formation is contained within their amino–acid sequence [1] . With an enormous amount of data available on genomic sequences in organisms and the structures of the proteins they encode , it has become evident that despite the large sequence space , the structure space is rather limited [2]–[4] . It has been predicted that merely a few thousand protein folds are needed to generate the entire repertoire of the multimillion strong protein universe [5] , [6] . The limited number of folds has been explained as a result of optimization of backbone packing [7] , [8] . A recent analysis of the fold space showed that the atomic interaction network in the solvent–unexposed core of protein domains are fold–conserved , and that the network is significantly distinguishable across different folds , providing a “signature” of a native fold [9] . As a common rule , homologous sequences generally take up similar folds and the sequence divergences are concomitantly accompanied by structural variations [10] . However , increasing number of identified sequences and folds show a significant departure from this rule , i . e the same fold is able to house highly dissimilar protein sequences [11]–[14] . Folds like the TIM ( Triosephosphate Isomerase ) barrel , Rossmann , αβ–plait , and all β–immunoglobins are taken up by divergent sequences thereby underscoring the availability of limited fold space . These folds with their simple and symmetric architectures seem to be favorable folds for a large number of non–homologous sequences . Such folds are of special interest since their investigation would provide profound insights into the principles governing protein folding and stability . Although functional variations are related to structural variations , it has been established that proteins with disparate structures may retain their function during the course of their evolution as long as the local active site geometry is maintained [10] , [15] . Triosephosphate Isomerase ( TIM ) Barrel is one the ancient folds with considerable sequence diversity [2] . It is also one of the ubiquitously occurring enzymatic folds and hosts the most diverse enzymatic reactions catalyzing five of the six classes of biochemical reactions [16] , [17] . Thus TIM barrel , possessing both structural and functional diversity , has appealed both structural biologists and biochemists equally over the years . Factors responsible for its structural maintenance and functional diversity have been investigated in detail since its first structural discovery in 1975 [16] , [18]–[24] . The fold consists of an alternating helix–loop–strand secondary structure motif , where the strands assemble into the core β–barrel . This β–barrel is therefore formed by parallel strands , which is a rarity in fold space [24] . The outer rim of the barrel is maintained by helix–sheet and helix–helix interactions . Evolutionary studies suggest that there are evidences for both divergent [23] and convergent [20] evolution of the TIM barrel proteins , and hence , its evolution is being highly debated . A large number of computational studies have been carried on this fold , focusing mainly on their prevalence in the enzymes of various organisms catalyzing different functions , their structural and evolutionary properties [16] , [21]–[26] . In this study we have explored the factors responsible for the stability of TIM fold taken up by dissimilar sequences . Unlike earlier studies that focus on residue conservation , we have focused on interaction conservation as the basis of understanding the underlying structural determinants of the TIM fold . Although this is a novel method , several concepts related to protein sequence-structure-function relationship have been explored and quantitative results have been presented in the literature . For instance , evolutionary concepts were implemented in identifying pair-wise [27] and sets of residues , called as a “sectors” , that have undergone correlated mutations [28] in the protein sequences . At the structure-dynamics level , coarse-grained network models have shown that proteins with similar architecture exhibit similar large-scale dynamic behavior [29] and the differences usually occur in regions where specific functions are localized . Energetic coupling between residues has been investigated both experimentally by mutation followed by biochemical measurements [30] and from computational methods [31] . The classical problem of studying the structure-function relationship in allostery has been addressed from protein structure network point of view [32]–[36] . In essence the protein sequence-structure relationship and the structural changes accommodating their biological function have been investigated by a variety of methods . Here , we have made the preliminary attempt to study the role of conserved interactions in stabilizing a fold by ( a ) analyzing residue–residue interactions obtained from atomistic force fields; ( b ) investigating the interactions and their threshold energy values at a global level by constructing Protein Energy Networks ( PEN ) ; ( c ) obtaining a common PEN for a family of proteins ( f–PEN ) by structure based alignment followed by the construction of a common energy–weighted interaction matrix; ( d ) using the f–PENs to study the conserved interactions responsible maintaining the fold and ( e ) exploiting the conservation of interactions ( obtained from f–PENs ) to deduce phylogenetic relationship ( trees ) as opposed to the commonly practiced sequence based methods . PENs are structure networks where the constituent amino–acids are the nodes and the edges represent the non–covalent interactions among them . By representing the interactions as interaction energies ( obtained from molecular mechanics force fields ) , both the chemistry and the geometry of the amino–acids are better represented than other contact–based structure networks [37] , [38] . We have used structural similarities between the remote homologues of TIM barrel fold to align their PENs to obtain information on the extent of interaction conservation among them . The analysis of f–PENs has provided us a wealth of information in terms of the strength of interactions and their conservation ( at pair–wise as well as at the level of a collection of multiple interactions ) . We have been able to identify the factors responsible for the stability of the different secondary structural interfaces in the TIM fold . In general we have observed that the residues involved in high–energy interactions to have more conservation than the residues forming low–energy vdW dominated interactions . We have seen that high–energy conserved interactions are present in the central β–barrel stabilizing it and in the catalytic loop regions helping in the functioning of the protein . The interface between helices and sheets are dominated exclusively by low–energy interactions between non–conserved residues , thus contributing much to the sequence diversity . We also observed that interaction conservation based phylogeny represents the structural and functional evolution better than those derived from sequence conservation . The new outlook from “interaction conservation” has shed more light on the factors behind the fold organization of TIM fold by sequentially diverse homologues . Such observations are unique and we believe that this method will pave an alternate way for understanding the basis of organization of other folds as well . Furthermore , the information on interaction conservation can enable more controlled engineering of new proteins with enhanced structural/functional properties .
The TIM fold comprises three major secondary structural interfaces: the central β–barrel , α/β and α/α ( Figure 1a ) . The central β–barrel is formed by staggered parallel β sheets forming the β/β interface and makes up the core of the fold ( Figure 1b ) . The α/β interface flanks the barrel and is formed by the most common α–X–β motif ( where X can be any secondary structure like loops and β turns or even separate motifs ) . The helices interact with each other to form the α/α interface facing the exterior . It has been shown that the face of the fold with the C–terminal ends of the barrel and the adjoining loops contain the active–site residues , thus forming the catalytic face of the fold ( Figure 1b ) [18] . As mentioned earlier TIM fold is rich in both sequential and functional diversity marking it a viable system for studying sequence–structure–function relationship . The analysis of the Protein Energy Networks ( PENs ) provides a rationale to investigate the non–covalent interactions in proteins at various levels such as the interacting pairs ( edges ) , network of connected residues ( clusters ) , nodes connected by a large number of interactions ( hubs ) as a function of interaction energy . The domains of the TIM barrel fold in the dataset ( Table S1 ) are represented as energy weighted structure networks ( PENs ) , in which the constituent amino–acids are considered as nodes and the edges are weighted based on the non–covalent interaction energies among the amino–acids ( Eq 3 , Methods Section ) . Such a representation of PEN , capturing the non–covalent interaction energies at the atomic level , is capable of providing a consolidated view of the forces stabilizing the fold of the protein , yet retaining the details of individual interactions . It is to be noted that highly favorable interactions ( for example , −25 kJ/mol ) will be referred to as “high–energy” interactions , whereas less favorable interactions ( for example , −10 kJ/mol ) will be referred to as “low–energy” interactions . A range of unweighted PENes can be generated from the PEN using specific maximum energy cutoffs ( e ) to define the edges ( Eq 4 , Materials and Methods ) . It was earlier noted from the PENs of a set of globular proteins that at low energies ( e>−10 kJ/mol ) the network is dominated by hydrophobic vdW interactions and above this value ( e<−10 kJ/mol ) , the electrostatic interactions starts dominating the edges in the PENs [38] . The ljPENs are generated to focus exclusively on the vdW interactions by excluding the dominant terms of electrostatic interactions . The largest cluster ( LC , see Materials and Methods ) profiles as a function of ‘e’ for both PENs and ljPENs are provided for the present dataset of 81 TIM barrel domains ( Figure S1 ) . It is clear that the domains show three distinct network behaviors as a function of ‘e’ ( Figure S1a ) . In the high–energy region ( e<−20 kJ/mol , henceforth denoted as pre–transition region ) , the LC size is small with the network connected by electrostatic interactions . The size of the LC increases in the intermediate energy region ( −20<e<−10 kJ/mol , transition region ) following a sigmoidal profile by accruing low–energy vdW interactions and to encampass the whole protein in the low–energy region ( e>−10 kJ/mol , post–transition region ) , where the vdW interactions are dominant , tethering together local pockets of high–energy interactions . The LC profile of ljPENs is similar to PENs except that the mid–transition point is around −7 kJ/mol ( Figure S1b ) , due to the absence of high–energy electrostatic interactions . The TIM barrel domain is a common fold adopted by a large number of diverse sequences . Here we ask the question whether these domains are stabilized by similar patterns of interactions . Despite high sequence diversity we find common patterns of interactions of equivalent energies emerged when investigated at the family level . The family level classification of the TIM fold was obtained from the SCOP database [39] . We constructed family specific PENs for a chosen ‘e’ value ( f–PENes ) ( Figure 2 ) and obtained the equivalent node/edge/network information from the multiple structural alignments of the constituent members ( Materials and Methods ) . Each edge in the family specific network is given a commonality coefficient ( ccij ) value indicating the frequency of occurrence of that edge/interaction in the f–PENe ( Eq 5 and Figure 2f ) . A ‘cc’ value of one corresponds to the presence and a ‘cc’ value zero represents the absence of interaction within a spatially similar position of the fold in all the members of a TIM family . Thus various f–PENe ( cc ) can be generated for a specific family where f–PENe ( 1 . 0 ) represents interactions that are present in all the members of the fold and f–PENe ( 0 . 5 ) represents interactions that are present in at least half the members of the family . In order to determine the role of an amino–acid ( node ) type in maintaining an interaction ( edge ) , we have used an Entropy based Conservation score ( EC ) for each node in the f–PEN ( see Methods Section 3 . 6 ) . Generally if EC is greater than zero then there is a degree of conservation of that residue in the family , while a negative EC score shows that the residue is not conserved in that position . Therefore , cc is a measure of “interaction conservation” between two nodes and EC is a measure of “residue conservation” of the nodes . We have analyzed f–PENes in the dataset for edge distribution in different secondary structural interfaces namely the central β–barrel , α/β and α/α interfaces . We further explore the network parameters like clusters and hubs in PENs and f–PENs to determine the maintenance of the fold architecture in the TIM fold despite low sequence homology . In our analysis we principally focus on f–PENs at the pre–transition region ( ∼e<−18 kJ/mol , Figure S1a ) for studying the electrostatic contribution to the fold and the post–transition region of f–ljPENs ( ∼e<−8 kJ/mol , Figure S1b ) for obtaining the vdW contribution . By analyzing the distribution of the conserved edges across different interfaces it is possible to determine how the fold is maintained irrespective of the residue conservation . While the interaction–based studies discussed so far is a step above the residue level investigation , the network parameters like clusters and hubs go beyond pair–wise , by providing a collective view of multiple interacting residues . For instance , even if common interacting pairs in a family of structures are not obvious , a collection of residues interacting at a threshold energy level at similar structural locations can be detected as clusters . Therefore , we have utilized the PENs and f–PENs to study certain network properties like hubs and clusters to further understand the formation and stabilization of the fold . One of the major implications in understanding protein sequence–structure–function relationship is that we can obtain a variety of evolutionary information . Classically , existing phylogenetic methods exploit sequence conservation information to infer relationships and recent increase in structural data has resulted in the inclusion of structural features to deduce relationships between proteins [43] . The most commonly used sequence conservation based methods fail to obtain correct relationships between remote homologues due to the misgivings of sequence alignment techniques in the “twilight region” of the sequence–structure space . Here we deduce improved similarity relationships between remote homologues of the TIM fold through quantification of the similarity of interactions ( edges ) from their PENs ( details described in Materials and Methods ) . Figure 6 shows the comparison of the cladograms ( a map of the hierarchical clusters ) obtained from the interaction based and sequence based techniques . It can be readily seen that the interaction conservation based method clusters proteins of the same family under the same clade better than the sequence conservation based method . It should be noted that the SCOP classification of families is based on sequence or structure or functional similarities . The interaction based phylogeny matches very well with the SCOP classification than the sequence based method for the same dataset . Despite low sequence identity ( ≤30% ) we were able to find domains that exhibited as high as ∼85% interaction conservation ( between d1r0ma1 and d1muca1 from DGDL family ) . These observations show that the interaction based phylogenetic tree may be able to cluster the members of the family better than a residue based classification scheme . Lockless and Rangathan [27] introduced a sequence-based method to investigate statistical interactions between residues ( Statistical Coupling Analysis ( SCA ) ) . Later Halabi et al . , grouped these statistically correlated amino-acids into quasi-independent groups called sectors and studied their characteristics in Serine proteases [28] . Here we have made the preliminary attempt to compare the interaction-energy based approach with the sequence based SCA approach . We selected β-glycanase family of TIM fold for this comparison . The interactions ( ≤−10 kJ/mol ) common to this family were identified and cross verified with correlated mutations obtained from SCA . Although the correlation appeared to be weak at the pair-wise level , significant correlations are identified when the collective behavior of these correlated pairs are examined . In other words , there is a significant match between the residues of the sector from SCA and the clusters obtained from the present energy based analysis . The results have been pictorially depicted in Figure 7 ( details of the underlying calculations and comparison are provided in Table S2 and Table S3 ) . Interestingly , the agreement is more in the regions stabilizing the structure . The residues located more towards the function are identified by SCA and the PEN clusters encompass more of the residues required for the structural integrity . Based on this reasonable correlation of the SCA sectors and PEN clusters , we emphasize the fact that the protein structures should be viewed as a collective entity and an examination of individual residues and pair interactions in isolation may not always provide a holistic view of the structure and function of proteins . This feature was also reiterated by the coarse-grained network model studies on Rossmann-like domain proteins [29] . A weak agreement of pair-wise correlations from SCA predictions with the biochemical experiments on double mutants of PDZ domain perhaps may be attributed to this reason . Furthermore , fundamental issues like divergent [23] or convergent [20] evolution of proteins like TIM barrel , whose sequences are so diverse , has always been debated [16] . Extensive investigation by complimentary approaches such as PEN , SCA and essential mode dynamics should be able to provide more clarity into such systems . The sequence–structure relationship is a well–researched area , however , the factors that drive highly diverse sequences to fold into the same structure has not been well understood because of the apparent absence of consensus information from sequence similarity analyses . Here we have taken an alternative approach in which we consider “interaction conservation” and analyze whether the preservation of interactions is an essential driving force in the formation of the fold rather than sequence conservation . TIM barrel fold is one of the most popular folds that have a high sequence variability and functional diversity . In this study we have analyzed non–homologous members of different families of the TIM fold and investigated various factors that contribute to the formation of the fold . We have adapted the concept of interaction networks in order to study these protein structures from a global perspective . Also , by using interaction energies we have realistically represented the residue–residue relationships in the network . The subsequent methodology that exploits structural alignment to align the Protein Energy Networks ( PENs ) in a family of TIM fold has provided us with valuable information on the conservation of interactions in the family . It was evident from our analyses of conserved interactions that the central β barrel is being stabilized by ( a ) sequentially long–range conserved high–energy interactions and ( b ) low–energy vdW interactions from residues of the neighboring strands interacting in tandem , in addition to the hydrogen–bonding network in the sheet . Also , the analysis of the other interfaces like the α/β and the α/α show an absence of any high–energy conserved interactions , and being maintained exclusively by low–energy interactions . In general we found that the residues involved in high–energy interactions are better conserved than low–energy interactions . From our cluster analysis it was seen that the conserved interactions are not segregated into isolated interacting pairs but rather coalesce together to form a sub–network of interactions . Our hub analysis has shown that the charged and the conserved residues are favorable to be hubs at higher energies , while hydrophobic residues with less conservation act as hubs at lower energies . All these results suggest that ( a ) the β barrel formation driven by high–energy interactions ( with the participating residues being conserved ) seem to be an important step in the organization of the TIM barrel; ( b ) the formation of the other interfaces mainly by low–energy interactions ( with residue conservation being immaterial ) is a more canonical step in the fold formation common to all the folds of the α/β class , and can be taken up by a variety of sequences , thus contributing the high sequence diversity . These conclusions concur with several experimental observations that suggest that while the α/β interfaces in TIM are resilient to mutations the β barrel is sensitive [18] , [40] , [41] , [44] . We have analyzed the structural and functional relevance of conserved interactions in the regions involving loops in various TIM barrel families . We found that loop based high–energy conserved interactions ( e<−20 kJ/mol ) are present near the active sites of a number of TIM barrel families . This suggests that the loop based interactions are conserved during evolution to maintain the active site geometry for successful enzymatic functioning of the TIM proteins . Therefore this method can be used in functional annotation of hypothetical proteins in cases where there are structural homologues but no sequence homologues . Finally we exploited the concept of “interaction conservation” to construct a cladogram and compare it with the sequence based cladogram . The outcome of analysis reinforces our assumption that it may be interaction conservation and not necessarily sequence conservation that determines the fold organization . Our attempt to correlate our method with that of SCA suggests that there may be significant correlation between the sector residues and cluster residues . However , extensive investigation by complimentary approaches such as PEN , SCA and Elastic Network Models ( ENM ) should be carried out and such an analysis will be able to provide more clarity to studying such protein systems . The methodology of representing the protein structures as interaction energy based networks and using structural alignments to align these networks has provided us a very convenient handle to study structure homology among sequentially diverse proteins , from a network point of view . We were able to study the salient features that stabilize the TIM fold using this method , and also analyze how interaction conservation can play an important role in the formation of this fold . We believe that this methodology can shed valuable knowledge on the fold maintenance by remote homologues and pave way for useful de novo design and analysis of protein folds .
The dataset used in this analysis is composed of domains from the TIM fold given by Structural Classification Of Proteins ( SCOP ) [39] . The coordinates for the domains are obtained from ASTRAL [45] . The domains are sorted into their respective families as given in SCOP . The sequence identity within the members of each family is less than 30% . The culling of domains with higher sequence identity was done using cd–hit [46] . All the families constitute at least three members ( except HMGL like domains ( HMGL ) and Adenosine/AMP deaminase ( ADA ) families , ( see Table S1 ) ) . The dataset consisting of 19 families with 81 domains is presented in Table S1 . The secondary structural elements ( SSE ) for each domain were assigned using DSSP [47] . Structure network construction requires the coordinates of the interacting amino acids ( nodes ) and a criterion to define the interactions ( edges ) . A purely geometry based all-atom interaction can be deduced from the crystal structure , which we had used to describe the Protein Structure Networks ( PSNs ) [48] . Recently , we have considered the chemistry in greater detail by explicitly considering the interaction energy between residues [38] . Although qualitative results are expected to be similar from both formalisms , PEN has the advantage of capturing subtle details of importance , whereas the PSN approach has the advantage of being simple to adopt ( Figure S7 ) . The interaction energies can be obtained on a single structure or on an ensemble of structures of a given protein . The set of structures can be obtained from experiments ( X-ray crystallography , Nuclear Magnetic Resonance ) under different environment or by simulations from a single starting conformation . In the case where the conformational changes are small , a set of conformations will provide a statistically relevant average structure and in the case of large conformational change , it is advantageous to study them independently to characterize the structural variations in different states of the same protein , for example to understand the effect of ligand binding . In this study we have used Molecular Dynamics ( MD ) simulations to obtain the structure ensemble for each of the TIM domains . We have considered the crystal structures for all the proteins in the dataset ( Table S1 ) and subjected them to minimization and Molecular Dynamics simulations for a brief time interval ( 20 ps ) to obtain interaction energies in equilibrium . In our earlier studies we have shown that the correlation between interaction energies calculated using the equilibrated structures from 2 ns simulations and 20 ps simulations was around 90% [38] . The MD simulations were performed using GROMACS ( GROningen MAChine for Simulations ) [49] for just 20 ps and structure ensemble for each domain is obtained by sampling its trajectory every 1 ps . The average interaction energies among the amino–acids are computed using the structure ensemble thus obtained . Selenomethionines ( MSE ) present in certain domains like d1pbga_ and d1uwsa_ from Glycosyl hydrolase family ( F1GH ) were converted to Methionine and missing atoms in the residues were generated using Swiss PDB viewer [50] . The best conformations for both the modified and the built residues recommended by the Swiss PDB viewer from its rotamer library were used . The details of the construction of PEN are given in Vijayabaskar and Vishveshwara [38] . Briefly , the non–bonded interaction energies ( Eij , Eq 1 ) between all pairs of residues were obtained as a summation of the electrostatic ( given by columbic potential , Eq 2 ) and van der Waals ( given by the Lennard Jones ( LJ ) potential , Eq 3 ) interaction energies averaged over the structure ensemble . PEN is constructed with amino–acids as nodes , and with edges drawn between all pairs of residues except the sequential neighbors . The edges are weighted with the calculated Eij . ljPENs take into account only the van der Waals ( vdW ) interactions ( i . e Eij = VLJ ) . Unweighted networks ( PENe and ljPENe ) can be obtained for a specific maximum energy cutoff ‘e’ as given in Eq 4 . ( 1 ) ( 2 ) ( 3 ) ( 4 ) Steps involved in the construction of the family specific PEN ( f–PEN ) by alignment of the PENes of its members is given in detail in Figure 2 . Domains in a family are structurally aligned using MUSTANG ( MUltiple STructural AligNment AlGorithm ) [51] ( Figure 2b ) . A family specific Multiple Structure based Sequence Alignment ( MSSA ) was obtained for all the members of a given family and the residues that are aligned in the MSSA are referred to as Equivalent residues . Residues that were not structurally super–imposable were compensated within the alignment using gaps ( Figure 2c ) . The PENes are remapped using the equivalent node information obtained from the MSSA ( Figure 2d ) . The gaps in the MSSA are introduced as virtual nodes in the corresponding PENes , such that the edge weights of a virtual node to all other nodes in the PEN were highly unfavorable ( Eij = 100 kJ/mol where either i or j is a virtual node ) ( Figure 2d ) . The remapped PENes are then aligned to form the family specific PEN ( f–PENe ) ( Figure 2e ) such that the nodes are equivalent and edges exists only if they were present in any of the realigned PENes ( Figure 2f ) . In a f–PENe , the values ( X , Eq 5 ) of the edges can vary from 0 to M , where 0 represents the absence of an edge in all the members of the f–PENe and M represents the edge being present in all members . Therefore each edge is given a commonality coefficient ( ccij , Eq 5 ) , and it represents the measure of the frequency of occurrence of an edge between equivalent nodes within the members of a family . ( 5 ) where X is the total number of members having the edge between nodes ‘i’ and ‘j’ with interaction energy better than ‘e’ , Aeij is the element of the adjacency matrix of the remapped PENe and M is the total number of members in the family ( Figure 2e ) . Thus , a family specific PEN can be denoted as f–PENe ( cc ) where ‘e’ is the interaction energy cutoff used to generate PENes for all the members of the family and edges are constructed only if their ccij is better than ‘cc’ . The f–PENe ( cc ) consists of both equivalent and virtual nodes and represents spatially conserved interactions across the members of that family . In fact both the ‘e’ and ‘cc’ values can be used as weights in order to construct a weighted matrix . However , in this study , we have considered un-weighted matrix at given values of ‘e’ and ‘cc’ . Entropy based Conservation scores ( EC ) for each alignment position in the MSSA were obtained using AL2CO [52] . In this method the entropy is normalized with the mean and standard deviation . Thus better the entropy score , the more conserved the amino–acids are at that position . A network similarity matrix ( S ) for any two members ‘a’ and ‘b’ in the dataset is constructed as given in Eq 6 . S is an adjacency matrix which takes a value of 1 if the interaction energies between equivalent residues in the MSSA are similar . The Similarity Score ( SSab ) between the PENs of any two members in the dataset is derived as given in Eq 7 . This value is the fraction of edges that is conserved between the two members . The distance matrix ( D , Eq 8 ) with each row and column representing a domain in the dataset , is used to construct the phylogenetic tree . ( 6 ) ( 7 ) ( 8 ) where Ea and Eb are PENs of any two members in the dataset that are remapped based on their pairwise MSSA , and N is the total number of nodes in the remapped PENs . The concept of structure conservation is often used in structural alignment methods [53] , [54] . For instance , an alignment based on dynamic characteristics of structurally similar but functionally distinct proteins have been reported earlier [29] . The identification of energetically similar edges in two proteins done in the present study , can also serve as a basis for alternate method of structural alignment , although it is not pursued in this study . Clusters were generated using Depth First Search ( DFS ) algorithm [55] . Family specific clusters in a family of TIM fold are connected sub–graphs present in the f–PENe ( cc ) with a size of at least three ( i . e . isolated pair–wise interactions are not considered as clusters ) . The Largest Cluster ( LC ) in a PENe is the cluster with highest number of constituent nodes . Degree which is the total number of edges incident on a node , is a measure of connectivity of that node in the network . Hubs are defined as nodes with higher degree . The family specific hubs are those residues which are spatially equivalent and have a degree of at least 3 . | Proteins are polymers of amino-acids that fold into unique three-dimensional structures to perform cellular functions . This structure formation has been shown to depend on the amino-acid sequences . But examples of proteins with diverse sequences retaining a similar structural fold are quite substantial that we can no longer consider such phenomenon as exceptions . Therefore , this non-canonical relationship has been studied extensively mostly by studying the remote sequence similarities between proteins . Here we have attempted to address the above-mentioned problem by analyzing the similarities in the spatial interactions among amino-acids . Since the protein structure is a resultant of different interactions , we have considered the proteins as networks of interacting amino-acids to derive the common interactions within a popular structural fold called the TIM barrel fold . We were able to find common interactions among different families of the TIM fold and generalize the patterns of interactions by which the fold is being maintained despite sequence diversity . The results substantiate our hypothesis that interaction conservation might by a driving factor in fold formation and this new outlook can be used extensively in engineering proteins with better biophysical characteristics . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"protein",
"structure",
"biology",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] | 2012 | Insights into the Fold Organization of TIM Barrel from Interaction Energy Based Structure Networks |
Embedded within large-scale protein interaction networks are signaling pathways that encode response cascades in the cell . Unfortunately , even for well-studied species like S . cerevisiae , only a fraction of all true protein interactions are known , which makes it difficult to reason about the exact flow of signals and the corresponding causal relations in the network . To help address this problem , we introduce a framework for predicting new interactions that aid connectivity between upstream proteins ( sources ) and downstream transcription factors ( targets ) of a particular pathway . Our algorithms attempt to globally minimize the distance between sources and targets by finding a small set of shortcut edges to add to the network . Unlike existing algorithms for predicting general protein interactions , by focusing on proteins involved in specific responses our approach homes-in on pathway-consistent interactions . We applied our method to extend pathways in osmotic stress response in yeast and identified several missing interactions , some of which are supported by published reports . We also performed experiments that support a novel interaction not previously reported . Our framework is general and may be applicable to edge prediction problems in other domains .
Networks of protein interactions can reveal how complex molecular processes are activated in the cell . However , even for model species , only a fraction of true physical interactions are known [1] , [2] and experimental verification of all remaining potential interactions is unlikely in the near future . Furthermore , interactions are often condition- or tissue-specific [3] while current experimental methods often focus on one condition and one cell type [4] . Thus , computational techniques to predict protein interactions have flourished as a means to build more complete interaction maps [5] , [6] . Signaling pathways are subnetworks of proteins that communicate via a series of interactions and are often only activated under specific conditions ( e . g . stress response , development , etc . ) . Perturbations of proteins within such pathways have been linked to several diseases [7] . In addition , pathways are often conserved , thus studying their interactions in model organisms may help elucidate cellular response mechanisms in other organisms [8] . Signaling pathways typically contain upstream proteins ( e . g . receptors on the cell's surface ) that sense changes in the environment or that are directly involved in host-pathogen interactions . These proteins trigger a signaling cascade that leads to downstream transcription factors ( TFs ) , which consequently carry forth regulatory programs . The former set of proteins can be considered sources that transmit information to a set of targets . Experimental protocols can infer source proteins based on their interactions with external stimuli ( e . g . host-pathogen interactions [9] ) , and likewise targets can be determined via expression or knockdown assays . This motivated several techniques that have been proposed to extract pathways from global interaction networks by searching for efficient and robust paths between the given sets of sources and targets [10]–[13] . These techniques , however , do not try to infer putative interactions that are missing from the network . We model this problem computationally by searching for missing edges that increase the network's ability to explain the signaling cascade from sources to targets . Many methods have been proposed to computationally predict protein-protein interactions . These methods leverage a variety of data sources , including physical docking models and protein structure [14] , [15] , evidence based on orthologous proteins in related species [16] , microarray expression profiles [17]–[21] , literature mining [22] , sequence-level features [23]–[27] , or a combination of heterogeneous features to learn a predictive model or classifier [28]–[32] ( for reviews , see [5] , [6] ) . Network-only approaches range from completing defective cliques [33] to analyses based on the shared topology or the distance between two candidate proteins [34] , [35] to embeddings of the network to find non-interacting but adjacent proteins in the new space [36] , [37] . None of these approaches , however , leverage known sources and targets to make pathway-aware predictions . Further , most other approaches use local cues of similarity , whereas our approach attempts to optimize a global distance function . There has also been theoretical work on predicting “shortcut edges” in graphs to minimize the average shortest-path distance amongst all nodes in the graph [38] or the diameter of the graph [39]–[42]; however , these works also do not exploit specific sources and targets when making predictions . In this paper , we propose a combinatorial optimization framework to identify missing interactions that putatively mediate the passage of signals within pathways . Formally , we seek the edges to add to the network that maximally decrease the shortest-path distances between sources and targets ( Figure 1 ) . We consider several variants of the problem: an unrestricted setting where long paths are allowed; a restricted setting where source-target paths are bounded by a maximum number of hops; and a setting where each target is only required to be regulated by a single source . In computational experiments using a confidence-weighted protein interaction network for S . cerevisiae under the high osmolarity glycerol ( HOG ) osmotic stress response pathway , we find that we can drastically reduce source-target distances via the addition of only a few edges . Several new interactions predicted by our method , while missing from current databases , are supported by the literature; other interactions are novel predictions . We selected one of our novel predictions , , for condition-specific follow-up experiments . New knockout microarray experiments suggest that Sok2 is indeed functionally downstream of Tpk2 in the osmotic stress response , and previous evidence suggests that this could be due to Tpk2's direct phosphorylation of Sok2 .
We assume we are given a directed protein interaction network with nodes ( ) corresponding to proteins and edges ( ) to physical interactions . Protein interaction networks inferred from high-throughput experiments are often noisy [2] , [43] , therefore we assume each edge is weighted by a value denoting our confidence in the interaction [13] . We also assume we are given a set of sources and targets . The sources are typically upstream proteins in pathways that initiate a signaling cascade to the downstream targets ( transcription factors ) . Our goal is to predict missing ( directed ) edges that lie centrally “in-between” the sources and targets . These edges putatively belong to the pathway but are not present in current databases . Formally: Problem 1 [Shortcuts] . Given a directed and weighted graph and a set of sources and targets , add edges to to minimize , i . e . the total shortest-path distance between all source-target pairs . We use the shortest-path distance to measure the distance between proteins and in the weighted network ( as opposed to other distance measures , such as those based on random walks [44] , [45] ) because the shortest path represents a direct and specific series of high-likelihood signaling events . The shortest path between two nodes in a weighted graph can be very long ( either because the diameter is long or if the path uses many high confidence , and hence lowly weighted , edges ) . This may not be biologically reasonable since pathway targets are typically no more than 5 edges away from their closest sources [13] . Thus , we also propose a hop-restricted version of our problem . Let be the shortest-path distance between and that uses at most links ( if no such satisfying path exists ) . Formally: Problem 2 [Shortcuts-X ( restricted ) ] . Given a directed and weighted graph , a set of sources and targets , and a maximum allowable number of hops , add edges to to minimize , i . e . the total hop-restricted shortest-path distance between the pairs . Both of these problems ( general and hop-restricted ) assumes that each transcription factor receives signal from each source . Another variant of these problems asks to minimize the distance between each target and any single source ( biologically , the same source does not need to regulate all targets , but every target is regulated by some source ) . Formally: Problem 3 [Shortcuts-SS ( single source ) ] . Given a directed and weighted graph and a set of sources and targets , add edges to to minimize , i . e . the total shortest-path distance between each target and its single closest source . We also consider the analogous problem in the hop-restricted setting: Problem 4 [Shortcuts-X-SS ( restricted , single source ) ] . Given a directed and weighted graph , a set of sources and targets , and a maximum allowable number of hops , add edges to to minimize , i . e . the total hop-restricted shortest-path distance between each target and its single closest source . In the Supporting Text ( Text S1 and Figure S1 ) we prove that these four edge predictions problems are NP-hard . Given these hardness results , we consider a heuristic greedy algorithm for our suite of edge prediction problems . The Greedy algorithm selects edges to add iteratively: in each step , it predicts a single edge that maximally reduces the objective function . In the case of the Shortcuts problem , this means the algorithm will pick , from amongst all possible non-existent edges , the edge that maximally reduces the global shortest-path distance between all sources and targets . In a network with nodes and directed edges , there are non-existent edges ( excluding self-loops ) . In the yeast network we use , and , which means there are almost 20 million directed edges to test . Each edge can alter the shortest path from any source to any target hence , done navely , this would require recomputing the shortest-path lengths from each source to each target 20 million times just to add a single edge . One trick to make the search more efficient is to notice that , if a candidate edge reduces the distance from source to target then the new shortest path from to consists of three components: the shortest path from to , the candidate edge , and the shortest path from to . If it does not reduce the distance , then the distance from to remains as it was without . Thus , the procedure can be made more efficient by pre-computing the shortest-path distances from every source to every other node in the network , and separately from every node in the network to every target . ( This latter step can be further optimized by computing the distance from every target to every other node in the reverse graph , where edge directions are reversed . ) To compute the cost reduction of candidate edge with weight we check if: ( 1 ) The left-hand side sums the ( pre-computed ) distance from to , the weight of the new edge , and the distance from to ; the right-hand side is the previous distance from to without the new edge . ( If we do not know the weight of the non-existent edge we set to encourage its usage; other values , e . g . based on the predicted likelihood of the interaction that is derived from other data sources may also be reasonable ) . The minimum of these two values is stored and is summed over each source-target pair , yielding the new objective function cost assuming exists in the graph . The edge that maximally decreases the cost function over all possible edges is added to the graph . Box 1 shows the pseudocode for the Greedy algorithm for the Shortcuts problem . This trick reduces the algorithm's complexity in each step from in the nave case to . The first term considers all possible non-existing edges , each of which requires a constant lookup ( Equation 1 ) ; the second term is the pre-computation of single-source shortest-path distances using Dijkstra's algorithm . Thus , we get a runtime reduction of a factor of , which in our case is roughly 60 , 000 for each iteration . For the hop-restricted problems ( Shortcuts-X and Shortcuts-X-SS ) , we seek short paths between sources and targets with the restriction that each path uses a maximum of hops . This bound stems from the fact that many pathways in signaling databases such as KEGG [46] depict on average 5 edges between a target and its closest source [13] . Other approaches have used similar bounds ( 3–4 [47] ) . To constrain the shortest paths to use at most edges , we use a modified version of the Bellman-Ford algorithm [48] , [49] . This algorithm computes single-source shortest paths starting from a node by relaxing every edge in each step ( i . e . checking if traveling along the edge yields a shorter path to the destination node ) . Shortest-path distances are propagated through the graph and , as a result , after iterations , the algorithm computes the shortest-path distance from to every other node in the graph using at most hops . Computing the updated cost for a candidate edge , however , requires a slightly different strategy than the one used before . The main challenge is that the new edge induces one hop , and hence , the two sub-cases ( and ) must be constrained to use hops in total . This leads to 6 possible cases to consider for the each candidate edge when computing the new distance from source to target , and each can be computed in constant time: ( 2 ) In the first case , the new path from to uses hop to reach , hop to reach ( via the new edge ) , and hops to reach . The cost of this path consists of the Bellman-Ford distances shown ( where e . g . is the distance from to that uses at most hops ) plus the weight of the new edge ( ) . Cases 2 and 3 follow similarly . If either endpoint of the candidate edge involves or , then a similar rule is checked ( cases 4 and 5 ) . Each case is considered and the one that yields the minimum distance is compared with the previous distance from to ( without the new edge; case 6 ) . For the Shortcuts-X problem , this is repeated for each source-target pair; for Shortcuts-X-SS this is done for each target to find the hop-restricted distance to its closest source . After an edge is added , the Bellman-Ford distances are re-computed ( from sources to all nodes in the graph and from targets to all nodes in the reversed graph ) and the process is repeated greedily . This algorithm takes time per step . The first term evaluates the benefit of each possible edge ( Equation 2 ) ; the second term is the pre-computation of single-source hop-restricted shortest-path distances using the Bellman-Ford algorithm . We compare our Greedy algorithm to several other popular algorithms for predicting missing interactions . Several strategies have previously been used to validate network-based edge predictions [34] , [61] . First , we describe the notion of potential edges , and then we describe four validation techniques using these edges . The STRING database aggregates protein-protein associations from over a dozen other pathway and protein interaction databases and combines these with computational predictions based on sequence , co-expression , literature mining , interactions between orthologous proteins , and other biological features to provide a comprehensive protein relationship resource [50] . Only a small subset of these relationships , however , represent physical binding interactions . The remainder , which we term potential edges , are composed of other types of experimentally- or computationally-derived non-physical associations . STRING assigns edge weights for both types of edges ( physical and potential ) based on biological and computational evidence supporting the link . One benefit of the STRING weighting scheme is that weights for both the physical and potential edges are computed in the same manner and thus are directly comparable . Edges supported by multiple types of evidence have higher weights [62] . Our predictions are based solely on the network topology and source-target connectivity — they do not rely on sequence , gene expression , or any of the other data types — and are therefore completely independent of the STRING predictions . Starting from only the STRING physical interactions , one way to test our predicted edges is to count how many of them exist within the set of STRING potential edges . The STRING potential network contains 659 , 719 of the approximately 20 million possible interactions ( 3 . 5% ) , hence identifying the correct interactions is still very challenging . Although identifying STRING potential edges is useful , these predictions may not bear any relevance to the HOG pathway from which the sources and targets are derived . Our second validation approach considers a prediction as correct if it exists within the STRING potential edges and it connects two proteins from the set of sources , targets , and other known HOG pathway members [46] , [53]; otherwise it is incorrect . KEGG and the Science Signaling Database of Cell Signaling provide an unbiased set of pathway members that are not dependent on our own subjective curation efforts . Although these pathway databases omit some HOG members reported in recent literature ( e . g . the upstream proteins in de Nadal and Posas [54] ) and other uncharacterized proteins that partake in the osmotic stress response , the proteins and interactions they do contain are provided by pathway experts and are thus trustworthy . Therefore this test serves as a strong proxy for each method's ability to make high quality and pathway-relevant predictions . Our third test measures the quality of an edge prediction based on how much its addition reduces the objective function cost . This approach directly quantifies the method's ability to reduce the distance between sources and targets . Finally , as a fourth test , we conducted the following cross-validation experiment: We started with the unoriented STRING PPI network and identified all the edges connected to at least one HOG-relevant node ( there were 1079 such edges ) . Because our algorithm specifically predicts edges that lie between sources and targets , these HOG-related edges were used as the cross-validation set . We performed 5-fold cross-validation for the Greedy algorithm using the Shortcuts and Shortcuts-X objective functions and counted how many of the top 10 predictions exactly recovered a left-out edge . The probability that a random prediction would recover a left-out edge from amongst all the potential edges is extremely small ( 0 . 033% ) , and thus this test is also very challenging . It is also challenging because it is difficult to decouple training and test sets of edges . Leaving out even a very small number of edges may result in an entirely different pathway structure in which alternative paths may emerge as more likely . This is especially prevalent on small scales: for example , if edges exist and the edge is left-out , then it is entirely reasonable to predict edge as a shortcut of the path chain . More generally , any chain can be shortcutted by directly connecting the ends ( which may often be hubs through which the paths diverge ) , and single-use edges that play a peripheral role in the pathway may be bypassed altogether . To summarize , we consider four approaches to validate edge predictions . The first test compares the prediction accuracy of each method in identifying STRING potential edges . The second test compares the prediction accuracy of each method when predicting STRING potential edges that are also relevant to the HOG pathway . The third compares each method's ability to reduce the objective function cost . And the fourth measures the cross-validation accuracy of the Greedy algorithm .
Our Greedy algorithm achieves the greatest cost reduction compared to the other four methods over all variants of the pathway-aware edge prediction problems ( Figure 2 ) . Moreover , Greedy substantially decreased source-target distances after adding only a few edges . For example , after adding 3 edges , the Shortcuts cost ( measured as the total shortest-path distance amongst source-target paths ) can be reduced to approximately 60% of the original cost . In contrast , it takes 10 edges for Direct-ST to achieve the same ratio . The Betweenness algorithm does monotonically decrease the cost , however , because edges are added based on greater usage ( as opposed to greater explicit cost reduction ) , its reduction is much slower than Greedy overall . The global methods ( Jaccard and Short-Path ) do not leverage the sources and targets and therefore are unable to reduce source-target distances at all; in general , there are an enormous number of possible edges that play no putative role in the pathway and it is difficult for these methods to disambiguate these edges from HOG-relevant edges . The tremendous cost reduction seen with the Greedy predictions implies that there are a few missing edges in the network whose addition may cover a large bulk of the information flow in the network . For Shortcuts-SS and Shortcuts-X-SS , both Greedy and Direct-ST perform equally well . This is because there are only 11 paths to optimize over instead of 55 ( each target to a single source ) . Thus , a viable strategy is to find the target that is furthest away from any source and connect a source directly to it . This can greatly reduce the cost function , even if no other path uses this edge , though this need not be the case in general . Next , we judged the quality of the predictions based on how well they overlapped with the STRING potential edges and with HOG-relevant proteins ( Figure 3 ) . In these tests , the accuracy of the method is the percentage of predicted edges , made from amongst all possible non-existent edges , that lied in the relevant set . When only considering support in STRING ( Figure 3A ) , we find that the global methods ( Jaccard and Short-Path ) significantly outperform the source-target-based methods . In particular , every prediction made by the Jaccard algorithm is correct according to STRING as are over 60% of the Short-Path predictions . This result agrees with previous studies that showed that network distance and shared topology are strong indicators for functional or physical relatedness [33] , [35] , [37] , [57]–[59] . The probability of predicting a STRING potential edge from amongst all possible edges is only 3 . 5% , and thus most approaches perform significantly better than baseline . This test , however , does not tell us whether the predictions bear any relevance to the HOG pathway , which is the primary focus of this study . To better home-in on HOG-relevant predictions , we filtered the STRING potential edges to only include those edges that connected two known HOG-related proteins . Figure 3B shows that the global methods do not make any predictions that relate to the HOG pathway . On the other hand , the Greedy predictions remain at the same level in both tests , which implies that its predictions tend to be highly accurate and lie amongst HOG-related nodes . The difference is especially pronounced in the hop-restricted cases , where Greedy is more accurate than any other method by roughly 40% ( Shortcuts-X ) . Two of these edges connect Hog1 to known HOG transcription factors , Msn4 and Cin5 — both previously established interactions in KEGG [46] or the literature [63] ( which are missing from the STRING database and thus do not appear in the original network we used ) . The probability of predicting a HOG-relevant STRING potential edge from amongst all possible edges is only 0 . 076% , which is much lower than the accuracy of all three source-target-based algorithms . Of the top 15 predictions made by Greedy and Betweenness for the Shortcuts-X problem , only one prediction overlaps , and a similar trend holds for the other objectives . This likely stems from the fact that Greedy takes the magnitude of the cost reduction into account , whereas Betweenness only computes the number of shortest paths that use the candidate edge . Because both algorithms perform significantly better than baseline , this implies that they may provide complementary predictions and both may be reasonable depending on the use case . Interestingly , despite their similar performance in cost reduction for Shortcuts-SS and Shortcuts-X-SS ( Figure 2 ) , Greedy makes more accurate predictions than Direct-ST ( Figure 3 ) . This is because there are many cases where a direct source-to-target prediction can be equivalently replaced by a target-target interaction . For example , if was added in the first step , the predictions and ( regulated via ) both equally reduce the cost from a single source ( ) to the target . However , target-target interactions are more likely to exist within the STRING potential edges than direct source-target edges , and indeed Greedy makes several TF-TF predictions ( e . g . ) , thereby giving it an advantage . To show that the orientation step is indeed useful in extracting HOG paths given sources and targets , we ran each algorithm on the unoriented STRING PPI network ( Figure S2 ) . We found that for both hop-restricted objective functions , the Greedy algorithm makes more HOG-relevant predictions when using the oriented network ( 53% vs . 46% for Shortcuts-X and 40% vs . 20% for Shortcuts-X-SS , compared to using the unoriented network ) . Moreover , the global methods ( Short-Path and Jaccard ) also benefited significantly from the orientation , which implies that defining network neighbors more precisely can help in identifying putative interactions . Overall , these results show that the global methods perform well in identifying putative interactions , but that the Greedy algorithm can home-in on more pathway-consistent interactions while drastically reducing source-target distances . While predicting plausible edges from amongst all possible edges serves as a strong validation technique , in practice , we would also like to leverage other data sources ( such as expression , sequence , and literature evidence ) when making predictions . To naturally integrate these sources into our framework , instead of predicting from amongst all possible edges , we only predict from amongst the set of STRING potential edges ( Methods ) . Each potential edge is weighted by STRING with a confidence value in , which we explicitly set to ( Equations 1 and 2; in the previous sections , was given a default weight of 0 ) . By using these data types and weights together , we can pinpoint putative interactions that have evidence from a wide variety of biological sources as well as evidence from the network . Table 2 presents the top 10 predictions made by the Greedy algorithm for the Shortcuts objective function , many of which are known physical interactions missing from STRING . The and predictions have direct evidence of physical interaction according to BiOGRID [64] , but were not present in the STRING network . The and predictions lied within the STRING binding edges ( and thus represent physical interactions ) , but were either oriented in the opposite direction or were left out of the oriented network . was originally oriented , but the Greedy algorithm suggests that that this edge was either oriented incorrectly or is bidirectional . was left out of the network because the orientation algorithm did not find any length-bounded paths that included this edge . Although in general biological pathways are short , this prediction exemplifies an exception where considering longer pathways through the edge improves the source-target connectivity . These correct predictions demonstrate that our approach can correct for limitations of the edge orientation . For the following three predictions , we verified both the physical interaction between the two nodes and the directionality ( which is not possible for edges validated with the undirected STRING or BioGRID databases ) . The prediction ( ) involves two general stress TFs that play a substantial role in the HOG pathway [51] . Harbison et al . [65] showed that indeed Msn4 binds the MSN2 gene in the succinic acid stress condition . This study did not profile Msn4 DNA binding in osmotic stress , but it is plausible that this stress-activated TF could bind MSN2 in other conditions as well . The prediction ( ) was recently shown by Pokholok et al . [63] to occur in osmotic stress . We discuss the prediction ( ) at length in the next section . Overall , 7 of the top 10 predictions have support for direct physical binding in the cell . In addition , the prediction was not directly supported in the literature but warrants further study . Both Reg1 and Msn4 have been shown to physically associate with the 14-3-3 proteins Bmh1 and Bmh2 [66] but have not yet been shown to directly interact with one another . Proteins with a common physical interaction partner may be more likely to directly interact themselves than proteins with other types of functional connections ( e . g . genetic interactions ) [33] , [35] , [57] . Table 3 presents the top 10 predictions made by the Greedy algorithm for the Shortcuts-X objective function , which attempts to model more biological constraints by imposing a hop-restriction on the source-target paths . Remarkably , the top three predictions ( , , and ) represent best-case predictions: The two genes/proteins involved are known to physically interact , the directionality is correct , and the interaction is highly relevant to osmotic stress response . In particular , and are core HOG pathway interactions that are well-characterized [51] and appear in KEGG [46] , but lack evidence for physical binding in STRING . The MAPK Hog1 is central to the HOG response program , and its activation of downstream TFs is a critical component of the response . The other two validated predictions involve HOG pathway members as well . Sho1 is a transmembrane osmosensor , and its branch of activation of Hog1 is known to be mediated by interaction with Cdc42 [67] . The interaction is also present as part of the related starvation subpathway of MAPK in KEGG [46] . Finally , the prediction ( ) is between two members of the Sho1 HOG pathway input branch [53] . Overall , of the 659 , 719 STRING potential edges considered , only 0 . 0011% are in KEGG , and thus the fact that 3 of the top 10 predicted edges lie in KEGG is highly significant ( , Fisher's exact test ) . Other predictions whose physical interaction could not be validated also involve pairs of HOG pathway members . Some predictions occur between the two independent upstream input branches in the pathway ( e . g . and ) or between upstream proteins and proteins that are very far downstream ( e . g . ) . From an algorithmic standpoint , these edges do indeed provide faster diffusion of signal from sources to targets; however , they may not represent direct interactions that occur in the cell . In contrast , the prediction is a shortcut within the Sho1 input branch , which contains the cascade [54] . Note that several of these predicted edges have very high weights ( e . g . ) from STRING reflecting their strong functional dependencies , which makes them more likely to be selected by our algorithm . However , several predictions were made despite lower evidence ( e . g . ) , which suggests that their addition strongly aided source-target connectivity . Interestingly , none of the top 10 predictions directly connects a source to a target . This further necessitates an approach like ours versus Direct-ST . To further validate our ability to extract accurate pathway-relevant predictions from within the potential set , we conducted 5-fold cross-validation experiments by leaving out HOG-relevant edges ( see Methods ) . The probability that a random prediction would recover a left-out edge from amongst all the potential edges is extremely small ( 0 . 033% ) . Using the Greedy algorithm , we found that 12% ( 16% ) of the top 10 predictions for Shortcuts ( Shortcuts-X ) recovered a left-out edge . Recovering one correct edge ( 10% ) yields a P-value of and recovering two correct edges ( 20% ) yields a P-value of ( Fisher's exact test ) . Both values are significant ( our results lie between them ) further supporting the ability of our method to make accurate edge predictions . To explore the sensitivity of our results to the hop-restriction length , we repeated our computational experiments using a hop-restriction length of . Overall , we found similar qualitative performance for the algorithms when predicting from amongst all possible edges ( Figure S3 ) . However , when predicting from amongst the potential set , we found only a few overlapping predictions with those made when the hop length was 5 . Interestingly , these included the well-known HOG interactions , and , suggesting that the most confident and likely predictions are not wholly affected by the decreased hop restriction . Of course , some different predictions are also to be expected; for example , using a hop length of 4 , the algorithm makes predictions for and . While these predictions make sense algorithmically , they do not make sense biologically because they attempt to shortcut the sources of the pathway directly to a core node ( Hog1 ) . This suggests that 4 hops may be too restrictive and may motivate using a hop restriction of 5 in future efforts . We also found that our approach was able to recover missing interactions when not leveraging the STRING-derived weights ( see Text S1 ) . This implies that our approach is not entirely dependent on the potential edge weights and that our objectives are well-defined . To demonstrate our approach's ability to make novel , biologically meaningful predictions we selected for experimental validation . This was a top prediction for two objective functions ( for Shortcuts-SS it was the prediction and for Shortcuts it was the uncharacterized prediction; Table 2 ) . As we showed , the addition of a few edges can greatly reduce the objective function cost , and therefore we place more confidence in these top edges . Verifying a directed protein-protein interaction at the mechanistic level requires extensive experimentation and is beyond the scope of this work . However , genetic experiments such as gene deletions can establish condition-specific causal relationships between proteins in signaling pathways . For instance , loss-of-function mutations and gene over-expression were used to identify and order the genes along the apoptosis pathway in C . elegans [68] . In our case , if Tpk2 controls the TF Sok2 in osmotic stress , TPK2 deletion should affect Sok2's regulatory activity in this condition . Because many interactions along signaling pathways occur post-translationally , we would not expect the SOK2 gene to be differentially expressed in the mutant even if Tpk2 does activate or inhibit Sok2 at the protein level . Instead we determine the degree to which the deletion alters Sok2's function as a transcriptional regulator . As predicted , the knockout significantly affected genes bound by Sok2 ( , Fisher's exact test; see Supporting Text S1 for microarray details and Table S1 for lists of affected genes ) . The knockout alone cannot confirm whether the interaction is direct or indirect , but clearly establishes that there is a functional connection between these proteins that is active in osmotic stress . Moreover , the orientation of the predicted edge is correct because if Sok2 were upstream of Tpk2 in the pathway , its bound genes would be unaffected by TPK2 deletion . To test the significance of our knockout ( KO ) with other perturbation experiments , we used the Rosetta compendium [69] of 300 KO expression experiments and compared the overlap between differentially expressed ( DE ) genes in each experiment with the list of Sok2 targets ( see Supporting Text S1 ) . Of 301 experiments , only 31 ( 10 . 3% ) had a lower P-value than the one obtained from our TPK2 KO . In the other direction , we considered 117 additional TFs for which a high confidence set of targets exists [70] . For each , we computed the significance of the intersection between their targets and genes affected by the TPK2 deletion using Fisher's exact test . Similar as the test above , of the 118 tests only 14 ( 11 . 9% ) had a lower P-value than our predicted Tpk2-Sok2 pair . Combined , our predicted interaction ranked close to the top 10% in these two independent analyses further supporting our prediction .
Our knockout experiment examines the predicted relationships between Tpk2 and the target TF Sok2 in hyperosmotic stress conditions . Tpk1 , Tpk2 , and Tpk3 form the catalytic subunit of protein kinase A ( PKA ) , the complex at the heart of the Ras/cAMP/PKA signaling pathway [72] . Through interactions with its many substrates , PKA is involved in general stress response , metabolism , growth , ribosome biogenesis , and various other biological processes [72] , including osmotic stress response . PKA's involvement in the osmotic stress response is parallel to the HOG pathway [73] . Msn2 , Msn4 , and Sko1 , which along with Hot1 are considered to be the primary HOG pathway TFs [51] , are each affected by PKA in osmotic stress [73] , [74] . Decreased PKA activity modulates the repressive effects of Sko1 in this condition . This behavior is complementary to Hog1's phosphorylation of Sko1 , which also alleviates Sko1 repression of its target genes [73] . While Tpk2's role in osmotic stress is well-established , Sok2 is not considered to be a core HOG pathway TF , but was rather assumed to be controlled by the primary TFs [52] . However , genetic screens illustrate that its role in the osmotic stress response may be larger [75] , [76] . Our TPK2 knockout establishes a functional link between Tpk2 and Sok2 in which Sok2 is downstream of Tpk2 . A previous genetic interaction reported by Ward et al . , who suggested that PKA may directly phophorylate Sok2 , supports this directionality and relationship [77] . Subsequent experiments confirmed that active PKA phosphorylates Sok2 when glucose is the carbon source [78] . However , this link does not appear in other conditions . For example , Sok2 was found to function in a pathway parallel to PKA [79] and Tpk2 [80] in pseudohyphal growth and adhesive growth , respectively . In addition , Tpk2 does not interact with Sok2 in a mutant yeast strain that is sensitive to exogenous cAMP [81] . These findings highlight the importance of pathway-specific predictions of missing interactions as opposed to general protein interaction predictions . Our results showing that Tpk2 functionally affects Sok2 in osmotic stress coupled with previous evidence that the Sok2 sequence contains a consensus PKA phosphorylation site at amino acids 595 to 598 [7] , [78] and that PKA phosphorylates Sok2 in other conditions , suggests that the predicted interaction warrants direct experimental validation . Despite their high sequence similarity , the three Tpk's have distinct sets of substrates [82] so confirmatory future work must specifically examine Tpk2 phosphorylation . Because in vivo verification of a kinase-substrate interaction is challenging , the next step experimentally will be to show that Tpk2 phosphorylates Sok2 in osmotic stress in vitro . Peptide arrays and kinase assays have been used to validate computational phosphorylation predictions in vitro [83] . Proteome chips did not detect Sok2 as a Tpk2 substrate in vitro [82] , highlighting the need for osmotic stress-specific experiments in order to validate our condition-specific prediction . Following in vitro confirmation any number of in vivo strategies could be used to decisively validate the interaction ( see Morandell et al . [84] for a review ) . For instance , electrophoretic mobility shifts in kinase deletion strains can provide in vivo evidence of phosphorylation and validate in vitro interactions [82] , [83] . Our analysis comparing the set of Sok2 targets and affected TPK2 knockout ( KO ) genes with other binding and KO experiments indicated that the overlap between these two sets lies close to the top 10% in both tests . It is not surprising that the deletion of other genes also leads to the differential expression of some Sok2 targets , but the fact that this occurs for only a fraction of experiments suggests that our KO holds against the statistical background . Further , of the 31 KOs with a higher overlap , none correspond to protein products that directly bind to Sok2 according to STRING . As for the overlap between the other TF targets and our TPK2 KO set , again , it is not surprising that other TFs were affected by the KO because deletions can affect both direct binding partners and proteins further downstream . The more significant Tpk2-TF associations do not correspond to direct binding in the interaction network — the average distance in the interaction network is 4 . 8 edges — which suggests that these are not candidates for missing interactions . Recently , there has been a great increase in the amount of experimentally derived protein interaction data in several species [85] and in our ability to experimentally query host-environments and host-pathogen interactions [9] . Given these networks , the problem of identifying response pathways can now be tackled in multiple species . A key problem in such studies is dealing with missing interactions , as these prevent algorithms from recovering the correct information flow . The method we presented in this paper is the first to address this issue in a pathway-specific context and can be applied to any species for which such data exists . Further , our method may have use in other domains , for example , in network design where the goal is to reduce routing lags or to aid the flow of information between entities in a network . | Networks of protein interactions encode a variety of molecular processes occurring in the cell . Embedded within these networks are important subnetworks called signaling pathways . Pathways are initiated by upstream proteins ( called sources ) that receive signals from the environment and trigger a cascade of information to downstream proteins ( targets ) . Modeling the interactions that occur within this cascade is important because pathway disruption has been linked to several diseases . Further , the interactions help us better understand how cells respond to various conditions and environments . Unfortunately , interaction networks today are largely incomplete , which makes this analysis difficult . We provide a framework to model missing interactions in pathways by searching for interactions that putatively result in quicker and more efficient source-target cascades . We find that we can substantially shorten source-target distances with only a few additional edges and that many of our predicted edges have support in several knowledge databases and literature reports . We believe our approach will be useful to identify interesting and important pathway-centric interactions that have been missed by previous experimental assays . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"algorithms",
"systems",
"biology",
"computer",
"science",
"signaling",
"networks",
"biology",
"computational",
"biology"
] | 2012 | A Network-based Approach for Predicting Missing Pathway Interactions |
The bunyavirus genome comprises a small ( S ) , medium ( M ) , and large ( L ) RNA segment of negative polarity . Although genome segmentation confers evolutionary advantages by enabling genome reassortment events with related viruses , genome segmentation also complicates genome replication and packaging . Accumulating evidence suggests that genomes of viruses with eight or more genome segments are incorporated into virions by highly selective processes . Remarkably , little is known about the genome packaging process of the tri-segmented bunyaviruses . Here , we evaluated , by single-molecule RNA fluorescence in situ hybridization ( FISH ) , the intracellular spatio-temporal distribution and replication kinetics of the Rift Valley fever virus ( RVFV ) genome and determined the segment composition of mature virions . The results reveal that the RVFV genome segments start to replicate near the site of infection before spreading and replicating throughout the cytoplasm followed by translocation to the virion assembly site at the Golgi network . Despite the average intracellular S , M and L genome segments approached a 1:1:1 ratio , major differences in genome segment ratios were observed among cells . We also observed a significant amount of cells lacking evidence of M-segment replication . Analysis of two-segmented replicons and four-segmented viruses subsequently confirmed the previous notion that Golgi recruitment is mediated by the Gn glycoprotein . The absence of colocalization of the different segments in the cytoplasm and the successful rescue of a tri-segmented variant with a codon shuffled M-segment suggested that inter-segment interactions are unlikely to drive the copackaging of the different segments into a single virion . The latter was confirmed by direct visualization of RNPs inside mature virions which showed that the majority of virions lack one or more genome segments . Altogether , this study suggests that RVFV genome packaging is a non-selective process .
Rift Valley fever virus ( RVFV ) is a zoonotic bunyavirus of the genus Phlebovirus that causes recurrent outbreaks on the African continent , the Arabian Peninsula and several islands off the coast of Southern Africa . The virus predominantly affects ruminants , of which sheep are the most severely affected . Epizootics are characterized by massive abortions of pregnant ewes and high mortalities among newborns . Infected humans generally display mild flu-like symptoms , however in a minority of cases severe complications such as retinitis , hemorrhagic fever , and delayed-onset encephalitis may develop [1] . In humans , the overall case fatality ratio is estimated to range from 0 . 5 to 2% . Mosquito vectors of the Aedes and Culex genera are associated with RVFV transmission in endemic areas and are also present in other regions of the world with high ruminant density . Like all bunyaviruses , RVFV contains a tri-segmented single-stranded RNA genome of negative polarity [2] . The large ( L ) , medium ( M ) and small ( S ) genome segments are encapsidated by the nucleocapsid ( N ) protein , which is translated from a subgenomic mRNA transcribed from the genomic-sense S RNA . Encapsidated genome segments are referred to as ribonucleoproteins ( RNPs ) . The antigenomic-sense S-segment additionally encodes the non-structural protein NSs . NSs is the main virulence factor of the virus and is known to antagonize host innate immune responses [3–5] . The M-segment encodes the two major structural glycoproteins Gn and Gc [6] which are involved in host cell entry and fusion , respectively . The M-segment also encodes two accessory proteins , known as NSm and 78-kDa protein . NSm was shown to have anti-apoptotic function [7 , 8] and the 78-kDa protein was shown to be incorporated predominantly into virions matured in insect cells [9] . The L-segment encodes the RNA-dependent RNA polymerase , which is responsible for transcription of genes and replication of the viral genome [10] . Remarkably , and in contrast to many other RNA viruses , bunyavirus mRNA synthesis is coupled to translation to prevent premature transcription termination [11] . The termini of all bunyavirus genome segments are inverted complementary and facilitate the formation of a panhandle structure , which comprises signals for transcription , replication and encapsidation [12–19] . Bunyavirus particles assemble in so called ‘virus factories’ , located at the Golgi network [20–23] . In these factories viral budding is believed to be initiated by interactions of the RNPs with the cytoplasmic tail of the Gn protein [19 , 22 , 24 , 25] . How infectious particles , containing at least one S , one M and one L RNP , assemble is not yet fully understood . Interestingly , in 2011 Terasaki and co-workers provided some clues for a selective genome packaging process using a virus-like particle ( VLP ) system . They suggested that copackaging of S , M and L genome segments into individual RVFV virions is mediated by direct or indirect inter-segment interactions , with a central role for the M-segment [17] . Other findings however suggest that inter-segment interactions do not play a major role in RVFV genome packaging . A fully viable two-segmented RVFV variant lacking the M-segment was described [26] and RVFV replicon particles that comprise only S and L genome segments can be produced very efficiently [27 , 28] . More recent results further emphasize the flexibility of the RVFV genome . A RVFV variant with a ‘swapped’ S segment , encoding N from the NSs locus and vice versa , is viable [29] . Moreover , four-segmented RVFV variants were recently created , which may contain two or even three M-type segments [30] . Here , we investigated the RVFV genome packaging process using state-of-the-art fluorescence in situ hybridization ( FISH ) . Experiments with infected cells and mature virions revealed that copackaging of all three genome segments into individual particles is unlikely to involve the formation of a supramolecular complex . Instead , our results reveal that RVFV genome packaging is a non-selective process .
To investigate replication and recruitment of the RVFV RNA genome segments inside an infected cell , a single-molecule multicolor RNA FISH assay was developed . Probes were designed to be complementary to the S , M and L viral RNAs . After confirming specificity and optimizing sensitivity ( S1 Fig ) , the assay was used to evaluate the genome segment distribution in RVFV-infected Vero cells at 2 , 4 , 6 , 8 and 10 hours post infection ( hpi ) . At 2 and 4 hpi a single patch of up to 600 genome segments was detected in the cytoplasm of most infected cells ( Figs 1 and 2D ) . The location of the patch varied among cells and at higher multiplicity of infection ( MOI ) cells with more than one patch of genome segments were observed as well . Most likely , the genome segments of an infecting virion start to replicate near the site of infection immediately after fusion of the viral membrane with the endosomal membrane . At about 4–6 hpi the total level of genome segments had increased considerably and a more random cytoplasmic distribution of genome segments was observed . The average doubling time of a genome segment was estimated to be about 40 min . At 6–8 hpi recruitment of genome segments to the virion assembly site at the Golgi [20–23 , 31 , 32] became evident in most of the infected cells . The total level of cytoplasmic genome segments reached a plateau around 6 hpi , which is probably the result of ongoing replication and continuous Golgi recruitment and budding of particles containing mature RNPs . An average cytoplasmic inter-segment ratio approaching 1:1:1 between the S , M and L segments was observed during the first 4 hrs of infection , whereas later on , due to more efficient Golgi recruitment of the M-segment , the cytoplasmic ratios slightly changed ( Fig 2G ) . Remarkably , in about 30–40% of infected cells the segment ratios were strikingly different . Cells with about twice as many S , M or L segments as well as cells lacking any evidence of M-segment replication ( up to 25% ) were observed frequently ( Fig 3 and S2 Fig ) . It is important to note that cells infected with particles lacking an S and/or L segment will not reveal genome replication and are not detected by FISH . Altogether these results suggest that during particle assembly no quality control mechanisms are present that ensure packaging of each type of genome segment . To evaluate whether S , M and L genome segments form a supramolecular complex and comigrate to the Golgi prior virion assembly we evaluated the extent of S , M and L colocalization at 5 hpi . The 5 hpi time point was selected because at this stage of infection the genome segment density was relatively high and the resolution of spots , corresponding to single genome segments , was still sufficient to discriminate between colocalized spots and non-colocalized spots . Moreover , Golgi recruitment has not yet started at this time point . As a positive colocalization control , cells were probed with two differentially labelled probe sets recognizing either the Gn or Gc gene , which are both encoded by the M genome segment . As a negative control , cells were probed with a GAPDH mRNA probe set and a Gc probe set . The Pearson colocalization coefficient of the probe sets recognising either the Gn or Gc-coding region was on average 0 . 65 and the colocalization coefficient of the GAPDH and Gc probe sets was below 0 . 1 ( Fig 4A and 4B ) . These values , which are similar to what others have reported in the influenza field [33] , confirm that our FISH assay is well suited for studying genome segment colocalization . The Pearson colocalization coefficients of the different RVFV genome segments were all below 0 . 1 ( Fig 4C ) . This indicates that RVFV genome segments , in contrast to the genome segments of the influenza virus [33 , 34] , do not form a supramolecular complex consisting of more than one genome segment in the cytoplasm . The important role of the RVFV glycoproteins , specifically the cytoplasmic tail of the Gn protein , in RNP incorporation into virions is well recognized [19 , 22] . The involvement of the glycoproteins in intracellular genome segment recruitment is , however , less understood . Here we used our previously developed RVFV replicon particles , also referred as nonspreading RVFV ( NSR ) [27] , to study the role of the glycoproteins in genome segment recruitment in more detail . NSR particles are phenotypically similar to wild-type virus , however they cannot spread autonomously because they lack the glycoprotein-encoding M genome segment . Vero cells were infected with NSR and the spatio-temporal distributions of the S and L genome segments were determined by FISH ( Fig 5 ) . The results show that the total level of genome segments rapidly increased in time , similar as observed in RVFV infected cells ( Fig 2 ) . Importantly , no evidence of Golgi recruitment was observed at any time point ( Fig 5 ) . This suggests that in wild-type virus infected cells recruitment of genome segments is fully mediated by the glycoproteins , most likely mediated by the cytoplasmic tail of Gn , as was previously suggested by Piper and co-workers [19] . Remarkably , starting at 8 hpi , we consistently observed aggregates of genome segments in NSR-infected cells ( Fig 5 ) . The aggregates were randomly distributed and not associated with the Golgi . Probably , the absence of viral budding results in accumulation and subsequent aggregation of RNPs . In RVFV infected cells no such aggregates were found , not even at later time points . The NSR experiments suggested a major role for the RVFV glycoproteins , probably Gn , in genome segment recruitment . The RVFV glycoproteins Gn and Gc are normally produced from a glycoprotein precursor ( GPC ) protein that is proteolytically cleaved . Gn and Gc subsequently form heterodimers and mature at the endoplasmic reticulum ( ER ) and Golgi . Gn harbours a Golgi localization motif and Gc contains an ER retention signal [31] . We previously constructed RVFV-4s variants by splitting the M segment into two M-type segments encoding either the Gn or Gc protein [30] . We hypothesized that genome replication and recruitment is affected by changes in glycoprotein processing and genome organisation . To test this hypothesis we evaluated the spatio-temporal distribution of genome segments in RVFV-4s infected cells by FISH . Vero cells were infected with RVFV-4s and hybridized with probes complementary to the S , M-Gn , M-Gc and L genome segments . The results show that the M-Gn segment is replicated more efficiently compared to the M-Gc segment ( Fig 2I ) . Moreover , recruitment of the M-Gn segment to the Golgi was much more efficient compared to recruitment of the other segments ( Fig 6B ) . Recruitment of M-Gn was also more efficient compared to recruitment of the wild-type M-segment in RVFV infected cells ( Figs 1 and 6B ) . Since RVFV-4s is able to spread after infection at low MOI ( < 0 . 001 ) a significant population of particles in a virus stock is expected to contain all four genome segments . Interestingly , the FISH experiments at 6 hpi revealed that various infected cells ( up to 40% ) did not show evidence of M-Gc replication ( Fig 6C ) . Most likely these cells were originally infected with virions containing the S , M-Gn and L genome segments but lacking the M-Gc segment . The number of M-Gc lacking virions correlates very well with the reduced replication of the M-Gc segment ( Fig 2I ) and , like for wild-type virus , confirms that during particle assembly no quality control mechanisms are present that ensure packaging of all different segments , including M-Gc , into a single particle . Another interesting observation in RVFV-4s infected cells was the reduced replication of the S segment . Most likely there is increased competition for polymerase molecules in RVFV-4s infected cells ( 4 instead of 3 segments ) resulting in reduced replication of segments with a relative low affinity for the polymerase . Differences in polymerase affinity have already been shown at the transcription level [13] . A final characteristic of RVFV-4s infected cells was the presence of higher densities of genome segments near the plasma membrane later on in infection ( Fig 6D ) , suggesting that in RVFV-4s infected cells , various Gn molecules move to the plasma membrane and bind genome segments during transit . The ability of Gn to move to the plasma membrane , especially in the absence of Gc is well known [22 , 31] . Whether RVFV-4s is able to bud at the plasma membrane awaits further study . Altogether , the overall unbalance in genome segment replication , the enhanced Golgi recruitment of the M-Gn segment and the increased number of particles lacking one or more genome segments explain , at least partly , the observed attenuated phenotype of RVFV-4s [30] . Although the experiments thus far show that a supramolecular complex , consisting of an S , M and L genome segment is not formed in the cytoplasm , we cannot yet rule out the possibility that a supramolecular RNP complex is formed at the virion assembly site . During the influenza infection cycle , the formation of a supramolecular RNP complex is based on RNA-RNA interactions between the different segments and this process is believed to trigger viral budding [35 , 36] . To obtain additional information about the putative formation of a supramolecular RNP complex during the RVFV infection cycle we tried to rescue a RVFV variant with a codon shuffled M-segment ( Fig 7 and S2 Fig ) . Codon shuffling changes the genomic RNA sequence but does not affect the protein sequence and has limited effects on protein expression . When RNA-RNA interactions exist between the S , M and L RNPs , a virus with a codon shuffled M segment is expected to grow less efficiently . Interestingly , rescue of the RVFV variant with a codon shuffled M-segment , referred as RVFV-Mshuffled , was successful . Moreover , we additionally rescued a RVFV variant with a shuffled M-segment and an optimized S segment , referred as RVFV-MshuffledSopt ( Figs 7 and S3 ) . Both variants were able to grow with similar kinetics and to similar titers in Vero cells compared to the parental RVFV strain ( Fig 7 ) . The efficient growth of these variants further suggests that the formation of a supramolecular RNP complex does not drive the production of infectious RVFV virions . Altogether , the presented results suggest that RVFV genome packaging is a non-selective process . To obtain additional evidence for this conclusion we evaluated the genome segment content of mature virions . Virions in wild-type virus stocks ( produced on Vero cells ) were immobilized on coverslips and incubated with antibodies targeting the Gn glycoprotein and probe sets recognising the S , M and L genome segments as described in the M&M section . After confirming specificity and the ability to determine colocalization with this assay ( Fig 8B and 8C ) the genome content of >800 virions was determined . As expected , the results revealed a high level of heterogeneity in genome composition . Virions were observed that did not comprise any genome segment ( about 40% ) as well as virions with only one or two segment types ( Fig 8D and 8E ) . About 1 out of 10 virions showed evidence for the presence of all three different segments . The relatively low abundance of virions containing all the different segments is in full agreement with the FISH data obtained with infected cells and confirms the non-selective nature of RVFV genome packaging .
Although genome packaging of viruses with segmented genomes has intrigued researchers for decades , we are only just beginning to understand the molecular processes involved . In the field of segmented negative-strand RNA viruses , most knowledge resulted from studies with influenza virus . In the latest influenza model , genome packaging is proposed to be a highly selective process based on the formation of a supramolecular RNP complex [33–35 , 37 , 38] . From an evolutionary perspective , a selective genome packaging process for an 8-segmented virus is easily understood . If not selective , the influenza virus would need to produce about 400 particles to generate 1 particle that contains each of the 8 genome segments , which is rather inefficient . For bunyaviruses , which only have to package 3 segments , the evolutionary pressure to selectively incorporate genome segments during virion assembly is much lower . With this study , we provide evidence that RVFV uses a non-selective genome packaging strategy . At the beginning of this study , limited knowledge was available about the molecular mechanisms involved in RVFV genome replication , recruitment and packaging . Moreover , as explained in the introduction section , some results pointed towards a highly selective genome packaging strategy whereas others were compatible with a non-selective packaging process . In the current study , we investigated the molecular mechanisms involved in RVFV genome packaging by combining new tools such as replicon particles , four-segmented- and codon-shuffled viruses with state-of-the-art single molecule RNA-FISH . The absence of colocalization of RNPs in the cytoplasm ( Fig 4 ) , the similar to wild-type growth of codon shuffled variants ( Fig 7 ) , the efficient production of replicon particles ( Fig 5B ) , the observed heterogeneity in intracellular segment replication among infected cells ( Fig 3 and S2 Fig ) and the heterogeneity in segment composition of mature virions ( Fig 8 ) demonstrate that the non-selective genome packaging model is the most plausible model to date . The non-selective genome packaging model is in full agreement with the ability to construct a wide variety of RVFV variants without the need to conserve coding sequences and RNA structures [26 , 29 , 30] . We here demonstrate that replication of RVFV genome segments starts locally , probably near the site of fusion of the virion with the endosome , and subsequently ( within 4–6 h ) continues to proceed throughout the cytoplasm . After the replication phase , genome segments are recruited to the Golgi . Recruitment is probably mediated by interactions of the nucleocapsid protein , which covers the viral RNA , with the cytoplasmic tail of Gn [19 , 22 , 24 , 25] . After recruitment , a very heterogeneous population of virions , containing various amounts and types of genome segments , buds into the Golgi lumen . Virions with at least one S , M and L RNP will be able to produce progeny virions upon infection . Alternatively , co-infection with complementing particles may result in productive infection . Interestingly , virions containing antigenomic-sense RNPs may also contribute to the RVFV infection cycle [29 , 39] . In Fig 9 , a schematic presentation of the RVFV infection cycle , according to the newly obtained insights , is provided . Although our results suggest that a supramolecular RNP complex is not formed , or at least does not play a critical role in the RVFV replication cycle , we cannot exclude that some degree of selectivity exists , as has been previously suggested [17 , 18] . If some degree of selection indeed occurs , our results obtained with codon-shuffled variants suggest that this selection is mediated by the UTRs . A major finding in the RVFV-4s infected cells was the difference in replication efficiency of the M-Gn versus M-Gc segment . The difference in replication is not explained by large differences in segment size ( 2319 nt versus 1869 nt ) or differences in UTR sequence , since these are identical . An explanation might be that the NSm coding region , which is present in the M-Gn segment but absent from the M-Gc segment , contains a yet unknown cis-acting replication element . At first glance , the efficient replication of codon-shuffled variants seems to contradict this hypothesis . However , a short stretch of nucleotides downstream of the 5’ UTR and a short stretch of nucleotides upstream of the GnGc open reading frame were maintained in these viruses ( S3 Fig ) to preserve efficient translation . These sequences are not present in the M-Gc segment and could be involved in replication . Future research will determine if these sequences indeed contain cis-acting replication signals . Another very consistent finding throughout the experiments was the enhanced Golgi recruitment of the M segment compared to the S and L segments in wild-type virus infected cells and the enhanced recruitment of the M-Gn segment in RVFV-4s infected cells . The enhanced recruitment was calculated by dividing the cytoplasmic segment ratios before ( 4 hpi ) and after Golgi localization ( 8 hpi ) . The percentage of cytoplasmic M-segments decreased with 16% in wild-type virus infected cells and the percentage of M-Gn segments decreased with 11% compared to the other segments in RVFV-4s infected cells . The enhanced recruitment of Gn encoding segments can be explained by the coupled transcription and translation in bunyaviruses . Specifically , we propose the following sequence of events: transcription of the M segment is initiated in the cytoplasm , followed by translation of the Gn signal sequence by free ribosomes . A complex of M genome segments , mRNA transcribed from this segment and ribosomes is then translocated to the ER and subsequently to the Golgi compartment to continue membrane-associated translation of M segment mRNAs . Although the current study provides evidence for a non-selective genome packaging process during RVFV virion assembly , we do not think these results can be extrapolated to all bunyaviruses . Whereas RVFV RNPs are expected to bind to the cytoplasmic tail of the Gn protein via the N protein , for other bunyaviruses , such as Crimean Congo hemorrhagic fever virus ( CCHFV ) , evidence was provided that the viral RNA directly interacts with the cytoplasmic tail of the Gn protein [40] . This N-independent interaction might be segment specific and could facilitate a more selective packaging process . The latter could also explain the lower particle to PFU ratio of CCHF compared to RVFV [41] . In summary , this study suggests that RVFV genome packaging is a non-selective process and does not involve the formation of a supramolecular viral RNA complex .
The RVFV strain Clone 13 [42] was kindly provided by Dr . Michèle Bouloy ( Institut Pasteur , France ) . RVFV-4s , RVFV-Mshuffled and RVFV-MshuffledSopt were constructed using reverse genetics . Sequences were based on the published Clone 13 genome ( Accession: DQ375417 . 1 , DQ380213 . 1 , DQ380182 . 1 ) . Working stocks were obtained by low MOI ( 0 . 01 ) infections of Vero E6 cells ( ATCC CRL-1586 ) grown in Eagle's Minimum Essential Medium ( EMEM ) supplemented with 5% FBS , 1% non-essential amino acids , 1% L-glutamine and 1% antibiotic/antimycotic . RVFV replicon ( NSR ) stocks were obtained by transfection of replicon cells , which stably maintain replicating S and L genome segments with an expression plasmid expressing the RVFV glycoproteins as described previously [27] . RVFV sequences , flanked by a minimal T7 promoter and a hepatitis delta virus ribozyme sequence , were synthesized by the GenScript Corporation ( New Jersey , USA ) and cloned into pUC57 plasmids . RVFV-4s M-type plasmids were designed ( Clone 13 sequence based ) , as previously described , to contain half of the GPC gene , either encoding ( NSm ) Gn or Gc ( segmented at the tyrosine-675 codon of the GPC ) [30] . The RVFV-Mshuffled segment was designed by shuffling of the NSmGnGc gene resulting in 77% homology . The RVFV-Sopt plasmid contains a codon-optimized N gene for optimal expression in mammalian cells . NSmGnGc shuffled and N optimized sequences are presented in S2 Fig and S3 Fig respectively . RVFV-4s , RVFV-Mshuffled and RVFV-MshuffledSopt were rescued using a three ( or four for RVFV-4s ) plasmid system . Briefly , BSR-T7/5 cells [43] ( previously kindly provided by Prof . Karl-Klaus Conzelmann ) were seeded in T75 flasks ( 2 , 500 , 000 cells/flask ) in GMEM containing 5% FBS and after overnight incubation medium was replaced with Opti-MEM . Cells were transfected with a total of 20 μg pUC57 transcription plasmids per flask using TransIT transfection reagents according the manufacturers’ instructions ( Mirus , MAD ) . Three to five days post transfection , supernatants were collected and used to infect Vero E6 cells . All RNA-FISH assays were performed according the Stellaris FISH method originally developed by Ray , Femino and co-workers [44 , 45] . For the RNA-FISH cell assays Vero E6 cells ( 15 , 000 cells/well ) were seeded on CultureWell 16 Chambered Coverglass ( Grace Biolabs ) . After overnight incubation , cells were incubated with the indicated viruses for 1 h ( MOI 0 . 1–0 . 01 ) and at the indicated time points infected cells were fixed for 10 min with fixation buffer ( 75% methanol , 25% glacial acetic acid ) . Cells were subsequently washed with PBS ( 5 min ) and pre-hybridization buffer ( 5 min ) consisting of 10% formamide and 2 mM vanadyl ribonucleoside complex ( VRC ) in 2x concentrated SSC . Subsequently , cells were probed overnight ( 18 h ) at 37°C in hybridization buffer ( 10% formamide , 2 mM VRC , 10% w/v Dextran-Sulphate in 2 times SSC ) with the indicated probe sets ( S1 Table ) at an end concentration of 125 nM . The probes were designed using the RNA FISH Probe Designer available online at www . biosearchtech . com and purchased from Biosearch Technologies Inc . ( Petaluma , CA ) . After the hybridization , cells were extensively washed with pre-hybridization buffer and 2 times SSC . Cell nuclei were visualized using DAPI and prior imaging , cells were submerged in VectaShield mounting medium ( H-1000 , Vector Laboratories ) . For the RNA-FISH virion assays , undiluted virus stocks were incubated for 3 h in the CultureWell 16 Chambered Coverglass wells at 37°C . The negatively charged glass binds virions relatively efficient . After bound virions were fixed and hybridized according the procedure described for cells , with the only exception that hybridization time was reduced to 4 h , virions were visualized with the RVFV-Gn specific monoclonal antibody 4-39-cc [46] in combination with a DyLight 350 labelled Rabbit anti-Mouse ( H+L ) conjugate ( ThermoFisher Scientific ) . Immobilized virions were finally submerged in VectaShield prior imaging . Images of infected cells and immobilized virions were obtained with an inverted fluorescence wide-field ZEISS Axioskop 40 microscope with appropriate filters and a 1 . 3 NA 100× oil objective in combination with an Axiocam MRm CCD camera . Raw cell images were subsequently deconvolved and analysed using Huygens deconvolution software ( SVI , Hilversum , The Netherlands ) in combination with the ImageJ program ( National Institutes of Health , USA ) . Spots ( individual vRNAs ) were counted using the StarSearch algorithm http://rajlab . seas . upenn . edu/StarSearch/launch . html . Images of coverslip immobilized virions were analysed by ImageJ in combination with the ComDet plugin https://github . com/ekatrukha/ComDet/wiki . | The bunyavirus family is one of the largest virus families on Earth , of which several members cause severe disease in humans , animals or plants . Little is known about the mechanisms that facilitate the production of infectious bunyavirus virions , which should contain at least one copy of the small ( S ) , medium ( M ) and large ( L ) genome segment . In this study , we investigated the genome packaging process of the Rift Valley fever virus ( RVFV ) by visualizing individual genome segments inside infected cells and virions . Experiments performed with wild-type virus , two- and four-segmented variants , and a variant with a codon-shuffled M segment showed that the production of infectious virions is a non-selective process and is unlikely to involve the formation of a supramolecular viral RNA complex . These observations have broad implications for understanding the bunyavirus replication cycle and may facilitate the development of new vaccines and the identification of novel antiviral targets . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"vero",
"cells",
"medicine",
"and",
"health",
"sciences",
"rift",
"valley",
"fever",
"virus",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"biological",
"cultures",
"microbiology",
"viral",
"structure",
"viruses",
"rna",
"viruses",
"bunyaviruses",
"microbial",
"genomics",
"research",
"and",
"analysis",
"methods",
"viral",
"genomics",
"genome",
"complexity",
"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"viral",
"replication",
"cell",
"lines",
"virions",
"biochemistry",
"virology",
"viral",
"pathogens",
"genetics",
"ribonucleoproteins",
"biology",
"and",
"life",
"sciences",
"genomics",
"computational",
"biology",
"organisms"
] | 2016 | Single-Molecule FISH Reveals Non-selective Packaging of Rift Valley Fever Virus Genome Segments |
Broad-scale geographic gradients in species richness have now been extensively documented , but their historical underpinning is still not well understood . While the importance of productivity , temperature , and a scale dependence of the determinants of diversity is broadly acknowledged , we argue here that limitation to a single analysis scale and data pseudo-replication have impeded an integrated evolutionary and ecological understanding of diversity gradients . We develop and apply a hierarchical analysis framework for global diversity gradients that incorporates an explicit accounting of past environmental variation and provides an appropriate measurement of richness . Due to environmental niche conservatism , organisms generally reside in climatically defined bioregions , or “evolutionary arenas , ” characterized by in situ speciation and extinction . These bioregions differ in age and their total productivity and have varied over time in area and energy available for diversification . We show that , consistently across the four major terrestrial vertebrate groups , current-day species richness of the world's main 32 bioregions is best explained by a model that integrates area and productivity over geological time together with temperature . Adding finer scale variation in energy availability as an ecological predictor of within-bioregional patterns of richness explains much of the remaining global variation in richness at the 110 km grain . These results highlight the separate evolutionary and ecological effects of energy availability and provide a first conceptual and empirical integration of the key drivers of broad-scale richness gradients . Avoiding the pseudo-replication that hampers the evolutionary interpretation of non-hierarchical macroecological analyses , our findings integrate evolutionary and ecological mechanisms at their most relevant scales and offer a new synthesis regarding global diversity gradients .
The uneven distribution of species diversity is a key feature of life on Earth and has myriad implications . While the scale-dependence of the determinants of the global variation in diversity is well acknowledged [1]–[6] , to date a quantitative accounting of the roles of history and environment in generating and maintaining gradients in species richness is still lacking . Over the past three decades , increased data availability has facilitated analyses of contiguous geographic patterns in species richness at relatively fine spatial grains ( 100–200 km ) at both continental [7]–[9] and global scales [10] , [11] . At these spatial resolutions , environmental variables such as productivity or temperature have been shown to offer extremely strong statistical predictions of species richness [8] , [11]–[18] . However , it has been difficult to connect these results directly with underlying evolutionary and ecological processes . One problem is that the ultimate drivers underpinning diversity , namely speciation and extinction [19] , operate at scales much larger than the spatial resolution ( e . g . , 100 km grids ) of most analyses . A number of studies have confirmed the strong effect of regional richness on local richness [1]–[3] , [6] , [20] and have speculated on the role of energy driving diversification at regional scales [21]–[24] as well as sorting at local scales [25]–[27] . But attempts to integrate them at the appropriate scale have been limited , and we know of no study that has quantified the effect of productivity on richness gradients jointly at regional and local scales and both in terms of evolutionary and ecological processes . Another impediment to interpretations of gridded richness analyses has been that species' geographic ranges are generally much larger than , for example , 100 km×100 km grid cells , resulting in geographically non-random patterns of pseudo-replication , inflated spatial autocorrelation , and an overrepresentation of wide-ranging species and their respective climatic associations [8] , [28] . These issues have to date precluded straightforward evolutionary and ecological interpretations of macroecological environment correlations of gridded richness patterns [5] , [29] . While partly motivated by limits in the knowledge of fine-scale species distributions [30] , macroecological analyses have also been conducted using , for example , ca . 800 ecoregions as spatial units [14] , [31] , but these regions still incur significant and geographically variable redundancy in species . We are not aware of a study on richness gradients that has successfully overcome this problem and thus truly have given each species equal weight . Finally , while there is little doubt about the importance of time for diversification [32]–[34] , attempts to date to invoke paleoclimate for understanding richness have been hampered by the lack of data , especially at deeper time scales . Several studies have linked relatively recent climatic oscillations , for example , those causing quarternary high-latitude glaciation , to geographic richness patterns [22] , [35]–[37] . The geography of deeper time climate conditions and exactly how it relates to the tempo of past clade diversification is inherently difficult to estimate . But given deep conservatism in the environmental ( e . g . , biome ) associations within clades [38] , [39] compared to relatively dynamic geographic ranges [40] , clades are expected to much more strongly track climatically defined regions , or biomes , rather than specific geographical locations over evolutionary history . The ages of biomes may thus offer a promising avenue for understanding the role of paleoclimate contributing to contemporary patterns of species richness and have recently been successfully correlated with both turtle and tree richness at the regional scale [41] , [42] . To date , analyses connecting the age and area of regions to finer grain richness patterns have not been attempted . Here , we aim to address these problems with a hierarchical framework that integrates the drivers of regional diversification of species with those of their sorting into finer grain assemblages at their respective scales of influence . We use this model to test the relative importance of past spatio-temporal variation of climatic conditions ( specifically time-integrated area and productivity ) versus contemporary environment for explaining both the regional and finer scale variation in the species richness of terrestrial vertebrates worldwide . Due to environmental niche conservatism , organisms are generally restricted to climatically defined bioregions , or “evolutionary arenas , ” characterized by in situ speciation and extinction . We expect differences in species richness between such regions to arise from different levels of net diversification ( speciation – extinction over time ) . The number of speciation and extinction events should vary among regions due to differences in the sizes of populations over time and the opportunities for reproductive isolation for all resident taxa [32] . We expect these drivers to be associated with today's area [29] , [32] , [43] and energy availability ( i . e . , productivity ) [8] , [11]–[14] of bioregions but , critically , also with the past levels of these factors—that is , how bioregions have varied in areal extent and productivity over time [42] . Furthermore , regional rates of diversification have been hypothesized to vary with temperature and its effects on activity and biological rates such as rates of molecular evolution or species interactions [15]–[17] , [44] . We expect all of these drivers in concert to shape broad-scale gradients of diversity and predict that in an integrative assessment of regional differences in diversity , ( i ) models accounting for the temporal availability of area or productivity will outperform those without ( i . e . , regions that are older and/or have in the past been larger in extent will support higher vertebrate species richness than younger and/or smaller regions ) , ( ii ) area times energy availability ( net primary productivity ) will be a stronger predictor of richness than area alone [13] , [45] , and ( iii ) average bioregion temperature will positively affect richness above and beyond the effects of productivity and have a stronger effect in ectotherms compared to endotherms [15] . We test these predictions for the 32 main subdivisions , or “bioregions , ” of the world based on vegetation type and major landmass ( Figure 1 , Tables S1 and S2 ) [46] , [47] . We excluded montane regions ( which exclusively harbor ca . 5% of vertebrate species and represent ca . 15% of global land area ) due to their extremely steep environmental gradients and associated species turnover , which impedes reliable bioregional delineation and estimates of their extent over time . Over historical time-scales these climatically and geographically distinct bioregions have been characterized by similar environmental and climatic conditions , but have changed in size and shape over time within their respective realms [42] . All bioregions are within the range of scales over which allopatric speciation of terrestrial vertebrate speciation typically occurs ( 100–1 , 000 km scale [48] ) and may thus be considered bio-climatically and geographically distinct “evolutionary arenas . ” After deriving time-integrated models of bioregion species richness , we then in a second step assess their ability to predict the variation in richness at the scale of 110 km grid cell assemblages . We make these finer scale predictions first under a model of simple random sorting of species from those predicted for the bioregion and , second , under a model of sorting mediated by the relative productivity of a grid cell . The goals of this second step include ( i ) an evaluation of the ability of this hierarchical model to make strong fine-scale richness predictions ( while including paleoclimate and avoiding regional-level conflation of sample size ) and ( ii ) a demonstration of the separate roles energy availability has at different temporal and spatial scales .
Paleoclimatic data reveal dramatic variation in the age and spatial dynamics of different bioregions from the end of the Paleocene ( 55 MY bp ) to the present day ( Figure 1 , Table S5 ) . For example , grasslands are not thought to have covered large areas on earth until 8 million years ago , resulting in a much smaller area over time than observed for , for example , temperate or tropical moist forests that have a longer history ( Figure 1 ) . Linking estimates of the extents of bioregions over time allows the calculation of “time-integrated area” ( TimeArea ) [42] , a synthetic index of area available to the bioregion's biota over time , varying from just 48×104 km2 integrated over 55 million years in the case of the Mediterranean bioregion at the southern tip of Africa to over 100 , 000×104 km2 in Eurasian temperate and African moist tropical forests . Unlike bioregion extent and position , climatic conditions of bioregions are assumed to be relatively static over time [49] , which allows the determination of average bioregion net primary productivity ( Productivity ) and Temperature . Summed Productivity over bioregion Area yields total bioregion productivity ( AreaProductivity ) —that is , total annual carbon flux measured in kg/year over a whole bioregion , a measure that exhibits joint dynamics with bioregion Area . But integrated over time in the form of TimeAreaProductivity , it exhibits very different geographic patterns than TimeArea ( Figure 1 ) with , for example , African and IndoMalay tropical moist forests experiencing a flux of over 8 , 000×1017 kg of carbon over the past 55 million years and the Mediterranean regions of the New World and Africa just under 3×1017 kg . We summarized terrestrial vertebrate richness per bioregion as Total ( every species found in a bioregion ) , Resident ( species for which a given bioregion contains the largest portion of the range ) , and Endemic ( species that are restricted to a single bioregion; Table S4 ) . We find minimal overlap in Total species among bioregions ( median Jaccard similarity among bioregions: 4% for birds , 0% for other taxa; Figure S1 , Table S3 ) , which confirms their relative evolutionary isolation in addition to climatic and spatial independence and a consistently strong pattern of biome conservatism [38] , [50] , [51] . It also confirms that across all four vertebrate groups these selected bioregions represent useful spatial units that avoid the pseudo-replication of species: for Resident species richness every species enters a given analysis exactly once , and the number of distribution records is equal to the global richness of species ( 13 , 860 endothermic mammals and birds , 11 , 836 ectothermic amphibians and reptiles; montane endemics excluded ) . For Endemic species ( total of 13 , 111 species and records ) bioregions are even more likely to represent the true regions of origin compared to Resident species . We therefore expect a stronger correlation of area and productivity integrated over time ( TimeAreaProductivity ) with the diversity of Endemic species . All three predictions of our integrative model regarding the effect of time-area-productivity on richness are confirmed ( Table 1 ) . The models that account for time-integrated productivity and also include temperature as an additional predictor yield the strongest fits . For endotherms , the time-integrated measures of area outperform models that ignore time only for the Endemic richness dataset , which offered the more direct test of our hypotheses . Predictions of the two-predictor TimeAreaProductivity+Temperature model are consistently strong across all four vertebrate taxa , which represent independent replicates ( Figure 2 ) , explaining over 77% of the variation in richness ( Figure 2 , N = 128 , see Tables S7 and S8 for more details ) . Models that fit TimeArea and Productivity as statistically separate terms do not on the whole yield stronger predictions ( Table S9 ) . This lends support to Wright's [45] parallel findings for large islands , which represent similarly closed systems , and contrasts with previous results reported for 110×110 km grid cells [13] . The shape of the Productivity-richness relationship is linear ( in Endotherm Residents ) or positive accelerating in linear space ( in Ectotherm Residents and both Endemics groups ) . In contrast , the slopes of the AreaProductivity- and TimeAreaProductivity-richness relationships , whether fitted with or without Temperature , are all positive saturating—that is , species richness tends to increase more steeply in the low than in the high productivity ranges ( coefficients in ln-ln space vary between 0 . 4 and 1 , Table S7 ) . We did not find evidence of a hump-shaped pattern for any measure of productivity and richness at the bioregional scale [52] , [53] . As predicted , ectotherm richness increases much more steeply and strongly with temperature than endotherm richness , both when fitted singly and when controlled for TimeAreaProductivity ( Figures S2 and S3 , Table S7 ) . This supports at the global scale the significant and complementary effect temperature may contribute to levels of regional ectotherm diversity ( see also [4] , [14] , [44] ) . For ectotherms , higher temperatures in tropical regions may be promoting higher rates of genetic incompatibilities among populations or faster rates of biotic interactions , further accelerating speciation rates [44] , [54] , [55] . Alternatively , the thermal dependence of activity represents a strong constraint on ectotherm distribution [56] , likely imposing limits on clade origination and diversification in high-latitude regions . Third , in warm regions , ectotherms are released from physiological and behavioral adaptations to cold stress promoting a greater diversity of life histories and metabolic “niches” [57] , [58] . These factors are not mutually exclusive , and more work is needed for understanding the potential role of temperature and thermal physiology in driving diversification . Preliminary results from phylogenetic analyses suggest increased diversification rates at lower latitudes in both amphibians [59] and mammals [60] , but with a much weaker and more equivocal trend in the latter . Overall , our bioregion results support the hypothesized interactions of environmental conditions and area over time in influencing the speciation and extinction and ultimately species richness of biota in bioregions . We suggest that the bioregional variation in time-integrated productivity successfully captures key factors affecting both cumulative population sizes over time as well as the different opportunities for reproductive isolation . Large , productive areas like the Neotropical moist/wet forest biome have been characterized by high productivity and a continuously large extent , and thus have supported large populations of each of the four vertebrate clades , since before the Eocene ( 700×1012 km2 years and 663×1018 kg Carbon produced since 55 MY bp; Figure 1 ) . Reproductive isolation has been facilitated by the large amount of time that vertebrate populations have had to encounter geographical barriers ( such as rivers in non-volant mammals [61] ) as well as heightened habitat heterogeneity related to the high productivity ( i . e . , multiple vertical forest strata ) [12] . This contrasts with , for example , unproductive North American deserts , which have only come to cover a substantial area within the last few million years ( 12×1012 km years and 3×1018 kg Carbon; Figure 1 ) [62] . We suggest that the large TimeAreaProductivity seen in , for example , the Neotropical forest compared to the North American desert bioregion in Figure 1 reflects all factors affecting cumulative population sizes over time ( which have affected both speciation and extinction probabilities ) as well as opportunities for reproductive isolation . Together , these factors have led to the wide discrepancy in vertebrate diversity between these two bioregions . Previous studies have employed phylogenies or sister-group comparisons to test whether the latitudinal diversity gradient derives from more evolutionary time [63] , niche conservatism [38] , or differences in speciation or extinction rates at different latitudes [22] , [59] , [60] , [64] . Factors such as orbital forcing causing glaciation at high latitudes have been posited to elevate extinction rates and are expected to accentuate the observed disparities in species richness among bioregions , especially for endemics [35] , [65] . The results reported here complement these studies and suggest that at the bioregion scale , and over an extremely large window of time ( 55 MY ) , diversification rates consistently vary with respect to the area , age , and productivity of a given bioregion ( Figure 2 ) . We thus view the time-integrated productivity of bioregions to be a general explanation for why so many clades originate at lower latitudes and correspondingly fewer have diversified into bioregions at higher latitudes . It is important to note that time alone is not sufficient to explain these patterns: temperate bioregions are just as ancient as tropical bioregions but strongly differ in their cumulative time-integrated area and productivity . In sum , the strong associations we find indicate a pathway toward first-order approximations of rates of net species production per bioregion , based on variation in area over time , productivity , and temperature . Future studies could integrate our approach with more detailed comparisons of clade-level diversification rates among bioregions or combine it with existing phylogenetic methods for quantifying correlates of diversification . Having addressed key evolutionary drivers affecting the broad-scale variation in vertebrate diversity , we next assess how each bioregion's species sort into grid cell assemblages and how both processes combine to explain the finer scale geographic variation in richness ( Figure 3A ) . We perform this assessment for the 18 , 467 bird , mammal , and amphibian species in the bioregion analysis and their 2 , 966 , 137 occurrences across the 9 , 253 110×110 km terrestrial grid cells encompassed by the bioregions ( Figure 3B ) . Strong effects of regional- on fine-scale richness have previously been demonstrated [1] , [2] , and here we provide a first test of their pervasiveness at a global scale by evaluating the performance of bioregion models for explaining grid cell richness . We find that the two-predictor TimeAreaProductivity+Temperature model developed above ( Table 1 ) alone explains 46%–60% and 32%–50% of the variation in Resident richness and Total richness , respectively ( Figure 3B left column , Tables S11 and S12 ) . This highlights how regional effects together with even simple null models of proportional sorting are able to explain much of the finer scale richness patterns . Fine-scale–regional richness relationships are known to be affected by spatial scale as well as by species' dispersal abilities [66] . In larger regions a grid cell of the same size represents a smaller portion of the regional area and , assuming similar levels of grid cell immigration/extinction , grid cell richness is expected to be smaller . This should apply whenever average species range sizes increase less than proportionally with bioregion size and should be particularly noticeable for taxa with relatively low dispersal rates or small within-bioregion range sizes ( such as amphibians compared to birds or mammals ) , because with increasing bioregion size species will be progressively less likely to occupy a given grid cell . We find these expectations confirmed . Bioregion Area exhibits an additional negative effect and improves fine-scale predictions , especially for Total richness . It does so most strongly in Amphibians ( Figure S4 , Table S12 ) , whose greater dispersal limitation ( and on average by a factor of four smaller geographic ranges ) compared to mammals or birds has been previously suggested as contributing to their strong patterns of species turnover [67] . Species vary strongly in the number of assemblages they occupy and the species richness of grid cell assemblages is a function of the drivers that affect species' sorting and resulting overlap in geographic ranges . One variable strongly associated with the sorting into assemblages , particularly by wide-ranging species , is local energy availability [8] , [25] . We find that relative productivity in a grid cell ( CellPropProductivity , i . e . , the proportion of the maximum grid cell productivity observed in a bioregion ) predicts a substantial additional amount of observed variation in grid cell richness ( Figure 3B middle column , and S13 ) and confirms the expected greater tendency of species within a bioregion to occupy high-productivity grid cells . Allowing the shape of the richness–productivity relationship to vary among regions improves predictions ( Tables S12 and S13 ) , but only slightly so , suggesting a within-regional role of productivity that is globally fairly consistent . Nevertheless , the total amount of variation explained by the TimeAreaProductivity+Temperature model ( 58%–77% ) is remarkable and similar to that found in previously published broad-scale gridded richness regression analyses [8] , [11] . Notably , however , the hierarchical approach avoids the dual problems of species pseudo-replication and conflation of among- and within-regional processes—issues that have seriously impeded interpretations of all previous gridded biogeographic or macroecological analyses at broad scales . Our results largely corroborate past studies that have hypothesized that net primary productivity should be a dominant predictor of fine-grain assemblage richness [8] , [11] , [16] . However , our hierarchical model is able to separate how productivity influences species richness at different temporal and spatial scales . At the bioregional scale , productivity should increase the cumulative population size and opportunities for reproductive isolation over time , promoting higher species richness in high-productivity bioregions [12] . At the fine scale productivity affects the occupancy of assemblages in relation to the regional pool [27] , [68] . In addition to the sampling effects inherent with larger assemblage-level population sizes , increased productivity may promote greater richness due to an increased number of niches facilitating species coexistence [12] , [25] . We consider the contributions of this study to be conceptual in addition to empirical and hope that its framework will inspire further consideration of diversity gradients that aims to integrate ecological and evolutionary mechanisms across scales . Our global hierarchical approach represents an analytical paradigm shift away from the traditional analysis of fine-scale assemblages as independent spatial units . But there are obvious limits to our analysis . While the strong association of vertebrates with dominant vegetation types and the observed biotic independence of bioregions support their delineation as major evolutionary arenas , challenges remain surrounding the demarcation of the exact boundaries of such regions , the accuracy of past climate reconstructions , and their comparability across clades . Future availability of higher resolution phylogenies of the four vertebrate clades will allow more rigorous comparative approaches within and across lineages , but even comprehensive , strongly supported phylogenetic reconstructions are unlikely to provide vital information regarding the estimation of ancestral distributions ( or ranges ) and extinction rates [69] . Thus , our model can be viewed as a template on top of which other processes surely influence the origin and maintenance of diversity . For example , glaciation cycles influence speciation and extinction rates [36] and play an important role in driving recent speciation over broad scales [70] . Historical climate dynamics along elevation gradients in particular are known to create opportunities for rapid climate-associated parapatric or allopatric speciation and contribute strongly to the high richness of many tropical mountain areas [71]–[73] . Furthermore , a multitude of trophic interactions are likely to interact with these large-scale processes to cause positive , coevolutionary feedback loops , thus further increasing fine-scale and regional diversity [15] . Our findings show that energy availability has a large effect on both the regional pool and local sorting of richness . This highlights its importance for both evolutionary and ecological processes and the critical need to integrate these effects . This is especially crucial today , given the attention paid to recent models predicting the effects of climate change on the richness of whole gridded assemblages . The redundancy of information and conflation of ecological and evolutionary processes in smaller scale models impede interpretation in a way that is overcome in our analysis . Here we have shown how history can be integrated into a model predicting diversity with area , productivity , and temperature at the global scale . The separate consideration of drivers of diversification and finer scale occupancy and their joint effects on observed gradients of species richness should help pave the way for a more integrated macro-evolutionary and -ecological understanding of the origin and maintenance of global richness gradients .
We selected 32 well-established , geographically and climatically distinct bioregions ( Figure 1 ) . These bioregions correspond to the biomes ( tundra , desert , grassland , boreal forest , temperate forest , tropical moist/wet forest , tropical dry forest/savanna , and Mediterranean forest/shrublands ) within the world's main biogeographic realms ( Neartic , Paleartic , Neotropical , Australian , IndoMalayan , and Afrotropics ) as described by Olson et al . [46] and also used in the Wildfinder vertebrate distribution database ( see below ) [74] . Although we do not have detailed , fine-scale records throughout every interval of time for the past 55 million years , enough information exists regarding the age of all biomes and directionality of their expansion and contraction to make reasonable estimates of the measures of their area integrated over time ( Table S5 ) . We excluded the “Mangroves” biome ( Biome ID 14 in [46] , [74] ) and also the “Montane Grasslands & Shrublands” Biome ( Biome ID 10 in [46] , [74] ) . The latter was not included due to the difficulty in estimating areal and climate changes over their steep gradients over such a long time period . For example , in the Andes , different biomes occur at different elevations on the western and eastern slopes at different latitudes , and the available data are not sufficient to accurately estimate the elevations of the southern , central , and northern Andes at various time intervals since the Miocene , as each chain has uplifted at different rates and at different times [75] . This is critical information to be able to reconstruct the areal extent over time of each bioregion in the Andes and a general problem common to all of the world's mountain ranges , which is why they were excluded from our analysis . The last 55 million years is an appropriate interval of time to measure the time-integrated area of the world's biomes within realms for two reasons . First , the beginning of the window of time is 10 million years after the massive extinction , which occurred 65 million years ago , causing major upheaval in the vertebrates . By 55 million years ago , the biosphere had recovered but its biota was very different from the plants and animals that had dominated the Cretaceous . Second , most of the “higher taxa”—that is , ancestors of modern lineages of vertebrates that now dominate the extant diversity of mammals , birds , amphibians , and reptiles ( for example , fossils recognizable as extant genera ) —are already represented in the fossil record by 55 million years ago [76] . Plant communities by the Eocene are , for the first time , composed of Angiosperms and Gymnosperms that are recognizable as the “genera” and “families” that are dominant in today's biomes [62] , [77] . Thus , the biota in the Eocene has a “modern aspect” [76] , [77] . The Earth's biomes have experienced large changes over the last 55 million years due to the consistent pattern of cooling and drying that has steadily taken place over this period of time [62] , [78] , [79] . Average global temperatures have plummeted from 27°C 55 million years ago to today's average of 15°C and precipitation has similarly dropped [62] , [80] . For the moist/wet forest biomes ( boreal forest , temperate forest , and tropical moist/wet forest ) we used maps generated by Fine and Ree ( 2006 ) that were based on five sources: [49] , [62] , [81]–[83] . For the other biomes , our approach to estimate the time-integrated area of each biome was first to try to determine the paleobotanical consensus opinion for the age of each biome ( Table S5 ) . Then , we took the extant area of that biome and backcasted in time over the years that it has been present , reasoning that as tropical forests have receded during the past 55 MY years , dry and cold biomes such as tundra , desert , Mediterranean , grassland , and dry forest/savanna must have increased in size from the date of their origin to today's area . We made two interpretations—a “wet” and a “dry” interpretation ( Table S5 ) . These two interpretations span the diverse opinions regarding the extent and age of the world's biomes over the last 55 million years and thus gauge the robustness of our results according to a range of expert opinions . For example , desert plants are absent in fossil records until about 2 Ma [77] , even though it is hypothesized with molecular dating that plant lineages today found only in desert floras are at least 50 Ma old [62] . Thus , the consensus opinion is that deserts were probably present in the Eocene , but much restricted in size compared to today . For example , evaporite sediments point to extreme aridity in western Africa , Arabia , and central Asia in the late Miocene [82] . We thus made two estimates for the time-integrated area of desert biomes . The “wet” interpretation gives deserts an origin of 34 MYA but covering 10% of their current area from 34 MYA until 2 MYA , which is consistent with the lack of fossil evidence for any desert plant communities . The “dry” interpretation also gives the origin of deserts 34 MYA but has deserts covering the same areal extent as today since their origin , which is almost certainly an overestimate but is possible given the ancient age of some desert plant lineages and the difficulty of fossilization of desert environments ( Table S5 ) . Our wet and dry interpretations both yield qualitatively similar results , and for simplicity , we focus on the “wet” interpretation throughout the article . The current-day extent of a bioregion as given in [46] yielded our predictor variable Area ( units km2 ) . Time-integrated area ( TimeArea , in units year km2 ) was given as the integrated areal extent of a bioregion over 55 million years , or simply the sum of the area estimated for each of the 55 one-million-year periods . We acknowledge that this offers only a first order approximation . While exact values will be subject to change as paleoecological knowledge advances , we expect these changes to refine the details rather than radically alter overall patterns , which would have relatively little effect on our analyses , and thus we do not expect systematic biases in our results . While topographic heterogeneity is expected to also influence the potential for reproductive isolation [32] , in this dataset ( which excludes montane regions ) it is largely captured by bioregion Area and does not yield improved predictions ( see Table S12 ) . We aggregated existing eco-regional terrestrial vertebrate species lists for the selected 32 bioregions from the Wildfinder distribution database [74] . We excluded all eco-regions in biomes not selected for analysis ( see above ) , including all montane eco-regions ( which have a total of 1 , 015 terrestrial vertebrate species restricted to them ) . This resulted in 54 , 122 bioregion occurrence records for 25 , 696 species ( 9 , 229 birds , 4 , 607 amphibians , 4 , 631 mammals , and 7 , 229 reptiles ) . We calculated terrestrial vertebrate richness ( “vertebrate richness” ) per bioregion in three different ways: Total , which includes every vertebrate species found within each bioregion; Resident , which only counts species in the bioregion with the largest proportion of its geographic range; and Endemic , which counts only species that are restricted to a single bioregion ( see Table S2 for complete raw data ) . Assigning each species only to its dominant bioregion to eliminate pseudo-replication yields a Resident richness pattern very similar to that of Total richness ( rS = 0 . 85 , Table S4 ) . For the analyses , vertebrates were divided into ectotherms ( amphibians and reptiles ) and endotherms ( birds and mammals ) and further separated into birds , mammals , reptiles , and amphibians . All richness values were natural log-transformed . Species occurrence data across grid cells were compiled from global expert opinion range maps extracted across a 110×110 km equal area grid in a Behrman projection . For mammals [84] , and amphibians , sources were the IUCN assessment ( http://www . iucnredlist . org ) . For birds , breeding distributions were compiled from the best available sources for a given broad geographical region or taxonomic group [85] . For reptiles , global-scale expert range maps have not yet been compiled , and they were therefore not included in the grid cell assemblage analyses . We excluded all cells that were not >50% inside the selected bioregion boundaries as described above ( and shown in Figure 1 ) . Only cells with >50% dry land and with at least one species from each of the three vertebrate groups were included in the analysis , resulting in 9 , 253 cells . For each grid cell we summarized richness of Resident species ( i . e . , species were counted if they occurred in several grid cells only within the same bioregion ) and of Total species ( i . e . , species were counted whether they occurred in multiple grid cells within the same or in a different bioregion ) . Values were log10-transformed before analysis . For Total species , the full database consisted of a total of 2 , 966 , 137 grid cell records ( birds 2 , 010 , 091; mammals 695 , 133; and amphibians 260 , 913 ) . Bioregion-typical temperature estimates ( Temperature ) were based on average annual temperatures calculated from the University of East Anglia's Climatic Research Unit gridded climatology 1961–1990 dataset at native 10-min resolution [86] . For estimates of bioregion-typical annual net primary productivity , we used an average from 17 global models at a spatial resolution of 0 . 5 degrees latitude-longitude [87] . Average bioregion productivity ( Productivity , units grams Carbon m−2 year−1 ) was calculated from all 0 . 5×0 . 5 degree grid cells that predominantly fall inside a bioregion , and summed productivity ( AreaProductivity , units grams Carbon year−1 ) was then given by the product of this value and bioregion Area . With bioregions defined by their typical environmental conditions , we assumed average productivity characteristic of a bioregion to have been constant through time [49] , [62] . Time-integrated productivity ( TimeAreaProductivity , unit grams Carbon ) was thus given as the product of Productivity and TimeArea . Values for all bioregion predictor variables are given in Table S1 . All response and predictor variables were natural log-transformed for analysis , except for temperature , which was 1/kT transformed ( where k is the Boltzmann constant , see [44] ) . We used the same global net primary productivity dataset [87] to estimate productivity at the level of 110×110 km grid cells . First , we calculated average grid cell productivity ( NPP ) across all encompassing 0 . 5×0 . 5 degree grid cells . Second , we normalized each grid cell by dividing by the maximum productivity grid cell value observed in a bioregion , resulting in a measure of proportional productivity ( PropNPP ) varying from 0 to 1 . We performed a total of nine GLM models on the bioregion data and used the Akaike criterion to identify those offering the best fit [88] . Six models were given in the form of single predictors ( Temperature , Area , Productivity , AreaProductivity , TimeArea , and TimeAreaProductivity ) . An additional three models were formed by the combination of the latter three variables with Temperature . We performed a separate set of analyses to assess the potential additional effect of elevation range within a bioregion , but adding this variable to any of the three two-predictor models did not improve model fit , and thus we excluded the variable from further consideration . Because of the strong independence of sampling units both in terms of response ( no overlap in species ) and predictor variables ( by definition each bioregion is environmentally highly distinct from neighboring bioregions ) , the usual concerns about spatial autocorrelation affecting model results [89] , [90] do not apply to this analysis , and additional spatial regression analysis was not performed . Having established models of bioregion richness , we assessed the success of predictions of resident bioregional richness to explain the species richness ( Total and Resident , see above ) of all 110×110 km grid cells within bioregions ( for a conceptual overview of the analytical steps , see Figure 3 ) . Note that unlike the bioregional tests described above , analyses at this scale do double-count species . In our study we make the simplifying assumption that diversification processes are sufficiently accounted for at the bioregional scale . The models at the within-bioregion scale then address the sorting of these species each into multiple grid cells , with multiple occurrences an integral part of the signal . We acknowledge that , depending on taxon and region , diversification processes may still exert influence on the within-bioregion patterns of distribution and richness , and we hope that our work will spur further research into additional approaches that can be integrated across all scales . We first evaluated bioregion predicted resident richness alone ( in essence testing for a random sorting of bioregion species into finer scale assemblages ) , then included bioregion Area as an additional predictor , and finally we added estimates of grid cell NPP as a finer scale predictor . We first performed simple GLM models with all 9 , 253 grid cells as sampling units , together with bioregion Resident richness as predicted by the TimeAreaProductivity+Temperature and AreaProductivity+Temperature models as a first predictor ( BioregPred ) and bioregion Area as a second predictor ( Figure 3 , Table S9 ) . In the same GLM we then added grid cell proportional net primary productivity ( CellPropNPP , i . e . , relative productivity within a bioregion , see above ) as an additional predictor . In preliminary post hoc analyses with a number of environmental variables CellPropNPP remained by far the strongest , in line with recent work on within-regional richness filters that also find productivity-related variables to be dominant [26] , [27] . Given the nested nature of these analyses we focus on pseudo-r2 values ( fit of observed versus predicted ) and visual examination of results in the form of partial residual plots ( Figure 3 ) . For this first demonstration , focused on a single variable , we did not include further analyses additionally fitting the signal of spatial autocorrelation . We performed a second set of analyses in an explicit mixed effects model setting ( Table S10 ) , with bioregion as a random effect ( R library lme4 , Version 0 . 999375-32 , function lmer ) . As in the GLM model , grid cell richness is first fitted by the predictions for regional resident species richness ( BioregPred , see Table 1 ) , and then by area of the region ( Area ) , and grid-cell-level NPP ( NPP ) . Region was fitted as a random effect , and the slope and strength of BioregPred and BioregPred+Area as fixed effects were assessed ( model formula in R: lmer ( y∼BioregPred+Area+ ( 1|Bioregion ) ) . The additional effect of grid cell NPP was then evaluated by fitting it as an additional fixed effect with a globally constant slope ( NPPconst ) and by allowing the NPP–richness relationship to vary within regions as random slope ( NPPvar ) ( model formula in R: lmer ( y∼BioregPred+Area+ ( 1|Bioregion ) + ( NPP|Bioregion ) ) . The data are deposited in the Dryad Repository ( http://dx . doi . org/10 . 5061/dryad . 45672js4 ) . | Understanding what determines the distribution of biodiversity across the planet remains one of the critical challenges in biology and has gained particular urgency in the face of environmental change and accelerating species extinctions . Our study develops a novel analytical framework to jointly evaluate historical and contemporary environmental predictors of the latitudinal gradient in the diversity of terrestrial vertebrates . The number of vertebrate species is greater in warm , productive biomes , such as tropical forests , that have both a large size and a long evolutionary history . Using just a few key predictor variables—time , area , productivity , and temperature—we are now able to explain more than 80% of the variability in biodiversity among bioregions . By integrating each of these factors at both the regional and local scale in a hierarchical model , we are able to provide a consensus explanation for broad-scale diversity gradients that encompasses both ecological and evolutionary mechanisms . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"ecology",
"biology"
] | 2012 | Global Gradients in Vertebrate Diversity Predicted by Historical Area-Productivity Dynamics and Contemporary Environment |
The Unfolded Protein Response of the endoplasmic reticulum ( UPRER ) controls proteostasis by adjusting the protein folding capacity of the ER to environmental and cell-intrinsic conditions . In metazoans , loss of proteostasis results in degenerative and proliferative diseases and cancers . The cellular and molecular mechanisms causing these phenotypes remain poorly understood . Here we show that the UPRER is a critical regulator of intestinal stem cell ( ISC ) quiescence in Drosophila melanogaster . We find that ISCs require activation of the UPRER for regenerative responses , but that a tissue-wide increase in ER stress triggers ISC hyperproliferation and epithelial dysplasia in aging animals . These effects are mediated by ISC-specific redox signaling through Jun-N-terminal Kinase ( JNK ) and the transcription factor CncC . Our results identify a signaling network of proteostatic and oxidative stress responses that regulates ISC function and regenerative homeostasis in the intestinal epithelium .
Long-term homeostasis of high-turnover tissues relies on the precise regulation of stem cell ( SC ) activity that allows tailoring regenerative responses to the needs of the tissue . Regenerative processes in barrier epithelia , such as the intestinal epithelium , are particularly vulnerable to exogenous insults . Understanding how cellular stress responses of intestinal epithelial cells ( IECs ) and intestinal stem cells ( ISCs ) coordinate and maintain regenerative processes in the gut will provide insight into the etiology of pathologies ranging from inflammatory bowel diseases ( IBDs ) to colorectal cancers . The unfolded protein response of the ER ( UPRER ) plays a central role in the control of homeostasis of the intestinal epithelium . Loss of protein folding capacity in the ER of IECs results in complex cell-autonomous and non-autonomous activation of stress signaling pathways , triggering an inflammatory condition that severely perturbs proliferative homeostasis , innate immune function and cell survival in the epithelium , and has been implicated in IBDs [1]–[7] . The UPRER is triggered by the accumulation of misfolded proteins in the ER [8] , which activate three highly conserved UPRER sensors: the PKR-like ER kinase PERK , the transcription factor ATF6 , and the endoribonuclease IRE1 ( Figure 1B ) . These sensors make up the three branches of UPRER signaling , which consists of IRE1-mediated splicing of the mRNA encoding the bZip transcription factor X-Box binding protein 1 ( Xbp1 ) , phosphorylation of the translation initiation factor 2 alpha ( eIF2α ) by PERK , and cleavage and activation of ATF6 , resulting in its nuclear translocation and activation of stress response genes , including Xbp1 [1]–[7] , [9] . Xbp1 regulates transcription of ER components , and the resulting transcriptional induction of ER chaperones and of genes encoding ER components enhances ER folding capacity , and the reduction in protein synthesis ( by eIF2α ) alleviates the protein load in the ER . Furthermore , factors required to degrade un/misfolded proteins through ER-associated degradation ( ERAD ) are induced [8] , [10]–[12] . The accumulation of un/misfolded proteins in the ER is further associated with increased production of reactive oxygen species ( ROS ) , most likely due to the production of hydrogen peroxide as a byproduct of protein disulfide bond formation by protein disulfide isomerase ( PDI ) and ER oxidoreductin 1 ( Ero1 ) [13]–[15] . Recent studies suggest that the UPRER may influence regenerative processes in the gut directly , as it is engaged in cells transitioning from a stem-like state into the transit amplifying state in the small intestine of mice [16] . Regeneration is also influenced by the intracellular redox state of stem cells , and changes in intracellular ROS production play an important role in the regulation of SC pluripotency , proliferative activity , and differentiation [17]–[20] . Coordinated control of cellular protein and redox homeostasis by the UPRER and other stress signaling pathways is therefore critical to maintain SC function . Exogenous ER stress likely disrupts this coordination , perturbing regeneration and proliferative homeostasis . Consistent with this model , excessive UPRER activity has been implicated in tumorigenesis [2] , [21] . To understand the long-term maintenance of epithelial homeostasis in the intestine , detailed insight into the regulation and function of the UPRER and its coordination with the redox response in the intestinal epithelium , in a cell-type specific and temporally resolved manner , is required . Here , we have initiated such an analysis , using the Drosophila intestinal epithelium as a model system . The Drosophila ISC lineage exhibits a high degree of functional and morphological similarities with the ISC lineage in the mammalian small intestine [22]–[24] . When a regenerative response is induced in the intestinal epithelium , ISCs self-renew and give rise to transient , non-dividing progenitor cells called EnteroBlasts ( EBs ) , which differentiate into either absorptive EnteroCytes ( ECs ) or secretory EnteroEndocrine ( EEs ) cells , triggered by differential Notch signaling . ISCs are the only dividing cells in the posterior midgut of Drosophila and their entry into a highly proliferative state is regulated by multiple stress and mitogenic signaling pathways , including Jun-N-terminal Kinase ( JNK ) , Jak/Stat , Insulin , Wnt , and EGFR signaling [24] , [25] . The transcription factor CncC ( orthologue of mammalian Nrf2 and worm SKN-1 ) , a master regulator of intracellular redox homeostasis , controls proliferation of ISCs by limiting ROS accumulation [19] . Interestingly , mammalian Nrf2 has been suggested to buffer ROS production during ER stress , while worm SKN-1 has recently been found to coordinate antioxidant gene expression with Xbp1 [26] , [27] . During aging , flies develop epithelial dysplasia in the intestine , caused by excessive ISC proliferation and deficient differentiation of EBs [28] , [29] . This phenotype is a consequence of an inflammatory condition initiated by dysbiosis of the commensal bacteria , and causes metabolic decline , loss of epithelial barrier function , and increased mortality [30]–[32] . The ISC-intrinsic mechanisms causing the decline of proliferative homeostasis in the aging intestinal epithelium remain unclear . Here , we have dissected the role of the UPRER and redox signaling in the control of ISC function and epithelial homeostasis at cellular resolution . We find that ER homeostasis is lost in the aging intestinal epithelium , and that this loss correlates with intestinal dysplasia . Activation of the UPRER within ISCs is required and sufficient for ISC proliferation , and excessive ER stress contributes to the age-associated dysplasia observed in the Drosophila gut . These effects are mediated by changes in the intracellular redox state , which perturb Nrf2/CncC and JNK activities . Accordingly , we find that JNK and Nrf2/CncC act epistatically in the control of ISC proliferation by ER stress . Our findings provide an integrated model for the regulation of ISC activity by redox and proteostatic signaling , and highlight the effects of this integration on epithelial homeostasis .
In a recent transcriptome analysis of age-related changes in the Drosophila intestine [32] , we noticed that expression of the ER stress-responsive genes Bip/Hsc70-3/Hsc3 , the ER chaperone Gp93 , and Xbp1 are significantly induced in aging guts ( Figure 1A ) . To confirm these findings , we used an antibody against Hsc3 and a reporter for Xbp1 expression , Xbp1P::dsRed [33] , and assessed their expression in young and old guts ( Figure 1C , 1D , S1H ) . Consistent with the RNAseq results , Hsc3 immunoreactivity and Xbp1 expression increased throughout the posterior midgut of aging flies ( Figure 1C , 1D , S1H ) , suggesting that the UPRER is activated in the aging intestinal epithelium . Since ER stress has been implicated in deregulation of mammalian ISC function [2] , [16] , and since Drosophila ISCs over-proliferate in aging guts , causing epithelial dysplasia [28] , [29] , [32] , we assessed whether the UPRER is activated in aging ISCs . ISCs and EBs can be identified in the posterior midgut of flies by expression of GFP driven by the esg::Gal4 driver , and ISCs can further be identified by expression of the Notch ligand Delta ( DI ) . In young animals , Hsc3 was expressed at lower levels in progenitor cells than in differentiated cells . In old guts however , Hsc3 expression was strongly increased in progenitor cells , suggesting a specific activation of the UPRER in these cells ( Figure 1E , Figure S1H ) . We confirmed this using an Xbp1 splicing reporter [11] , [34] , which assesses activation of Ire1 . When this reporter was expressed in ISCs and EBs using esg::Gal4 , no activity was detected in young flies , but GFP fluorescence was readily detectable in old guts ( Figure 1F ) . To test whether the loss of ER homeostasis is a cause or a consequence of the age-associated over-proliferation of ISCs , we examined the proliferative activity of ISCs in which Xbp1 had been knocked down by RNAi ( the effectiveness of the RNAi and over-expression constructs used here and below were validated by RT-PCR; Figure S2D ) . We used the esg::Gal4 driver in combination with tub::Gal80ts , which allows temperature-inducible expression of UAS-controlled dsRNAs in ISCs and EBs ( this combination is labeled esgts throughout ) . Perturbing Xbp1 was sufficient to strongly induce ISC proliferation , as measured by the frequency of cells positive for the mitotic marker phospho-Histone H3 ( pH3; Figure 2A ) , and by the expansion of Dl+/esg+ cells within the epithelium ( Figure 2B ) . We confirmed the induction of ISC proliferative activity in these conditions by assessing the rate of tissue turnover in flies in which Xbp1RNAi and GFP were heritably expressed in ISCs and their daughter cells in response to an ISC-specific recombination event ( using esg::Gal4 , tubGal80ts , UAS::GFP , UASFlp , act>STOP>Gal4 , also termed esgtsF/O , [35] ) . Knocking down Xbp1 greatly accelerated epithelial renewal compared to wild type conditions , further highlighting a role for ER stress in promoting ISC proliferation ( Figure S1A ) . The over-proliferation induced by knocking down Xbp1 in ISCs also increased phosphorylation of eIF2α , which is phosphorylated in response to ER stress by PERK , indicating that this induction of ISC proliferation is associated with an activation of the UPRER ( Figure 2C ) . We asked whether the proliferative response of ISCs in Xbp1 loss of function conditions was induced by changes in ER homeostasis in ISCs specifically , or whether induction of ER stress in EBs or daughter cells was driving ISC proliferation non-autonomously . To address this question , we first restricted expression of Xbp1RNAi to ISCs , by combining esg::Gal4 with a transgenic construct that inhibits Gal4 activity in EBs ( Su ( H ) -Gbe::Gal80; Su ( H ) -Gbe promoter elements are activated specifically in EBs in response to Dl/N signaling in EBs [36]; Figure S2E ) . Using 3 different dsRNA constructs against Xbp1 , we confirmed that Xbp1 knockdown specifically in ISCs is sufficient to induce ISC proliferation ( Figure 2D , Figure S1G ) . Xbp1 has a complex role in ER homeostasis , serving both as a sensor for ER stress and as a promoter of ER growth and proteostasis [11] , [34] , [37] , and may also act independently of the ER stress response to regulate ISC proliferation . We therefore tested if independently activating the UPRER , by impairing the removal of unfolded proteins in the ER directly , is sufficient to induce ISC proliferation . To this end , we perturbed the ER-associated degradation ( ERAD ) pathway by knocking down the ERAD-associated E3 ubiquitin ligase Hrd1 , which is required for ubiquitination and degradation of unfolded proteins in the ER [38] . Knockdown of Hrd1 also induced eIF2α phosphorylation in ISCs ( Figure 2C ) and increased ISC proliferation , both when driven by esgts and when driven by esgts in combination with Su ( H ) ::Gal80 ( Figure 2E , Figure S1G ) . We also examined whether knocking down Xbp1 or Hrd1 in other cell types of the gut epithelium is sufficient to promote ISC proliferation . Knocking down Xbp1 , but not Hrd1 in EBs ( using Su ( H ) Gbe::Gal4 [39] in combination with tub::Gal80ts ) or in ECs ( using NP1::Gal4 , tub::Gal80ts ) increased ISC proliferation ( Figure 2F ) , suggesting the existence of an Xbp1-specific non-autonomous effect on ISC proliferation in this tissue . Taken together , our results indicate that loss of ER homeostasis within ISCs induces ISC proliferation . The non-autonomous feedback of Xbp1 perturbation in ISC daughter cells on ISC proliferative activity is interesting and will be explored mechanistically in a separate study ( Wang et al . , in preparation ) . If activation of the UPRER is required for the regenerative response of ISCs , perturbation of UPRER components should influence the growth of ISC-derived cell clones . To test this idea , and to determine the requirement for UPRER components in the regulation of ISC activity in homeostatic conditions , we performed linage tracing of mutant stem cells via the MARCM system [40] . Clones generated by ISCs homozygous for the Xbp1 loss-of-function allele Xbp1k13803 , the Hrd1 loss of function allele hrd1Delta ( a deletion that deletes Hrd1 and CG2126 , see methods ) , or clones expressing Xbp1RNAi or Hrd1RNAi grew significantly faster than wild-type controls ( Figure 2G , Figure 2H , Figure S1B–E ) . Accordingly , clones derived from ISCs over-expressing endogenous Xbp1 ( using Xbp1d08698 , a line in which Xbp1 transcription is induced downstream of a transgenic UAS [37] , [41] ) , a transgene encoding a constitutively active , spliced version of Xbp1 [11] , or transgenic Hrd1 [42] , grew significantly slower than clones derived from wild-type ISCs ( Figure 2G , Figure 2H , Figure S1F ) . While maintaining ER homeostasis through the UPRER is thus essential to limit ISC proliferation and prevent dysplasia , a functional UPRER is also required for normal homeostatic regeneration . To further confirm that promoting ER homeostasis within ISCs selectively limits their proliferation , we assessed if increasing the expression of UPRER components in ISCs or their daughter cells was sufficient to allay tunicamycin-induced ISC proliferation . Tunicamycin , potently induces ER stress by inhibiting N-linked protein glycosylation and thus impairing protein folding [43] . Feeding tunicamycin very robustly induced ISC proliferation , supporting a role for activation of the UPRER in promoting ISC proliferation ( Figure 3A , Figure 3B , Figure 3D , and Figure 3E ) . Increasing ER stress tolerance by over-expressing endogenous Xbp1 , spliced Xbp1 , Hrd1 , or Hsc3/Bip in ISCs and EBs ( using esg::Gal4 and esgts; spliced Xbp1 was expressed only in adults using esgts ) is sufficient to significantly reduce tunicamycin-induced ISC proliferation ( Figure 3A , Figure 3B , Figure 3D , note that expressing spliced Xbp1 , endogenous Xbp1 ( using Xbp1d08698 or Xbp1EP2112 [37] , [41] ) , as well as Hsc3/Bip also inhibited proliferation induced by oxidative stress inducer paraquat , Figure 3C , Figure 3D , ) . This inhibition was also observed when spliced Xbp1 was over-expressed selectively only in ISCs ( using esgts; Su ( H ) Gbe::Gal80; Figure 3E ) , but not when spliced Xbp1 or Hrd1 were expressed in ECs or EBs only ( using the EC-specific NP1::Gal4 or the EB-specific Su ( H ) Gbe::Gal4 , both rendered heat-inducible by combination with tub::Gal80ts; Figure 3F , Figure 3G ) . Altogether , our data indicate that maintaining ER homeostasis in ISCs is critical for long-term ISC quiescence , while an active UPRER within ISCs is required and sufficient for ISC proliferation under homeostatic conditions , as well as in response to ER or oxidative stress . To assess whether engaging the UPRER is universally required for ISC proliferation , we assessed if reducing ER stress by over-expressing spliced Xbp1 was sufficient to limit ISC proliferation in a range of mitogenic conditions . ISC proliferation can be triggered through the JNK or the insulin/IGF signaling ( IIS ) pathways by over-expressing the JNK Kinase Hemipterous ( Hep ) [28] or the Insulin Receptor ( InR ) [44]–[46] . Over-expression of spliced Xbp1 was sufficient to inhibit ISC proliferation in both conditions ( Figure S2A , Figure S2B; this inhibition is not due to apoptosis of ISCs , as ISCs were readily observed even at 14 days after inducing expression of Hep and/or Xbp1spliced ) . Modulating Xbp1 activity further influenced the growth of ISC/EE tumors that accumulate due to defective EB differentiation in Notch loss of function conditions: While spliced Xbp1 prevented tumor formation , loss of Xbp1 exacerbated the growth of these tumors ( Figure S2C ) . By regulating ER homeostasis , Xbp1 thus serves as a rheostat broadly controlling ISC proliferative activity . The control of ISC proliferation by the UPRER resembles ISC control by ROS , which can trigger dysplastic over-proliferation of ISCs , but are required for proliferation during homeostatic regeneration [19] , [24] . Oxidative stress and ER stress are tightly linked: perturbation of redox homeostasis results in the accumulation of misfolded proteins and activation of the UPRER , and ER stress results in cytosolic oxidative stress [47]–[49] . To explore the relationship between ER stress , oxidative stress , and the UPRER in the ISC lineage , we assessed changes in intracellular redox homeostasis in ISCs deficient in Xbp1 or Hrd1 ( Figure 4A ) . Both conditions resulted in significantly increased fluorescence of dihydro-ethidium ( DHE , a redox-sensitive dye that can be used to detect ROS accumulation in live intestines [17] , [19] ) compared to wild-type progenitor cells ( Figure 4A ) . Over-expression of spliced Xbp1 , in turn , resulted in decreased DHE fluorescence in ISCs even under stress conditions ( Figure 4B ) . To further dissect the relationship between ER stress and oxidative stress , we perturbed the peroxiredoxin Jafrac1 , which strongly influences intracellular redox homeostasis and regulates ISC proliferation [19] , [50] . Knockdown of Jafrac1 was sufficient to increase ISC proliferation , and this increase was insensitive to the expression of spliced Xbp1 ( Figure 4C ) . Over-expression of the anti-oxidant enzymes glutathione peroxidase I ( GTPx-1 ) or Catalase ( Cat ) , on the other hand , inhibited tunicamycin-induced ISC proliferation ( Figure 4D ) , while knocking down Jafrac1 in ISCs prevented the inhibition of tunicamycin-induced ISC proliferation by spliced Xbp1 ( Figure 4E ) . Increased ROS production thus acts downstream of Xbp1 in the regulation of ISC proliferation . Increased ISC proliferation in Xbp1 or Hrd1 loss of function conditions , or in response to tunicamycin treatment , was associated with increased phosphorylation of JNK in Dl+ ISCs ( Figure 5A , Figure S3B ) , and activation of the JNK target gene puckered in all cells of the intestinal epithelium , including ISCs and neighboring ECs ( Figure 5B–D , Figure S3A , Figure S3B ) . This activation can be repressed by over-expressing spliced Xbp1 , GTPx-1 , or Cat in ISCs , suggesting that JNK is activated in response to ER stress-mediated ROS production ( Figure 5D ) . Since JNK activation in ISCs promotes their proliferative activity [28] , we tested whether JNK activity was required for ISC proliferation in Xbp1 loss of function conditions . Indeed , expression of BskRNAi , or of a dominant-negative version of Bsk ( BskDN ) , reduced proliferation of ISCs in which Xbp1 was knocked down , and in animals exposed to tunicamycin ( Figure 5E ) . These results suggest that activation of JNK in response to ER-stress-induced ROS production is required in ISCs to induce proliferation . How do Xbp1 and the UPRER regulate ISC proliferation ? Since promoting ER homeostasis by increasing Xbp1 activity or by stimulating the ERAD pathway was sufficient to limit ISC proliferation in all tested stress and mitogenic conditions , and since the Nrf2 homologue CncC exerts a similar effect on ISC proliferation [19] , we asked whether CncC activity was influenced by the ER stress response . To test whether the UPRER influences CncC activity in ISCs , we used a gstD1::lacZ construct that responds to CncC activity in ISCs [19] , [51] . Strikingly , loss of Xbp1 or Hrd1 was sufficient to inhibit gstD1::lacZ expression in ISCs , while ISCs over-expressing spliced Xbp1 maintained high gstD1::lacZ expression ( Figure 6A ) . We confirmed the modulation of Xbp1 activity in these cells by detecting expression of the Xbp1 target hsc3 ( Figure 6A ) . When ER stress was induced by treating animals with tunicamycin , gstD1::lacZ expression was reduced in ISCs , and this inhibition could be alleviated by over-expressing CncC , Xbp1 , or Hrd1 ( Figure 6B ) . The same results were obtained when spliced Xbp1 was expressed ( Figure S4A ) . Loss of ER homeostasis thus reduces CncC activity in ISCs , suggesting that CncC inhibition is a required component of the ER stress response in the regulation of ISC proliferation . To test this idea , we assessed if ISC proliferation is influenced by the interaction between Xbp1 and CncC . ISCs mutant for the CncC-specific E3 ubiquitin ligase Keap1 do not divide , due to impaired inhibition of CncC activity [19] . ISCs deficient in both Xbp1 and Keap1 did not divide either , suggesting that CncC acts downstream of Xbp1 in the regulation of ISC proliferation ( Figure 6C , Figure 6D ) . Accordingly , knocking down Keap1 or over-expressing CncC was sufficient to rescue ISC over-proliferation caused by loss of Xbp1 ( Figure 6E , Figure S4B ) . Over-expressing CncC was also sufficient to inhibit proliferation induced by tunicamycin treatment ( Figure 6F ) . At the same time , loss of CncC was not sufficient to rescue the proliferation defect of ISCs over-expressing spliced Xbp1 , suggesting that Xbp1 inhibits ISC proliferation not only by preventing CncC inhibition , but by additional mechanisms , most likely by inhibiting PERK activation through ER stress ( Figure 6G ) . The age-associated activation of the UPRER in ISCs , and the control of ISC proliferation by the UPRER , suggested that ER stress in ISCs also plays an important role in promoting age-related dysplasia . To address this question , we asked whether promoting ER homeostasis in progenitor cells is sufficient to limit dysplasia . Xbp1 or Hrd1 over-expression was sufficient to maintain expression of gstD1::lacZ , indicating that CncC activity , which declines with age in ISCs [19] was maintained ( Figure 7A ) . Accordingly , Hrd1 expression prevented the age-related increase in hsc3 expression in ISCs ( Figure S5A; CncC or Keap1RNAi expression also preserve gstD1::lacZ expression , confirming the responsiveness of the reporter to CncC activity; Figure S5B ) . As expected , the same genetic conditions also limit ISC proliferation in aging flies , preventing dysplasia ( Figure 7B ) [19] .
Our results establish a critical role for the coordination of oxidative and ER stress responses in the control of stem cell function , proliferative homeostasis and regenerative capacity in the Drosophila intestine . As previously observed for ROS signaling [19] , [24] , we find that ER stress not only promotes ISC proliferation , but that the UPRER is also required for ISC proliferation under basal , homeostatic conditions . The UPRER thus emerges as a rheostat regulating ISC proliferation under both stress and homeostatic conditions . Our results suggest that the tissue-wide increase in ER stress in the aging intestinal epithelium perturbs this regulation , resulting in intestinal dysplasia . The consequences of perturbing ER homeostasis in the intestinal epithelium are reminiscent of similar effects in Xbp1-deficient mice , where loss of Xbp1 promotes ISC proliferation and intestinal tumorigenesis [2] . At the same time , a recent study suggests that UPRER components are primarily expressed in transit amplifying cells of the intestinal epithelium , and that activation of the UPRER ( specifically the PERK branch ) promotes differentiation of intestinal epithelial stem cells [16] . The Drosophila midgut epithelium does not contain a transit amplifying cell population , yet our data suggest that a role for the UPRER in the control of ISC activity is conserved . The requirement for CncC inhibition in ER stress-mediated activation of ISC proliferation highlights the integrated control of ISC activity by oxidative and ER stress signals . We propose that Xbp1 , by promoting ER homeostasis , limits ROS accumulation in ISCs and thus maintains ISC quiescence ( Figure 7D , Figure 7E ) . Excessive ROS results in JNK activation , which in turn activates Fos and inhibits CncC in ISCs , triggering proliferation ( [19] , [52] and Li , Hochmuth and Jasper , unpublished results ) . This coordination of ER and oxidative stress responses by CncC and the UPRER is likely to be complex . In C . elegans the UPRER coordinates transcriptional regulation of anti-oxidant genes with the CncC homologue SKN-1 [27] . Interestingly , SKN-1 can also directly control the expression of UPRER components ( including Xbp1 , ATF-6 and Bip ) by binding to their promoter regions , independent of oxidative stress [27] . Studies in worms have further established the UPRER as a critical determinant of longevity , and Xbp1 extends lifespan by improving ER stress resistance [53] , [54] . Strikingly , local activation of the UPRER can trigger UPRER responses in distant tissues , indicating that endocrine processes exist that coordinate such stress responses across cells and tissues [53]–[56] . Our results support the notion that improving proteostasis by boosting ER folding capacity improves long-term tissue homeostasis . These effects seem to be largely mediated by cell-autonomous integration of the UPRER and redox response by JNK and CncC , but we also observe non-autonomous effects of ER stress on ISC proliferation when knocking down Xbp1 in EBs or ECs selectively . Furthermore , JNK is activated broadly in the intestinal epithelium when Xbp1 or Hrd1 are knocked down in ISCs and EBs , suggesting that non-autonomous interactions between cells experiencing ER stress also influence the regenerative response of this tissue . The molecular events regulating the coordination between cell-autonomous and non-autonomous events in the ER stress response of ISCs are subject of current investigation ( Wang et al . , in preparation ) . In the small intestine of mice , the UPRER influences regenerative activity not only by influencing ISCs and transit amplifying cells directly , but also by influencing intestinal immune homeostasis . Loss of Xbp1 in intestinal epithelial cells ( IECs ) leads to apoptosis of secretory Paneth cells and goblet cells , and this pathology is associated with inflammation and higher risk of IBD [1] , [3] . Deregulation of innate immune responses by the UPRER is also found in human patients [1] , [3] , [57] , as well as in C . elegans [3] , [58] . It can therefore be anticipated that the age-related increase in ER stress in the fly intestine also influences innate immune homeostasis and may contribute to commensal dysbiosis , which we have recently shown to be a driving factor in the age-related loss of proliferative homeostasis of the fly intestine [32] . It will be intriguing to dissect the interaction between the UPRER machinery , innate immune signaling in ECs , commensal homeostasis and stem cell function in detail , and we anticipate that these interactions have a significant effect on overall lifespan of the organism .
Fly lines w1118 , frt82B , frt40A , UAS::nlsGFP , UAS::hsc3 , UAS::Xbp1RNAi ( TRip:HMS03015 ) were obtained from the Bloomington Drosophila stock center . The following RNAi lines were obtained from the Vienna Drosophila RNAi Center: UAS::Xbp1RNAi ( v109312 , v15347 ) , UAS::Hrd1RNAi ( v6870 ) , UAS::bskRNAi . The following fly lines were generously provided as indicated: y1w1; esg::Gal4/+ by Dr . S Hayashi; UAS::xbp1EP2112 by Dr . Kyoung Sang Cho; UAS::xbp1d08698 by Dr . P . Fernandez-Funez; esgtsF/O by Dr . H . Jiang; Su ( H ) Gbe::Gal4 by Dr . S . Bray; pucE69::lacZ by Dr . E . Martín-Blanco; UAS::xbp1spliced by Dr . P . Domingos , UAS::bskDN by Dr . M . Mlodzik . The Hrd1 loss of function allele Hrd1Delta was made by FRT-mediated deletion of sequences between the Pbac insertion lines Pbac{PB}sip3 c00467 and Pbac{PB}faf06363 . hs-FLP was expressed in flies in which these Pbac insertions were in trans , deleting Hrd1 and its nearby gene CG2126 . All flies were raised on yeast/molasses-based food at 25°C and 65% humidity on a 12 hr light/dark cycle , unless otherwise noted . For tunicamycin or paraquat exposure , flies were starved in empty vials for 6–8 hrs and fed with a 5% sucrose solution± 50 µM tunicamycin or ±5 mM paraquat for 24 hrs followed by dissection in PBS . For TARGET experiments , flies were raised at 18°C and shifted to 29°C at certain time points after eclosion . For MARCM clone induction , adult flies were aged for 1–2 days and then heat shocked at 37°C for 45 min . The DNA fragment containing an enhancer of Su ( H ) GBE and a mini promoter of HSP70 was amplified from Su ( H ) GBE-Gal4 [39] using PCR , with the following primers: 5′-AGTGAATTCAATTAGGCCTACTAGACTTG-3′ ( the 20th nucleotide “T” is replaced by “A” to eliminate the endogenous XbaI site ) . 5′-AGTTCTAGATCATGATGCGGCCGCTCAGGAGGCTTGCTTCAAGCTTG-3′ ( a NotI site was introduced in this primer ) . The amplified DNA was cut and ligated into EcoRI and XbaI digested pCasper-Tub-Gal80 [1]–[7] , [59] to produce the construct pCasper-Su ( H ) GBE . Then the DNA fragment containing Gal80 and Sv40 polyA was cut from pCasper-Tub-Gal80 at the NotI and XhoI sites , and ligated into NotI- and XhoI-digested pCasper-Su ( H ) GBE to produce the Su ( H ) GBE-Gal80 construct . The construct was sequenced , purified , and microinjected into embryos using the standard method . Guts were dissected in PBS , fixed for 45 min at room temperature in 100 mM glutamic acid , 25 mM KCl , 20 mM MgSO4 , 4 mM sodium phosphate , 1 mM MgCl2 , and 4%formaldehyde , washed for 1 hr , and incubated with primary antibodies and second antibodies in washing buffer ( PBS , 0 . 5% BSA , 0 . 1% Triton X-100 ) . The following primary antibodies were used: , guinea-pig anti-hsc3 antibody antibody [60] ( 1∶150 ) , mouse anti-Delta ( Developmental Studies Hybridoma Bank , 1∶100 ) , rat anti-Delta ( gift from Dr . MD Rand , University of Rochester , 1∶1000 ) ; rabbit anti-PH3 ( phosphorylated histone H3 , Upstate , 1∶1000 ) , mouse anti-β-galactosidase ( Developmental Studies Hybridoma Bank , 1∶500 ) , rabbit anti-β-galactosidase ( Cappel , 1∶5000 ) , rabbit anti-peIF2α antibody ( Cell Signaling: 3597 , 1∶150 ) , mouse anti-pJNK antibody ( Cell Signaling: 9255 , 1∶150 ) . For Delta antibody staining , guts were fixed using a methanol-heptane method as descried [61] . Fluorescent secondary antibodies were purchased from Jackson ImmunoResearch Laboratories . DNA was stained using DAPI . Confocal imaging was performed on a Zeiss LSM700 confocal microscope and processed using ImageJ and Adobe Illustrator . Total RNA from young female samples were extracted using Trizol ( Invitrogen ) and cDNA was synthesized using Superscript III ( Invitrogen ) . Real time RCR was performed on a Bio-Rad CFX96 detection system . Expression Values were normalized to RP49 expression levels . Primers included: total Xbp1 transcripts ( Forward:TGGGAGGAGAAAGTGCAAAG , Reverse:TCCGTTCTGTCTGTCAGCTC ) , Spliced Xbp1 ( Forward: ACCAACCTTGGATCTGCCG , Reverse:CGCCAAGCATGTCTTGTAGA ) , Hrd1 ( Forward:GCAGTTGGTCTTTGGCTTTG , Reverse: ATGGGCAGCGCGTATATTT ) , RP49 ( Forward:TCCTACCAGCTTCAAGATGAC , Reverse:CACGTTGTGCACCAGGAACT ) . ROS levels were measured as described before [19] . Briefly , guts were dissected in Schneider's medium , incubated in 30 µM ( Invitrogen ) for 5 min at room temperature in the dark , washed twice and mounted to be imaged immediately . GFP expressed under the control of esg::Gal4 , Su ( H ) ::Gal80 was used to identify ISCs and/or EBs . | Loss of proper protein homeostasis ( proteostasis ) as well as increased production of reactive oxygen species ( ROS ) is a hallmark of aging . In complex metazoans , these processes can result in proliferative diseases and cancers . The protein folding capacity of the endoplasmic reticulum ( ER ) is monitored and maintained by the unfolded protein response of the ER ( UPRER ) . In this study , we identify a coordinated role of UPRER and oxidative stress signaling in regulating the proliferation of intestinal stem cells ( ISCs ) . We find that the ER-stress responsive transcription factor Xbp1 and the ER-associated degradation pathway component Hrd1 are sufficient and required cell autonomously in ISCs to limit their proliferative activity . This function is dependent on the activities of the stress sensor JNK and the redox-responsive transcription factor CncC , which we have previously identified as regulators of ISC proliferation . We further show here that promoting ER homeostasis in aging ISCs is sufficient to limit age-associated epithelial dysplasia . Our results establish the integration of UPRER and oxidative stress signaling as a central mechanism promoting regenerative homeostasis in the intestinal epithelium . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"biology",
"biology",
"and",
"life",
"sciences"
] | 2014 | Integration of UPRER and Oxidative Stress Signaling in the Control of Intestinal Stem Cell Proliferation |
Malaria in pregnancy threatens birth outcomes and the health of women and their newborns . This is also the case in low transmission areas , such as Colombia , where Plasmodium vivax is the dominant parasite species . Within the Colombian health system , which underwent major reforms in the 90s , malaria treatment is provided free of charge to patients . However , patients still incur costs , such as transportation and value of time lost due to the disease . We estimated such costs among 40 pregnant women with clinical malaria ( 30% Plasmodium falciparum , 70% Plasmodium vivax ) in the municipality of Tierralta , Northern Colombia . In a cross-sectional study , women were interviewed after an outpatient or inpatient laboratory confirmed malaria episode . Women were asked to report all types of cost incurred before ( including prevention ) , during and immediately after the contact with the health facility . Median total cost was over 16US$ for an outpatient visit , rising to nearly 30US$ if other treatments were sought before reaching the health facility . Median total inpatient cost was 26US$ or 54US$ depending on whether costs incurred prior to admission were excluded or included . For both outpatients and inpatients , direct costs were largely due to transportation and indirect costs constituted the largest share of total costs . Estimated costs are likely to represent only one of the constraints that women face when seeking treatment in an area characterized , at the time of the study , by armed conflict , displacement , and high vulnerability of indigenous women , the group at highest risk of malaria . Importantly , the Colombian peace process , which culminated with the cease-fire in August 2016 , may have a positive impact on achieving universal access to healthcare in conflict areas . The current study can inform malaria elimination initiatives in Colombia .
Malaria is a harsh , undesirable and life-threatening disease and , if experienced during pregnancy , can cause adverse effects on birth outcomes and on the health of women and newborns [1] . Such effects encompass long term disabilities and , at the aggregate level , are likely to slow down considerably the economic development of endemic areas [2–4] . In the short term , a malaria in pregnancy ( MiP ) episode may produce a considerable shock to households’ budgets [5] . In low endemicity areas , including Colombia where P . vivax predominates , little information is available on the epidemiology and the socio-economic aspects of malaria . In Colombia , few studies have examined the socio-economic determinants and impacts of the infection [6–9] . All the published studies depict malaria in Colombia as the disease of the poorest people living in rural areas . However , only one study offered estimates of malaria costs to families [6] , while to date , no published study has assessed the socio-economic burden of MiP in the country . The socio-economic aspects of disease are influenced by the characteristics of the health system . This is particularly true for Colombia where the health system has been under continuous reform for the past 25 years . Major reforms were undertaken in 1993 with the aim of increasing health system efficiency and access to healthcare , which was very skewed towards formal workers and left informal workers , a large proportion of the active population , unprotected [10] . Under a managed competition model , the reform created two schemes , contributory and subsidised: the former targeting formal employees and people able to pay , financed by mandatory contribution; the latter targeting people with no ability to pay , funded by the contributory scheme and by other sources such as general taxation . Eligibility for the subsidised regimen is determined through a socio-economic index called SISBEN ( Sistema de Identificación de Beneficiarios; Beneficiaries Identification System ) that classifies the population into six strata based on several socio-economic indicators [11] . The lowest two strata ( the poorest ) are eligible for the subsidised scheme and patients pay either nothing or a small fee for the healthcare they receive . Although the 1993 reform effectively increased health insurance coverage , there were discordant views on the existence of pockets of inequity the reform may have left: according to some researchers the increase in insurance coverage did not completely translate into healthcare coverage [12–14] . Recently , under the principle of the universal right to health , citizens in search of ( expensive ) health interventions denied by the private insurers have brought legal action ( tutelas ) to the Constitutional Court . Denied health services were finally funded by the Governmental Solidarity and Guarantee Fund ( Fosyga ) leading to sharp increases in public expenditure , to the extent that a recent government decree declared a “social emergency” due to a health financing crisis [15 , 16] . Some studies have assessed the regressivity or progressivity of the Colombian health financing system but still little information is available on the out-of-pocket expenditures incurred , particularly in non-urban areas , among communities whose main activity is agriculture or livestock [17] . In addition , armed conflict in certain rural areas has led to additional challenges for healthcare provision with women and ethnic minorities being at highest risk [18 , 19] . To address this , a series of public health interventions have targeted women of ethnic minorities living in rural conflict areas which are often also malaria endemic areas [20] . Living in rural and conflict-affected areas has been found to be a risk factor for catastrophic expenditures in Colombia [21] . According to the National Public Health Plan , malaria in Colombia is a “disease of public health interest” and all the interventions , including both prevention and treatment , are free of charge to the population , irrespective of health scheme affiliation [22] . However , despite zero medical costs , patients still incur non-medical expenses , such as transportation , and indirect costs , such as the value of time lost because of the illness . The aim of this study was to estimate the costs associated with MiP in a rural area of the Cordoba department , Northern Colombia , the area with the highest malaria burden in the country [23–25] . At the time of the study , the area was characterized by armed conflict and high vulnerability of ethnic minorities .
This study was conducted in the context of the PregVax study . This was a health-facility based cohort study aimed to estimate the burden of P . vivax infection in pregnancy in five endemic countries , including Colombia . The ethics statement for PregVax is published elsewhere [26] . In addition , the economic study ( ECO_PregVax ) was approved by the Ethics Committee of the Hospital Clinic of Barcelona , Spain . All adult subjects provided written informed consent prior to participating in ECO_PregVax and , if younger than eighteen years of age , a parent or guardian provided written informed consent on the child’s behalf . This study was carried out in the municipality of Tierralta , department of Cordoba , a malaria endemic area of the Colombian territory [23] . The municipality has an area of nearly 5 , 000 km2 with about 90 , 000 inhabitants , 44 . 4% of whom live in rural areas , 55 . 6% in urban or semi-urban areas , and about 2% of the population is indigenous . The indigenous population Embera Katío used to live in a protected area located on the high basin of the Sinú river , administratively pertaining partly to the municipality of Tierralta and partly to Ituango ( Antioquia department ) . Due to both the construction of a hydroelectric power plant that came into operation in the year 2000 and to the armed conflict , the Embera Katío were forced to spread all over the municipality . The main economic activities within the municipality are cattle farming , agriculture , logging and fishing . The Gross Domestic Product per capita of Cordoba department for the year 2014 was about 3 , 900 US$ [27] . According to the national public health surveillance system , in 2010 , out of a total of 117 , 108 malaria cases reported in Colombia , more than 20 , 000 ( 17 . 2% ) were registered in Córdoba and most of them ( over 14 , 000 ) were due to P . vivax . Of the cases reported in Cordoba , 29 were in pregnant women [23] . In a village of the municipality of Tierralta , the prevalence of Plasmodium spp . infection was 17 . 9% ( 38/212; 95% CI: 12 . 5–23 . 3% ) [24] . The annual parasite index ( API ) recorded between 2008 and 2012 , was of 44 . 0 cases/1 , 000 habitants . Based on this , Tierralta was considered as a high risk malaria area in comparison with two other endemic areas of the country , namely Buenaventura ( Valle del Cauca ) and Tumaco ( Nariño ) , with API of 6 . 0 cases/1 , 000 habitants and 7 . 7 cases/1 , 000 habitants , respectively [25] . In Tierralta malaria and respiratory diseases are perceived as the most common health problems in the population . The lack of environmental interventions is recognized as the major cause of malaria [28] . In a knowledge , attitudes and practices study undertaken in three regions ( Tierralta , Buenaventura and Tumaco ) it was observed that most of the population uses insecticide-treated nets ( ITNs ) to protect themselves from malaria , but over 75% of the people in Tierralta do not use any tool or strategy to prevent malaria transmission outdoors [25] . Data were collected in the catchment area of San José hospital , a public primary level health facility offering a range of services , including outpatients , immunization , antenatal care , laboratory , pharmacy , dentistry , radiology services , emergencies and inpatients ( delivery , pediatric and adults ) . San José hospital directly administers 16 aid posts spread across the municipality where a medical doctor attends patients for a few hours every week . Aid posts help increase access to healthcare considering that traveling to the hospital from the villages takes up to 8 hours by combined transportation ( usually motorbike plus boat ) and the journey may include crossing the Sinú river . The PregVax study enrolled 2 , 043 pregnant women at the antenatal clinic of the San José hospital between April 2009 and June 2011 and followed them up until delivery . Prevalence of MiP by microscopy was 1 . 2% detected either at ANC follow up visits or at out- or in-patient wards during the study period [26] . Between July and August 2011 a subset of women enrolled in the main study and diagnosed with any Plasmodium species infection either during pregnancy or during post-partum , were invited to participate in this economic study . As many women as was feasible , given the conditions of insecurity in the area , were reached and interviewed at their homes . After written informed consent was given , a questionnaire was administered by a trained fieldworker . We collected personal information , time lost because of the illness , data on use and costs for the treatment and prevention ( bed net , skin repellent , insecticide ) associated with MiP , including direct ( medical and non-medical ) costs incurred at the health facility . Women were asked about any previous treatments sought for the symptoms associated with the same malaria episode . Characteristics of women included in this study were compared with those of women enrolled in the epidemiological study but not enrolled in the economic study ( both malaria infected and non-infected ) . The data analysis and presentation of results were similar to a previous study carried out in Brazil [5]: direct costs ( financial costs ) were broken down into medical , transportation and other costs ( food , phone calls , etc . ) . Indirect costs were calculated by multiplying reported time lost by the legal minimum wage in force in Colombia in the year 2012 ( Colombian pesos $566 , 700 , monthly , corresponding to US$ 307 ) [29] . The exchange rate was 1 , 848 . 14 Colombian pesos to the dollar [30] . Patient costs incurred at the San Jose hospital , as well as for any treatment sought earlier for the same episode , were calculated . Due to the skewness of the distribution , the median and the interquartile range ( IQR ) were used to report cost estimates . Mean values are also shown to provide a comprehensive description of cost distributions . Bootstrap simulation with 1000 replications was carried out to resolve distribution skewness [31 , 32] . The place of residence of each study woman ( neighbourhood ) was located on digital maps and distance to the health facility was calculated linearly , due to the impossibility of tracing the itinerary women may have followed to obtain treatment . For this study , women were approached only once . However , some women experienced more than one malaria episode during the follow-up period . To produce an estimate of the total cost of MiP , the database generated was merged with the data from the main epidemiological study . For the women with more than one episode , the cost estimated for one episode was multiplied by N episodes , assuming that cost per episode was constant . Data were analyzed with STATA version 12 . 0 ( Stata/SE 12 . 0 Stata corporation , College Station , TX , USA ) .
Median prevention costs ( all for bed nets ) , were US$ 0 ( IQR 8 . 12 ) . Only 14 women spent a positive amount to acquire a bed net ( possibly because these were distributed for free either by the government or by non-governmental institutions ) and 26 ( 65% ) women declared they had slept under a bed net the night before the interview ( Table 3 ) . Only 25% of the interviewed women had sought previous treatment and the cost incurred was negligible ( mean US$ 0 . 88 , with a median and IQR of 0 ) . Transportation was a substantial source of cost , US$ 12 . 99 ( IQR 23 . 80 ) and so were indirect costs of US$ 7 . 29 ( IQR 10 . 55 ) at the health facility or US$ 7 . 29 ( IQR 25 . 27 ) if the whole episode was considered . Estimation using a bootstrap with 1000 replications led to similar results . Cost of prevention and previous treatment had both median and IQR equal to 0; the mean values were US$ 2 . 53 and 1 . 87 , respectively ( Table 3 ) . Transportation costs were US$ 10 . 82 ( IQR 17 . 32 ) . Indirect costs at the health facility were US$ 9 . 11 ( IQR 3 . 64 ) or 17 . 31 ( IQR 18 . 22 ) when the whole clinical episode was considered . Total costs at the health facility were US$ 26 . 65 ( IQR 24 . 94 ) or 54 . 33 ( IQR 49 . 09 ) for the whole clinical episode . Bootstrapping the estimates only slightly reduced the mean values . Women who experienced two clinical episodes incurred costs ranging from US$ 18 . 91 to US$ 108 . 66 ( Table 4 ) . Of the 5 women who had more than one episode , two had two P . falciparum infections , two had one P . vivax episode and one had a P . vivax and a P . falciparum infection .
Although malaria control interventions are free of charge to users in Colombia , the economic costs incurred by pregnant women in the study were substantial . Total median costs associated with inpatient treatment of a whole malaria episode ( US$ 54 . 33 ) represented the 18% of the monthly minimum salary in force in the country at the time of the study ( US$ 307 ) . Transportation costs and indirect costs ( cost of time ) were the largest components . The highest costs of over US$ 100 were estimated for women who experienced two inpatient episodes . This study cannot assess whether such high costs constitute a barrier to malaria diagnosis and treatment as only women who actually received a ( positive ) malaria diagnosis were included . However , repeated episodes requiring admission are likely to imply substantial shocks to families’ budgets . To our knowledge , only one previous study has estimated the costs associated with P . vivax MiP [5] . In that study , conducted in Brazil , total costs were higher than the ones estimated here: bootstrapped median costs were US$ 49 . 27 for outpatients and 240 . 78 for inpatients versus 28 . 93 and 54 . 33 in this study . The main driver of this difference was the time spent to reach the health facility , which was higher in Brazil than in Colombia , and which translated into higher indirect costs , particularly for inpatients , despite the lower value of time used for Brazil than for Colombia ( monthly value of 237 US$ for Brazil and of 307 US$ for Colombia ) . All remaining parameters estimated in the two studies were similar . Even though the two health systems are very different , malaria is considered as a disease of public health interest in both countries , and prevention and treatment are provided free of charge to patients . All but one women in this study belonged to the first level of the subsidised system , suggesting that our sample was constituted by the poorest individuals . Although we cannot quantify their potential impact , we can speculate that costs incurred by these women are likely to represent a significant economic burden to their limited household budgets . Importantly , in the context of this study the challenges faced by women when seeking treatment are likely to go beyond the estimated costs . Armed conflict has a detrimental impact on healthcare both from the demand and supply sides [34] . From the patient perspective , the journey from home to the health facility is likely to be dangerous , and people seeking treatment may have to deviate from the easiest path to avoid assaults . In Colombia , the re-emergence of infectious diseases transmitted by vectors has been particularly important in areas with armed conflict [35] . In the “Colombian National malaria control and surveillance plan 2003–2006” , Tierralta , Córdoba , was defined as an area where armed conflict has exacerbated the burden of malaria due to forced displacement of the population; either a partial or total suspension of vector control activities; challenges of diagnosis and treatment; and deterioration of the environment leading to the formation of mosquitos breeding sites [36] . There has been a high proportion of victims of forced displacement in Tierralta , including the Embera Katío people who were forced to leave for several reasons: ( 1 ) the armed conflict for the control of the land for the production and distribution of illicit drugs; ( 2 ) the illegal exploitation of natural resources , mainly wood and ( 3 ) the construction of the dam Urrá I in the year 2000 [37] . There is no census reporting the exact number of displaced people; however , it is clear that the majority of displaced families lived in poverty [38] . For those who remained in the protected area , the construction of the dam resulted in serious constrains to access to food ( fish in particular ) and in additional limitations on movement imposed by the armed groups: these factors led to a substantial socio-economic collapse of that community [38] . In our sample , nearly 25% of women were indigenous . Malaria and the costs associated with its management are potentially only the tip of the iceberg of the problems incurred by Embera Katío community , who have suffered the violation of their rights to the land , their economic independence , education , and their culture . Currently interventions are aimed at preserving the culture and the rights of this indigenous population as planned by the Plan de Salvaguarda Etnica Pueblo Embera Katío del Alto Sinú [38] . Despite several challenges , the Colombian health system is making strong efforts to improve access to quality health care . Colombia is not far from achieving universal health care insurance: healthcare out-of-pocket expenditures have decreased consistently , and the population reports fewer unmet health needs and greater satisfaction with healthcare services following the 1993 reforms [39] . As in any context , the last mile is the most difficult to cover and the area where this study was conducted encompasses all main possible challenges: malaria endemicity , armed conflict , rural areas and minorities . Importantly , the peace process in Colombia , which started in 2012 with negotiations between the leaders of the guerilla and the government and having its climax in August 2016 with the cease-fire and end of hostilities , should have a positive impact also on the health system and on access to health services in conflict areas [40] . This study has one main limitation . Some women were interviewed several months ( up to a maximum of 9 ) after experiencing the malaria episode , which is likely to have resulted in recall bias in our estimates . Recall bias in self-reported expenditure has been described to translate into either underestimation or overestimation [41] . In this study , as questions were focused on costs incurred on a specific episode and not on recurrent expenditures , estimates are more likely to be underestimated as affected by “forgetting” . For this reason , the economic costs reported in this study should be interpreted as a lower bound of the real economic cost associated with MiP . In addition , women were interviewed only once limiting the possibility of estimating more precisely the whole cost associated with malaria during pregnancy and post-partum . A few studies have explored the economics of malaria in Colombia . In 2006 , costs of malaria treatment were estimated as part of the cost-effectiveness analysis of two alternative strategies for malaria control [7] . One strategy was constituted by the activities of the National Programme , the other by the integration of an educational strategy , the “Integrated Alternative” ( IA ) into the national program in Buenaventura on the Pacific coast of Colombia . Average household malaria treatment cost ( including direct and indirect cost ) in the area where the National Programme was implemented was US$36 . 2 and US$28 . 4 in the IA area ( 1998 figures ) . Malaria incidence appeared to be associated with income and with occupational status in Colombia in a study that tested Becker’s human capital theory and its further developments by Popkin and Schutz [8] . An in-depth study estimated the average cost per malaria case in Santa Cruz ( rio Naya , Colombia ) as US$17 . 30 , over 90% of which were indirect costs . The loss corresponded to the value of 5 . 6 days of work paid at the minimum monthly wage ( year 1986 ) [6] . Finally , labour reallocation within the family was assessed to be the most significant economic consequence of malaria , with women substituting for men in the fields when men are affected by the disease [9] . Despite the valuable information existing on socio-economic aspects of malaria in Colombia , no study has examined MiP . The present study fills this gap by estimating the economic burden of MiP that can inform malaria elimination initiatives in Colombia [42] . | Malaria in pregnancy is a harsh and undesirable illness and is the cause of adverse effects on birth outcomes and on the health of women and newborns . Despite the low transmission , the predominance of Plasmodium vivax over Plasmodium falciparum and free treatment , estimated costs incurred by pregnant women seeking malaria care in an endemic area of Northern Colombia are considerable . Importantly , these costs are likely to represent only one of the constraints that women face when seeking treatment in an area characterized , at the time of the study , by armed conflict , displacement , and high vulnerability of indigenous women , the group at highest risk of malaria in the area . Important advances may result from the current peace process , potentially able to support the efforts made since the 90s to reform the health system towards achieving universal health coverage . Within this context , the current study can inform malaria elimination initiatives in Colombia . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"parasite",
"groups",
"plasmodium",
"engineering",
"and",
"technology",
"transportation",
"tropical",
"diseases",
"geographical",
"locations",
"social",
"sciences",
"indirect",
"costs",
"parasitic",
"diseases",
"parasitology",
"health",
"care",
"apicomplexa",
"inpatients",
"patients",
"health",
"economics",
"south",
"america",
"economics",
"people",
"and",
"places",
"finance",
"outpatients",
"colombia",
"biology",
"and",
"life",
"sciences",
"malaria",
"financial",
"management"
] | 2018 | Patients’ costs, socio-economic and health system aspects associated with malaria in pregnancy in an endemic area of Colombia |
The contributions of the Sgs1 , Mph1 , and Srs2 DNA helicases during mitotic double-strand break ( DSB ) repair in yeast were investigated using a gap-repair assay . A diverged chromosomal substrate was used as a repair template for the gapped plasmid , allowing mismatch-containing heteroduplex DNA ( hDNA ) formed during recombination to be monitored . Overall DSB repair efficiencies and the proportions of crossovers ( COs ) versus noncrossovers ( NCOs ) were determined in wild-type and helicase-defective strains , allowing the efficiency of CO and NCO production in each background to be calculated . In addition , the products of individual NCO events were sequenced to determine the location of hDNA . Because hDNA position is expected to differ depending on whether a NCO is produced by synthesis-dependent-strand-annealing ( SDSA ) or through a Holliday junction ( HJ ) –containing intermediate , its position allows the underlying molecular mechanism to be inferred . Results demonstrate that each helicase reduces the proportion of CO recombinants , but that each does so in a fundamentally different way . Mph1 does not affect the overall efficiency of gap repair , and its loss alters the CO-NCO by promoting SDSA at the expense of HJ–containing intermediates . By contrast , Sgs1 and Srs2 are each required for efficient gap repair , strongly promoting NCO formation and having little effect on CO efficiency . hDNA analyses suggest that all three helicases promote SDSA , and that Sgs1 and Srs2 additionally dismantle HJ–containing intermediates . The hDNA data are consistent with the proposed role of Sgs1 in the dissolution of double HJs , and we propose that Srs2 dismantles nicked HJs .
Faithful transmission of genetic information in mitotically dividing cells requires the repair of DNA damage that occurs from exogenous and endogenous sources . Damage to both strands of DNA can cause a double-strand break ( DSB ) , as can replication of a damaged DNA template containing a nick . A single , unrepaired DSB can result in the loss of essential genes and lead to permanent cell-cycle arrest and cell death . To prevent these outcomes , DSBs are repaired by one of two pathways: error-prone nonhomologous end joining or error-free homologous recombination ( HR ) . As the major DSB repair pathway in the yeast Saccharomyces cerevisiae , HR promotes high-fidelity repair through the use of an intact template DNA sequence . However , HR can also lead to loss of heterozygosity and gross chromosomal rearrangements and thus requires tight regulation . To initiate HR , the 5′ ends of the DSB are resected to yield 3′ single-stranded regions of DNA ( for reviews , see [1] , [2] , [3] , [4] ) . These 3′ ends are coated with Rad51 to form nucleoprotein filaments that are competent to conduct a homology search and invade a donor duplex DNA molecule , promoting pairing with the complementary strand . Successful strand invasion of a homologous duplex results in the formation of a D-loop structure consisting of a region of heteroduplex DNA ( hDNA ) and a displaced single strand of DNA ( Figure 1 ) . New DNA synthesis occurs using the 3′ invading end as a primer , and this reaction enlarges the D-loop . Expansion of the D-loop , or its movement with the extending 3′ end [5] , eventually exposes sequences complementary to the other side of the break ( Figure 1A ) . In the canonical DSB repair ( DSBR ) model of recombination [6] , annealing between the D-loop and the non-invading end of the DSB ( “2nd end capture” ) results in the formation of a double Holliday junction ( dHJ ) intermediate ( Figure 1B ) . Alternatively , if the annealed D-loop is nicked , an intermediate with a single HJ ( sHJ ) will be generated [4] , [7] . In the gap-repair system used here , there is a strong dependence of CO events on the Rad1-Rad10 endonuclease [8] , which would be consistent with D-loop nicking . HJ-containing intermediates can be resolved by cleavage ( Figure 1C ) , and this process is generally assumed to yield either noncrossover ( NCO ) products that maintain the original linkages of DNA flanking the break , or crossover ( CO ) products in which the linkages of flanking DNA are switched . As an alternative to cleavage , a dHJ-containing intermediate can be “dissolved” to yield exclusively NCO products ( Figure 1D ) ( reviewed in [9] ) . In lieu of engaging the second end of the DSB and subsequent HJ formation , the D-loop can be dismantled ( Figure 1E ) . Annealing of the newly synthesized DNA to the non-invading 3′ end of the break then provides a template for the synthesis of the other strand of the damaged molecule . As this Synthesis-Dependent Strand-Annealing ( SDSA ) pathway does not go through an HJ-containing intermediate , it yields exclusively NCO products [10] . In S . cerevisiae , three 3′ to 5′ DNA helicases - Srs2 , Sgs1 and Mph1 - have been implicated in regulating the outcome of mitotic DSB repair [11] , and each increases the frequency of NCO events relative to CO events [12] , [13] . Srs2 ( suppressor of rad6 sensitivity ) was the first of the three helicases to be identified , and its gene was discovered in a screen for mutations that suppress the UV sensitivity of rad6 strains [14] . Because suppression depends on the HR machinery , it was suggested that this helicase normally inhibits recombinational bypass of DNA lesions [15] . In spontaneous recombination assays , loss of Srs2 increases the rate of recombination , confirming that Srs2 can inhibit recombination [16] , [17] . The anti-recombination activity of Srs2 has been attributed to its “strippase” activity , which removes the Rad51 protein from single-stranded DNA ends and thereby prevents strand invasion [18] , [19] . However , when Srs2 function was examined in the context of an HO endonuclease-induced DSB , it was paradoxically found to play a pro-recombination role [20] . The loss of Srs2 not only decreased the overall level of DSB repair , it led to a proportional increase of COs among the recovered products [12] , suggesting a specific role in NCO formation . Although it has been suggested that Srs2 directly dismantles D-loops to promote NCOs via SDSA [21] , an alternative possibility is that its pro-recombination role reflects the removal of Rad51 from single-stranded DNA ( ssDNA ) ends , which would promote annealing between the 2nd end of the DSB and the newly extended strand upon D-loop collapse . Consistent with this possibility , biochemical studies have shown that Rad51 complexed with ssDNA is a potent inhibitor of Rad52-mediated annealing reactions [22] . Sgs1 ( slow growth suppressor ) was identified based on genetic interactions with Top3 , with sgs1 mutations suppressing the genetic instability and slow growth of top3 strains [23] . Sgs1 is a member of the RecQ family of 3′ to 5′ DNA helicases and is the ortholog of the human BLM helicase [23] , [24] . Mutations in the BLM gene lead to the autosomal recessive disorder Bloom's syndrome , which is characterized by genetic instability and increased sister chromatid exchange [25] . Like Srs2 , the Sgs1 helicase has multiple roles in recombination . First , Sgs1 acts with the endonuclease Dna2 to promote extensive 5′ to 3′ resection of the DSB ends ( for a review , see [26] ) . Second , biochemical and in vivo studies suggest that Sgs1 , together with Top3 and Rmi1 , promotes NCO formation by dissolving dHJ-containing intermediates that could alternatively be cleaved to yield COs [27] , [28] . Dissolution involves migration of the two HJs towards each other , followed by decatenation of the two linked strands . Consistent with a role in dHJ dissolution , loss of Sgs1 results in increased CO formation during repair of an HO-induced DSB [12] . MPH1 was identified in a screen for mutants exhibiting a mutator phenotype [29] , and the encoded protein is the ortholog of the human Fanconi Anemia protein FANCM [30] , [31] . The participation of Mph1 in HR was initially inferred from epistasis analysis [32] . In its absence , the frequency of HO-induced COs was found to be elevated , but overall levels of repair were not affected [13] . The increase in COs was suggested to specifically reflect a loss of SDSA events , and consistent with this , Mph1 efficiently dismantles D-loops in vitro [33] . In a plasmid-based gap-repair assay , COs were similarly found to be elevated in the absence of Mph1 , a function that may be partially dependent on the mismatch repair ( MMR ) complex MutSα [34] . Although biochemical studies have suggested specific roles for Srs2 , Sgs1 and Mph1 in promoting either SDSA or dHJ dissolution , corresponding in vivo evidence has been lacking . In particular , prior studies have not been able to distinguish whether a given NCO product was produced by HJ cleavage , HJ dissolution or SDSA . To more rigorously assess the specific functions of the Mph1 , Srs2 and Sgs1 helicases in NCO formation , gapped plasmids were transformed into wild-type and mutant strains that were defective for the MMR protein Mlh1 and contained a diverged chromosomal template for repair . We measured gap-repair efficiency , determined the CO-NCO distribution among repair events and sequenced both products of individual NCO repair events to detect regions of hDNA . As the location of hDNA can be used to infer the underlying molecular mechanism of NCO formation , the data provide novel insight into how recombination intermediates are processed by these helicases . These molecular analyses are consistent the presumed roles of these helicases and suggest additional functions as well .
To control for variations in transformation efficiency , a linearized plasmid containing a LEU2 marker was co-transformed with the gapped HIS3 plasmid . His+ and Leu+ colonies were selected separately during each transformation , with the His+∶Leu+ ratio providing a measure of gap-repair efficiency . At least 12 independent transformations were done with the WT and each single-helicase mutant . To facilitate comparisons , the His+∶Leu+ ratio obtained in each individual transformation was divided by the mean His+∶Leu+ ratio obtained in the WT strain ( see Materials and Methods ) . The normalized ratios are presented in Figure 2B , where the mean transformation efficiency of WT is 1 . 00 . Relative to WT , the mean His+∶Leu+ ratio in the mph1Δ strain was 1 . 04 , indicating that loss of Mph1 does not affect the overall efficiency of gap repair ( p = 0 . 54 using a two-tailed Student's t-test ) . By contrast , the mean His+∶Leu+ ratio following transformation of the sgs1Δ strain was reduced 30% and that in the srs2Δ strain was reduced 3-fold relative to WT ( p = 0 . 018 and p<0 . 0001 , respectively ) . These data confirm that loss of either Sgs1 or Srs2 leads to decreased gap repair [8] , and additionally demonstrate that loss of Mph1 has no effect in this system . The gapped plasmid used in the transformation experiments contained an autonomously replicating sequence ( ARS ) but no centromere ( CEN ) sequence , allowing the repaired plasmid either to integrate into the chromosome with the repair template ( a CO event ) or to remain autonomous ( a NCO event ) . These two outcomes were distinguished by examining the stability of the plasmid-encoded URA3 marker , allowing His+ products to be partitioned into NCO and CO events ( Figure 2A; [8] ) . Simply comparing the proportions of COs and NCOs in different genetic backgrounds ( Figure S1 ) can be misleading , however , as it does not take into account changes in overall gap-repair efficiency . For example , an elevation in the proportion of COs could reflect either a specific gain in CO events with no effect on NCOs , a channeling of potential NCO products into the CO pathway or a specific loss of NCO products with no effect on COs . The efficiency of CO ( or NCO ) repair was thus calculated by multiplying the mean gap-repair efficiency by the proportion of CO ( or NCO ) events ( Table 1 ) . To allow statistical comparisons , the normalized His+∶Leu+ ratios in individual transformation experiments were multiplied by the proportion of COs or NCOs among gap-repair products , yielding a distribution of CO-type or NCO-type His+∶Leu+ ratios , respectively . The distributions in different strains were then compared using a two-tailed Student's t-test . In the WT yeast strain , only 9% of repair events were COs; the mean CO and NCO efficiencies were thus 0 . 09 and 0 . 91 , respectively ( Table 1 ) . Although the overall efficiency of gap repair in the mph1Δ strain was indistinguishable from that in WT , the proportion of COs increased to 19% . The mean CO efficiency in the mph1Δ strain was thus 0 . 20 , a change that was highly significant when compared to WT ( p<0 . 0001 ) . Even though there was a roughly compensatory decrease in proportion of NCOs in the mlh1Δ ( from 0 . 91 in WT to 0 . 84 ) , this did not translate into a significant change in mean NCO efficiency when compared to WT ( p = 0 . 13 ) . In the sgs1Δ strain , the proportion of COs among the repaired products increased to 15% . When the accompanying decrease in mean gap-repair efficiency in the sgs1Δ background was considered , however , there was no significant change in CO production relative to WT ( 0 . 09 and 0 . 11 , respectively; p = 0 . 30 ) . By contrast , the mean efficiency of NCOs decreased from 0 . 91 in the WT to 0 . 62 in the sgs1Δ strain , a change that was highly significant ( p = 0 . 005 ) . Finally , an even greater proportional increase in COs was observed in the srs2Δ strain: from 9% in WT to 25% of total repair events in the mutant . Taking into account the 3-fold decrease in overall gap-repair efficiency , however , the mean efficiency of CO formation was only very slightly reduced in the srs2Δ strain ( from 0 . 09 to 0 . 08; p = 0 . 039 ) . In contrast to the marginal effect on COs , the mean NCO efficiency decreased from 0 . 91 in WT to only 0 . 23 in the srs2Δ mutant ( p<0 . 0001 ) . Thus , with either an Sgs1 or Srs2 deficiency , the reduction in overall gap-repair efficiency reflects a strong reduction in NCO formation with little , if any , compensatory gain in COs . Upon loss of Mph1 , however , there was a re-distribution of products types without a change in overall gap-repair efficiency . Alterations in CO or NCO production could reflect an effect on the NCO-specific SDSA pathway , a change in the efficiency of forming HJ-containing intermediates and/or a change in how HJ-containing intermediates are resolved . To differentiate between these possibilities , a HIS3-containing CEN plasmid , which generates only viable NCO products , was used in transformation experiments . Both the plasmid and chromosomal alleles involved in individual gap-repair events were sequenced ( Table S1 ) . Of 249 NCO products sequenced from the WT strain , regions of hDNA were detected on the plasmid allele in 159 ( Table 2 ) . In 18 of these , hDNA was present on both sides of the gap ( bidirectional hDNA ) , consistent with the hDNA pattern predicted by formation of an HJ-containing intermediate; the remaining 141 had hDNA on only one side of the gap ( unidirectional hDNA ) , consistent with production via the SDSA mechanism ( Figure 3 ) . It should be noted the distribution of hDNA observed here when using the NCO-only plasmid is very similar to that previously reported using an ARS-containing plasmid [35] , and confirms that ∼90% of NCOs are likely derived from SDSA . Although the corresponding chromosomal alleles were sequenced in each NCO event analyzed , none had the hDNA pattern predicted by HJ cleavage , confirming that HJ cleavage does not contribute significantly to NCO formation in this system ( Table S1; [35] ) . To estimate the efficiencies with which HJ dissolution and SDSA occurred , the proportions of bidirectional and unidirectional hDNA tracts , respectively , among NCOs was multiplied by the mean efficiency of NCO production . In WT , the mean NCO efficiency of 0 . 91 was thus broken down into a bidirectional and unidirectional hDNA values of 0 . 10 and 0 . 81 , respectively ( Table 2 ) . The sequences of 242 NCO products isolated from the mph1Δ strain were analyzed and 176 hDNA tracts were detected . Of these tracts , 15% were bidirectional , and 85% were unidirectional ( Figure 3; Table 2 ) . When these proportions were multiplied by the mean NCO efficiency in the mph1Δ background , there was a small but significant increase in NCOs with bidirectional hDNA ( from 0 . 10 in WT to 0 . 12 in mph1Δ; p = 0 . 002 ) and a corresponding reduction in NCOs with unidirectional hDNA ( from 0 . 81 in WT to 0 . 71 in mph1Δ; p = 0 . 027 ) . Thus , even though there was no significant decrease in total NCOs in the absence of Mph1 , there was a shift from unidirectional to bidirectional NCO products . In the absence of Sgs1 , the overall gap-repair efficiency dropped to approximately 70% of the WT level , and this reflected a selective loss of NCO events with no compensatory gain in COs . The products of 285 NCOs isolated from the sgs1Δ strain were sequenced , 149 of which had detectable hDNA on the plasmid . Thirteen hDNA tracts were bidirectional , and 136 were unidirectional ( Figure 3; Table 2 ) . Taking into account the reduction in overall gap-repair efficiency in the sgs1Δ strain , NCOs with bidirectional hDNA decreased from 0 . 10 in the presence of Sgs1 to 0 . 05 in its absence ( p<0 . 0001 ) . NCOs with unidirectional hDNA also dropped significantly in the sgs1Δ strain ( from 0 . 81 in WT to 0 . 56; p = 0 . 009 ) . These data are consistent with the presumed role for Sgs1 in promoting NCO formation via dHJ dissolution and additionally indicate that Sgs1 promotes SDSA . To examine the relationship between Sgs1 and Mph1 during gap repair , we constructed an mph1Δ sgs1Δ double-mutant strain . The mean gap-repair efficiency decreased significantly from ∼1 . 00 in the WT and mph1Δ strains to 0 . 87 in the double mutant ( Table 2; p = 0 . 039 and p = 0 . 032 , respectively ) , a decrease that was similar to that observed in the sgs1Δ single mutant ( p = 0 . 21 ) . The mean efficiency of NCO production in the mph1Δ sgs1Δ strain also was similar to that obtained in the sgs1Δ strain ( p = 0 . 52 ) and significantly less that than in either the WT or mph1Δ background ( p = 0 . 0001 and p = 0 . 012 , respectively ) . Though the overall efficiencies of NCOs with unidirectional hDNA were similar in the mph1Δ sgs1Δ and sgs1Δ backgrounds ( p = 0 . 6 ) , the efficiency of the minority , bidirectional events in the double mutant was greatly elevated ( p<0 . 0001 ) , and only slightly different from that observed in the mph1Δ single mutant ( 0 . 14 and 0 . 12; respectively; p = 0 . 049 ) . Altogether , the data suggest ( 1 ) that Sgs1 is more important than Mph1 for the production of NCO events with unidirectional hDNA and ( 2 ) that Sgs1 does not remove bidirectional hDNA-containing NCO intermediates that arise in the absence of Mph1 . As will be elaborated further in the Discussion , we speculate that the elevated bidirectional hDNA in the mph1Δ background may reflect nicked HJ-containing intermediates , which are not expected to be substrates for the Sgs1-Top3-Rmi1 dissolvase . In the absence of Srs2 , the overall gap-repair efficiency decreased ∼3 fold and , as in the sgs1Δ strain , this reflected a specific reduction in NCO events ( Table 1 ) . Among 254 NCOs sequenced from the srs2Δ strain , hDNA was detected in 129 . Bidirectional hDNA was present in 27 of these and unidirectional hDNA in the remaining 102 ( Figure 3 and Table 3 ) . As expected , the decrease in the efficiency of NCOs with unidirectional hDNA ( from 0 . 81 in WT to 0 . 19 in srs2Δ; p<0 . 0001 ) was similar to the overall reduction in NCO events . There also , however , was a significant decrease in NCOs with bidirectional hDNA ( from 0 . 10 in WT to 0 . 05 in the srs2Δ mutant; p<0 . 0001 ) . To further explore the unexpected role of Srs2 in promoting the formation of bidirectional hDNA-containing NCO products , we examined gap repair in mph1Δ srs2Δ double- and srs2-860 single-mutant backgrounds . The mean gap-repair efficiency in the mph1Δ srs2Δ double mutant was 0 . 55 , a value 2-fold less than that in the mph1Δ single ( 1 . 04; p<0 . 0001 ) but significantly greater than that obtained in the srs2Δ single mutant ( 0 . 31; p = 0 . 009 ) . This suggests that in the absence of Mph1 , the need for the pro-recombination activity of Srs2 is relaxed . A similar , intermediate value for mean NCO efficiency was observed in the double mutant ( 0 . 39 ) relative to the mph1Δ ( 0 . 84; p<0 . 0001 ) or srs2Δ ( 0 . 23; p = 0 . 015 ) single mutant . When NCOs obtained in double mutant were partitioned into those containing unidirectional or bidirectional hDNA ( 43 of 85 NCOs sequenced contained hDNA ) , the unidirectional class again had an intermediate value relative to the two single mutants . By contrast , the efficiency of producing the bidirectional hDNA class of NCOs in the double mutant was indistinguishable from that in the srs2Δ single mutant ( p = 0 . 56 ) and significantly less than that in the mph1Δ single mutant ( 0 . 5 and 0 . 12 , respectively; p<0 . 0001 ) . Thus , in the absence of Mph1 , Srs2 remains important for generating the bidirectional hDNA class of NCOs , while Sgs1 is dispensable ( see above ) . The pro-recombination role of Srs2 in the gap-repair assay , which is specific for NCO events , could reflect its ability to unwind duplex DNA and/or its ability to remove Rad51 from nucleoprotein filaments; we will refer to these as its helicase and strippase activities , respectively . To examine the relevance of each activity to NCO production , we used the strippase-deficient srs2-860 allele , which truncates the protein and eliminates the Rad51-interaction domain [36] . If only the helicase activity of Srs2 is important , then the efficiency of NCOs in the srs2-860 strain is expected to be the same as in the WT background . If the strippase activity of Srs2 is relevant , however , then the efficiency of NCOs should be reduced in the srs2-860 strain . The mean His+∶Leu+ ratio decreased 20% in the srs2-860 strain relative to WT ( p = 0 . 0051 ) , but was nevertheless much greater than in the srs2Δ strain ( 0 . 80 and 0 . 31 , respectively; p = 0 . 001 ) . The srs2-860 allele had a similar , intermediate effect on mean NCO production when compared to WT ( 0 . 39 and 0 . 91 , respectively; p = 0 . 0006 ) or srs2Δ ( 0 . 39 and 0 . 23 , respectively; p<0 . 0001 ) . This intermediate effect extended to both the unidirectional ( p = 0 . 004 and p<0 . 0001 when compared to WT and srs2Δ , respectively ) and bidirectional hDNA classes of NCOs ( p = 0 . 006 and p<0 . 0001 when compared to WT and srs2Δ , respectively ) . By contrast , the mean level of COs was elevated to 0 . 12 in the srs2-860 strain , which was significantly higher than COs in either WT ( 0 . 09; p = 0 . 022 ) or srs2Δ ( 0 . 08; p<0 . 0001 ) . This suggests that the strippase activity of Srs2 may promote NCOs at the expense of COs during gap repair .
Though an mph1Δ , srs2Δ or sgs1Δ mutant accumulates proportionally more COs during HO-induced DSB repair , an important distinction is that NCOs are diverted into COs only in an mph1Δ background [12] . The mph1Δ results reported here are consistent with the HO data , with the 2-fold increase in CO events being accompanied by a coordinate decrease in SDSA-type NCOs . While there are no data from higher eukaryotes that address CO-NCO distribution in the absence of Mph1 , Schizosaccharomyces pombe mutants defective in the ortholog Fml1 similarly have normal efficiencies of gap repair accompanied by a strong proportional increase in COs [37] . These data place Mph1 at a key decision point during mitotic DSB repair , with its activity largely determining whether an intermediate has the potential to become a CO event . It indeed has been argued that Mph1 is the only one of the three helicases examined here that can unwind a mobile D-loop created by Rad51 [13] , [38] . Though this suggests that Mph1 is the primary activity that dismantles D-loops in WT cells , NCO products with unidirectional hDNA are nevertheless produced efficiently in its absence . In the mph1Δ background , D-loop collapse could involve Srs2 and/or Sgs1 , or some other helicase ( Figure 5 ) . As we reported previously using a similar gap-repair assay [8] , there was a reduction in total gap-repair efficiency upon loss of Sgs1 . The 2-fold increase in the proportion of COs observed in an sgs1Δ background reflected a specific loss of NCOs , with no corresponding gain in COs . The proportional gain in COs among recombination products is consistent with previous studies of yeast spontaneous and DSB-induced recombination [8] , [12] , [34] , [39] , as well as with studies in mammalian cells [40] and Drosophila [41] . In a time-course analysis of HO-induced DSB repair in yeast , the appearance of early NCOs , which were assumed to reflect SDSA , was not affected by loss of Sgs1 , leading to the suggestion that Sgs1 specifically promotes a later dHJ dissolution pathway [12] . There have been numerous in vitro and in vivo studies that support a role for Sgs1 , together with Top3 and Rmi1 , in dHJ dissolution . In vitro , for example , human and Drosophila BLM-TopIIIα and yeast Sgs1-Top3 can dissolve dHJs [27] , [42] , [43] . In vivo , meiotic joint molecules formed in an sgs1-ΔC795 mutant persist longer when cells are returned to mitotic growth , and their eventual resolution leads to proportionally more COs , consistent with dHJ dissolution by Sgs1 [44] . Furthermore , exposure of sgs1 cells to DNA damage is associated with an accumulation of recombination-dependent X-shaped molecules [28] , [45] . These molecules disappear when DNA is treated with bacterial HJ resolvases , suggesting that they correspond to fully-ligated HJs that are normally dissolved by the Sgs1-Top3-Rmi1 ( STR ) complex [28] . We have confirmed here that loss of Sgs1 is associated with a decrease in the specific class of NCOs predicted as the product of STR-driven dHJ dissolution: NCOs with bidirectional hDNA on the repaired molecule ( Figure 5 ) . A reduction in the bidirectional hDNA pattern predicted by dHJ dissolution was expected upon loss of Sgs1 , but a similar reduction in the unidirectional hDNA diagnostic of SDSA was not ( Figure 5 ) . Studies in Drosophila , however , have suggested a role for BLM in dismantling D-loops formed following P element excision [46] , [47] , and there is supporting biochemical evidence that human BLM binds and dismantles D-loops [48] , [49] . Recent data suggest that the role of STR in D-loop disruption might also involve migration of the back end of the D-loop and/or rewinding of the invaded duplex [49] , [50] . Either of these additional activities could explain why neither Mph1 nor Srs2 can fully substitute for Sgs1 during gap repair . We note that the reduction in unidirectional hDNA was no greater in the mph1Δ sgs1Δ double mutant than in the sgs1Δ single mutant , suggesting that Sgs1 and Mph1 work in the same pathway to promote SDSA . An interesting possibility is that , as the Mph1 helicase unwinds the invading strand , the catenating activity of STR is required to “rewind” the duplex that was part of the D-loop [50] . With regard to an SDSA-specific role for STR inferred here based on hDNA patterns , recent studies suggest that it is Sgs1 that promotes early SDSA-type NCOs in meiosis [51] , [52] . A pro-recombination role for Srs2 has been demonstrated in physical studies of HO-induced mitotic recombination [12] , [20] and in a plasmid-based gap-repair assay similar to the one used here [8] . Furthermore , Srs2 loss has been associated with an increase in the proportion of COs produced during HO-induced DSB repair [12] , during gap repair [8] and during spontaneous recombination [53] . Because only the early-appearing NCOs were lost following HO induction in an srs2Δ background , it was suggested that Srs2 promotes the NCO-specific SDSA [12] . In the current analyses , the efficiency of gap repair decreased 3-fold upon loss of Srs2 , reflecting a specific loss of NCO events with no effect on CO events . Analyses of hDNA position in NCOs revealed a corresponding 4–5 fold reduction in SDSA-type products in the srs2Δ background , as well as a smaller , 2-fold decrease in bidirectional hDNA . These molecular data support that conclusion that SDSA is an early , Srs2-dependent pathway of DSB repair [12] . As inferred in HO assays [12] , the pro-SDSA role of Srs2 in our gap-repair assay was much stronger than that of Sgs1 . The most straightforward way for a helicase to promote SDSA is through the dismantling of an extended D-loop ( Figure 5 ) , but whether this is the most relevant function of Srs2 in vivo has been the subject of debate [13] , [21] . The srs2-860 allele used here encodes a protein that retains helicase activity but does not interact with Rad51 , and hence is defective in the strippase activity that removes Rad51 from DNA [36] . A comparison of the overall gap-repair efficiencies in WT , srs2Δ and srs2-860 strains suggests that the pro-recombination role of Srs2 likely reflects both activities . Importantly , the significant decrease in NCOs suggests that the disruption of Rad51 nucleoprotein filaments by Srs2 [18] , [19] helps promote SDSA . This activity of Srs2 could be relevant for removing Rad51 from the 2nd end of the resected break to allow the requisite Rad52-dependent annealing reaction and/or it could be necessary for D-loop disruption when Rad51 remains bound to duplex DNA within the D-loop ( Figure 5 ) . Either role would be consistent with the observation that overexpression of Rad51 in an srs2Δ background almost completely eliminates NCOs [12] . A late role for Srs2 in promoting 2nd-end annealing , however , seems more consistent with the observation that the pro-recombination for Srs2 is relaxed in the absence of Mph1 ( Table 3 ) . It should be noted that the 2nd-end engagement required to generate a dHJ could occur either through an annealing reaction or through a second , Rad51-catalyzed strand invasion event . The latter would be expected to be more efficient in an srs2-860 background , which could explain the small increase in COs observed in this strain ( Figure 4 ) . Finally , the observation that neither Mph1 nor Sgs1 can substitute fully for Srs2 during DSB repair is consistent with a unique activity for this helicase . The loss of the SDSA pattern of hDNA among NCO events in the srs2Δ background was expected based on prior studies , but the accompanying 2-fold reduction in the bidirectional hDNA pattern assumed to be diagnostic of dHJ dissolution was not . The unwinding of a 4-way structure that mimics an HJ by yeast Srs2 has been examined , and it was concluded that it is a no better substrate for yeast Srs2 than blunt-end duplex DNA [21] . A recent analysis of the putative homolog of Srs2 from Arabidopsis thaliana , however , reported that the helicase has significant activity against a nicked HJ [54] . We thus speculate that , in addition to a D-loop , a nicked sHJ or nicked dHJ is a cognate substrate for Srs2 in vivo ( Figure 5 ) . A nicked dHJ can be formed by a mechanism analogous to that assumed to occur when SDSA-mediated repair of a gapped plasmid requires that each end invade a template on a different chromosome [55] , [56] . In the case of the assay used here , independent invasion of the same repair template by each end , followed by extension and unwinding - basically two independent SDSA reactions - would produce a repaired plasmid with bidirectional hDNA . A similar mechanism was previously invoked in a study examining the effect of homology length on the CO-NCO outcome during repair of HO-induced DSBs [57] . It should be noted that the contributions of Srs2 and Sgs1 to bidirectional hDNA among NCO products appear to be independent ( each is required for ∼50% of these events in WT; Figure 4 ) , which would be consistent with these helicases working on different structures: Srs2 on nicked single or double HJs , and Sgs1 only on fully ligated dHJs . Given the numerous roles that have been identified for the Mph1 , Sgs1 and Srs2 helicases in basic DNA transactions , determining their specific activities once HR has initiated has been problematic . Although a gapped plasmid was used here to model DSB repair , it should be noted that both the efficiencies of repair and the distributions of repair products are completely consistent with those reported in HO-initiated chromosomal assays . It remains possible , however , that the roles of Mph1 , Sgs1 and Srs2 inferred here may be specific to situations where the total homology between substrates is limited , a situation recently described for the Rad1-Rad10 endonuclease [58] . Through monitoring of hDNA among NCO products , the results presented here provide molecular confirmation that both Srs2 and Mph1 promote SDSA and that Sgs1 likely participates in the dissolution of dHJs . Importantly , additional roles for Sgs1 in promoting SDSA and for Srs2 in dismantling HJs have been inferred , broadening the potential range of activities of these helicases in vivo .
Cells were grown nonselectively in YEPD ( 1% Bacto-yeast extract , 2% Bacto peptone , 2% dextrose ) supplemented with 500 µg/mL adenine hemisulfate . Selective growth was on synthetic complete ( SC ) medium lacking the appropriate nutrient . Ura− segregants were identified on SC plates containing 0 . 1% 5-fluoroorotic acid ( 5-FOA ) . All growth was at 30°C . A complete strain list is provided in Table S2 . Helicase-defective derivatives of the haploid mlh1Δ strain SJR2157 , which contains the diverged gap-repair template [8] , were constructed by targeted gene deletion . The substrate for gap repair was generated by BssHII linearization of either the ARS-containing plasmid pSR987 [35] or the CEN/ARS-containing plasmid pSR1015 . pSR1015 was constructed by inserting an XhoI/XbaI HIS3 fragment from pSR987 into XhoI/XbaI-digested pRS316 [59] . The OD600 of the exponentially growing cultures , each of which was derived from an independent colony , was measured to determine cell density . Six cultures with OD600 values between 0 . 7 and 1 . 0 were selected for parallel transformation using the protocol described previously [8] . Each experiment was repeated with at least six more cultures derived from independent colonies . His+ and Leu+ colonies were counted 5 days after selective plating . To avoid bias when partitioning recombinants into CO and NCO events , plates were divided into sections and every His+ transformant within a given section was picked . His+ transformants were frozen in 20% glycerol without prior purification , and an aliquot was grown nonselectively in YEPD prior to spotting an appropriate dilution on 5-FOA . Spots with full growth on 5-FOA after 3 days were scored as NCO events; those with no growth or only a few papillae were scored as CO events . Transformations with pSR1015 and selection of His+ transformants were performed as described above . An aliquot of the frozen stock of each His+ colony was transferred to SC-his liquid medium and grown to saturation in 96-well microtiter plates . Following DNA extraction ( http://jinks-robertsonlab . duhs . duke . edu/protocols/yeast_prep . html ) , the plasmid and chromosomal alleles were separately amplified with the appropriate primers ( Table S3 ) , and products were sequenced by the Duke Comprehensive Cancer Center DNA Analysis Facility . Sequence chromatograms were examined visually to detect the double peaks indicative of hDNA at a given SNP . Samples with only gene conversion or with no detectable sequence transfer were not included in further analysis because it was not possible to infer a recombination intermediate . Two different mixes of linear plasmids were used during the course of the transformation experiments reported here . The absolute His+∶Leu+ ratios obtained when using these mixes differed for the WT strain ( mean ratios of 1 . 57 and 1 . 05 ) , and depending on which mix was used for a specific mutant background , the individual transformation values were normalized to the corresponding WT mean . For the mph1Δ and srs2Δ strains , His+∶Leu+ ratios were generated with both mixes; for other mutant backgrounds , only a single mix was used in transformation experiments . For data analysis , normalized values of all individual transformations were pooled . The mean His+∶Leu+ ratios for the various events in different strain backgrounds were compared using a two-tailed Student's t-test ( http://vassarstats . net/ ) , and p<0 . 05 is considered significant . | Chromosomal damage that occurs during normal cell division can be repaired using an intact sequence elsewhere in the genome as a template . This process , termed homologous recombination , is crucial for the repair of a particularly deleterious lesion , the DNA double-strand break . Although recombination is a repair process , it can also lead to exchanges of genetic material , generating crossovers ( COs ) between the involved chromosomes . Repair of the break without exchange of flanking DNA is called a noncrossover ( NCO ) . As COs can uncover recessive mutations or result in large-scale genome rearrangements , understanding how the CO-NCO outcome is regulated is critical to issues of genome stability . The current study examines the distinctive mechanisms whereby three yeast DNA helicases—Mph1 , Sgs1 , and Srs2—contribute to the repair of a DNA double-strand break . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"molecular",
"genetics",
"biology",
"genetics",
"and",
"genomics",
"gene",
"function"
] | 2013 | Heteroduplex DNA Position Defines the Roles of the Sgs1, Srs2, and Mph1 Helicases in Promoting Distinct Recombination Outcomes |
Severe leptospirosis is frequently complicated by a hemorrhagic diathesis , of which the pathogenesis is still largely unknown . Thrombocytopenia is common , but often not to the degree that spontaneous bleeding is expected . We hypothesized that the hemorrhagic complications are not only related to thrombocytopenia , but also to platelet dysfunction , and that increased binding of von Willebrand factor ( VWF ) to platelets is involved in both platelet dysfunction and increased platelet clearance . A prospective study was carried out in Semarang , Indonesia , enrolling 33 hospitalized patients with probable leptospirosis , of whom 15 developed clinical bleeding , and 25 healthy controls . Platelet activation and reactivity were determined using flow cytometry by measuring the expression of P-selectin and activation of the αIIbβ3 integrin by the binding of fibrinogen in unstimulated samples and after ex vivo stimulation by the platelet agonists adenosine-diphosphate ( ADP ) and thrombin-receptor activating peptide ( TRAP ) . Platelet-VWF binding , before and after VWF stimulation by ristocetin , as well as plasma levels of VWF , active VWF , the VWF-inactivating enzyme ADAMTS13 , thrombin-antithrombin complexes ( TAT ) and P-selectin were also measured . Bleeding complications were graded using the WHO bleeding scale . Our study revealed that platelet activation , with a secondary platelet dysfunction , is a feature of patients with probable leptospirosis , especially in those with bleeding manifestations . There was a significant inverse correlation of bleeding score with TRAP-stimulated P-selectin and platelet-fibrinogen binding ( R = -0 . 72 , P = 0 . 003 and R = -0 . 66 , P = 0 . 01 , respectively ) but not with platelet count . Patients with bleeding also had a significantly higher platelet-VWF binding . Platelet counts were inversely correlated with platelet-VWF binding ( R = -0 . 74; P = 0 . 0009 . There were no correlations between platelet-VWF binding and the degree of platelet dysfunction , suggesting that increased platelet-VWF binding does not directly interfere with the platelet αIIbβ3 signaling pathway in patients with probable leptospirosis . Platelet dysfunction is common in probable leptospirosis patients with manifest bleeding . Increased VWF-platelet binding may contribute to the activation and clearance of platelets .
Leptospirosis is a zoonotic disease of global importance caused by the pathogenic spirochaetes of the genus Leptospira [1 , 2] . A modeling exercise by the World Health Organization's ( WHO's ) Leptospirosis Burden Epidemiology Group , estimated that 873 , 000 cases and 48 , 600 deaths occur worldwide each year [3] . The clinical manifestations of leptospirosis range from a mild , self-limited febrile illness to a fulminant life-threatening illness with multi-organ failure [1 , 2] . Bleeding complications are common in severe leptospirosis , being reported in up to 60% of all hospitalized patients . Although the majority of bleeding events are mild , some patients may develop severe gastrointestinal or pulmonary hemorrhage , the latter having an alarmingly high mortality of >50% [4 , 5] . The pathophysiological mechanisms responsible for bleeding remain incompletely understood . Thrombocytopenia is frequently observed and is associated with poor outcome [6] , but its severity is not to the extent that spontaneous bleeding is expected [7] . Platelet dysfunction might also contribute to bleeding . Measuring platelet function in thrombocytopenic conditions is technically demanding as commonly used techniques such as light transmission aggregometry are not useful [8] . To the best of our knowledge , no studies on platelet function in leptospirosis have been performed . Platelets are involved in primary hemostasis , wherein they aggregate and form a plug to seal off vascular leakage . Platelet adhesion is initiated by the binding of GPIbα and GPVI receptors on platelets to VWF on endothelial surface and collagen , respectively , of which both are exposed from the subendothelium after vascular injury . Platelets are then activated , and adhesion and aggregation is strengthened via the platelet αIIbβ3 receptor binding with fibrinogen and von Willebrand factor ( VWF ) . The binding of VWF to the glycoprotein ( GP ) -1b receptor on platelets is a well-known platelet clearance mechanism [9] . VWF is a multimeric glycoprotein that predominantly originates from the endothelium . Its main function is the recruitment of platelets to sites of vascular injury . Under normal conditions , most of the VWF circulates in non-platelet binding form . VWF may however undergo a conformational change to a more active , platelet-binding form facilitating platelet aggregation [10] . An increase in this so-called ‘active’ VWF has been described in a number of infectious and non-infectious diseases [11–13] . A deficiency in ADAMTS13 ( a disintegrin and metalloproteinase thrombospondin type 1 motif , member 13 ) , an enzyme that cleaves large VWF multimers into their inactive conformation , may also lead to excessive intravascular platelet agglutination [14] . Von Willebrand disease type 2B is characterized by excessive VWF binding to platelets . Recently , it was reported that this excessive binding not only leads to thrombocytopenia , but also to thrombocytopathy via inhibition of the platelet αIIbβ3 , an integrin that primarily mediates platelet aggregation through binding of fibrinogen [15] . In addition , excessive platelet activation may also lead to exhausted and hence dysfunctional platelets [16] . We previously found in patients with dengue hemorrhagic fever that circulating platelets were activated , but less reactive to ex vivo stimulation with platelet agonists [17] . We hypothesized that similar platelet changes also occur in other hemorrhagic infectious diseases , including in severe leptospirosis . Specifically , we hypothesized that circulating platelets in severe leptospirosis are activated and have bound more VWF and that both these processes contribute to thrombocytopenia and platelet dysfunction , especially in those with bleeding complications . We therefore characterized VWF binding to platelets , platelet activation and platelet reactivity using flow cytometry in adult Indonesian patients with probable leptospirosis and determined whether these parameters were related to bleeding complications . In addition , we determined the plasma levels of VWF , active VWF , P-selectin , the plasma coagulation factor thrombin-antithrombin complexes ( TAT ) and ADAMTS13 activity .
The study was approved by the Ethics Committee of the Faculty of Medicine Diponegoro University and Dr Kariadi Hospital , Semarang , Indonesia . Upon participation , all participants signed an informed consent form . We carried out a prospective study at the Department of Internal Medicine of Dr . Kariadi Hospital , Semarang , Indonesia , between December 2013-March 2014 . We enrolled adult patients admitted with a high clinical suspicion of leptospirosis using the case definition of the World Health Organization South East Asia Regional Office supported by a positive result of Leptospira IgM lateral flow ( Pakar Biomedika , Indonesia ) , classifying them as probable leptospirosis [18] . The Leptospira IgM lateral flow test had a reported sensitivity and specificity of 85 . 6% and 96 . 2% , respectively [19] , while another study by Goris et . al . reported a specificity of 95% [20] . Clinical diagnosis of leptospirosis was confirmed in a single acute sample by microscopic agglutination test ( MAT ) with a panel consisting of 31 serovars ( 28 pathogenic serovars: Australis , Bratislava , Autumnalis , Rachmati , Ballum , Castellonis , Bataviae , Benjamini , Whitcombi , Cynopteri , Grippotyphosa , Hebdomadis , Copenhageni , Hardjo , Icterohaemorrhagiae , Lai , Naam , Coxi , Javanica , Panama , Pomona , Proechimys , Pyrogenes , Sarmin , Saxkoebing , Sejroe , Shermani , Tarassovi; three non-pathogenic serovars: Andamana , Patoc and Semaranga ) . A titre of ≥1/320 on a single sample was considered positive . Additionally , dengue IgM/IgG serology was tested in all patients as part of the protocol in patients with clinical suspicion of leptospirosis in the hospital where this study was performed , and patients with a positive dengue IgM were not enrolled . Healthy controls were all tested negative for rapid Leptospira IgM lateral flow . All diagnostic tests were performed at the National Reference Laboratory for Leptospira , Dr . Kariadi Hospital , Semarang , Indonesia . Patients and healthy controls were included after written informed consent was obtained . Blood samples were obtained on inclusion day ( day 1 ) and upon follow up ( day 4 ) . The study is approved by the ethical committee of the Faculty of Medicine Diponegoro University-Dr . Kariadi Hospital , Semarang , Indonesia . Whole blood was collected from patients by using venipuncture from the antecubital vein into citrate-anticoagulated tubes ( 3 . 2%; BD Vacutainer , Becton Dickinson ) . All samples were processed within one hour after blood collection . Platelet activation and reactivity were determined by flow cytometry using a method previously described [21 , 22] . In short , the platelet membrane expression of P-selectin ( CD62P ) and platelet-fibrinogen binding , which correspond with platelet degranulation and aggregation , were determined in unstimulated whole blood and in whole blood stimulated 20 min with the platelet agonists adenosine-diphosphate ( ADP; 7 . 8 μM and 31 . 2 μM ) or thrombin receptor-activating peptide ( TRAP; 39 μM and 625 μM ) diluted in HEPES-buffered saline . Saturating concentrations of the following monoclonal antibodies were used: PE-labeled anti-CD62P ( Bio-Legend , San Diego , USA ) , FITC-labeled anti-fibrinogen ( DAKO Ltd . , High Wycombe , UK ) , and PC7-labeled anti-CD61 ( platelet identification marker; Beckman Coulter , France ) . Platelets were gated based on their forward- and sideward-scatter ( FSC/SSC ) properties and positivity for CD61 , which was defined as a median fluorescence intensity ( MFI ) exceeding the MFI of the matched isotype control . The binding of VWF to platelets was determined by adding whole blood sample to a mixture of HEPES-buffered saline and saturating concentrations of FITC-labeled anti-von Willebrand factor ( Abcam , Cambridge , UK ) and PC7-labeled anti-CD61 , with or without the addition of ristocetin ( 0 , 84 mg/ml and 1 , 5 mg/ml ) . After incubation for 20 minutes at room temperature , a fixative solution ( 0 . 2% paraformaldehyde ) was added and samples were analyzed with a BD FACS Canto II flow cytometer ( Becton Dickinson , USA ) . Next , the MFI of CD62P , fibrinogen and VWF on CD61-positive events were determined . Platelet-poor plasma was obtained from citrate-anticoagulated whole blood by centrifugation ( 1500 g without brake , 15 min , 20ehyde ) was added and thrombin-antithrombin ( TAT ) complexes were subsequently measured with enzyme-linked immunosorbent assay ( ELISA ) as previously described [24] . Sheep anti-human thrombin ( SAHT-AP , SAHT-HRP ) antibodies were purchased from Kordia/Affinity Biologicals , USA . Plasma VWF concentrations were determined with ELISA as described previously [23] . Active VWF was quantified by ELISA using a nanobody ( AU/VWFa-11 ) that recognizes the GPIb binding configuration of VWF , as described previously [24] . We express the relative amount of VWF that circulates in its active , platelet binding conformation by using the term VWF activation factor . VWF activation factor of normal pooled plasma was referred to as 1 . ADAMTS13 activity was quantified using the fluorescence resonance energy transfer ( FRETS ) assay ( Peptides International , Lexington , USA ) , whereby the ADAMTS13 activity of normal pool plasma was set at 100% , and the values obtained in study participant samples were expressed as percentage of normal pool plasma [25] . Differences in subject characteristics were compared with analysis of variance ( ANOVA ) with posttests for comparing more than two groups , and with Mann-Whitney U-test or chi-square test for comparing the two patient groups . Data are presented as median with interquartile range ( IQR ) unless stated otherwise . Relationships between parameters were assessed with the Spearman correlation coefficient . All analyses were performed with SPSS version 20 ( SPSS , Inc . , Chicago , Illinois , USA ) . P values less than 0 . 05 were considered statistically significant .
Thirty-three consecutive hospitalized patients with probable leptospirosis were enrolled together with 25 healthy controls . Twelve ( 36% ) of these patients had a positive result of the MAT ( titer >1/320 ) . All patients were tested negative for dengue IgM , while 29 had a positive dengue IgG ( 12 in the bleeders group and 17 in the non-bleeders group ) . Characteristics of participants are presented in Table 1 . Fifteen patients ( 46% ) presented with or developed clinical relevant bleeding manifestations during hospitalization . The most common were gastrointestinal ( n = 10 ) and genitourinary ( n = 8 ) bleeding events . The severity of bleeding events in patients was graded using the WHO bleeding scale [26–28] . Ten out of 15 patients ( 67% ) who experienced clinical bleeding developed Grade 2 bleeding ( i . e . epistaxis with a total duration of all episodes in previous 24 hours of >30 minutes , grossly visible blood in urine , stool or emesis ) , while 4 ( 27% ) developed that of Grade 1 ( i . e . epistaxis with a total duration in previous 24 hours of <30 minutes , microscopic hematuria and petechiae ) . There was one patient ( 7% ) with Grade 3 bleeding whose hematemesis and melena caused hemodynamic instability . One Grade 1 ( 7% ) and three ( 20% ) Grade 2 patients died during hospitalization . Median ( IQR ) platelet count was significantly lower in the group of bleeders compared with the non-bleeders ( 105 , 56-152x109/l vs . 123 , 106–183 x109/l; P = 0 . 003 ) . Five patients had a platelet count lower than 50 x109/l ( platelet counts of four bleeders: 12 x109/l , 17 x109/l , 30 x109/l , 30 x109/l; platelet count of one non-bleeder: 49 x109/l ) . Additionally , the hemoglobin levels were lower in the bleeders group than the non-bleeders group ( 10 . 9 , 9 . 4–13 . 2 gr/dl vs . 14 . 8 , 13 . 9–16 . 5 gr/dl; P = 0 . 004 ) , with six patients having hemoglobin levels below 10gr/dl ( hemoglobin levels of five bleeders: 8 . 2 gr/dl , 8 . 9 gr/dl , 9 . 1 gr/dl , 9 . 4 gr/dl , 9 . 8 gr/dl; hemoglobin level of one non-bleeder: 9 . 5 gr/dl ) . Leukopenia is a common presentation of dengue . However , none of the patients in our patient cohort had leukopenia at presentation , with a median ( interquartile range ) of leukocyte of 14 . 5 x109/l ( 9 . 4 x109/l -17 . 4 x109/l ) . Fig 1A shows the fibrinogen binding to the activated αIIbβ3 receptor and expression of P-selectin on platelets . Patients with bleeding had significantly higher platelet-fibrinogen binding ( MFI 2601 , 1726–2938 vs . 1577 , 1460–1752; P = 0 . 001 ) and P-selectin ( MFI 1095 , 798–1685 vs . 654 , 575–1056; P = 0 . 002 ) in unstimulated blood samples than controls , suggesting increased platelet activation . The non-bleeding patients also had significantly higher membrane P-selectin expression compared to controls ( MFI 1179 , 910–1444; P = 0 . 003 ) . Platelet reactivity was assessed by ex vivo stimulation of whole blood with two concentrations of the platelet agonists ADP or TRAP . In contrast to the findings in unstimulated samples , P-selectin expression and fibrinogen binding in response to TRAP or ADP were lower in the bleeders compared with non-bleeders and controls , suggestive of platelet dysfunction . A reduction in platelet reactivity , albeit to a lesser extent , was also found in the non-bleeders in response to TRAP . Interestingly , whereas bleeding score was not associated with platelet count ( Fig 1B ) , an inverse relation was present for bleeding score with P-selectin expression and fibrinogen binding to ex vivo stimulation with TRAP ( R = -0 . 72; P = 0 . 003 for P-selectin and R = -0 . 66; P = 0 . 01 for fibrinogen; Fig 1B ) and ADP ( R = -0 . 41; P = 0 . 01 and R = -0 . 63; P = 0 . 03 , respectively; shown in S1 Fig ) . The flow cytometric findings of platelet activation in bleeders were supported by measurement of plasma concentrations of soluble P-selectin , which was significantly higher in bleeders compared to both the non-bleeders and healthy controls . Plasma P-selectin in the non-bleeders was comparable with that of the controls . Overall , leptospirosis patients also had significantly higher plasma TAT concentrations , suggesting activation of the plasma coagulation pathway , with the highest concentrations found in the bleeders ( Fig 1C ) . Comparing MAT-positive with MAT-negative patients , no differences were found in platelet activation status as well as platelet reactivity in the bleeders and non-bleeders ( Fig 2 ) . There was a trend for lower platelet count in the MAT-positive group , with a median ( IQR ) of 135 x109/l ( 50 x109/l -180 x109/l ) , compared to the MAT-negative group ( 134 x109/l , 105 x109/l -244 x109/l; P = 0 . 09 ) . Additionally , there were no statistically significant differences in the bilirubin , AST , ALT , ureum and creatinine levels between the MAT-positive and MAT-negative patients . In further analyses , MAT-positive and MAT-negative cases were analyzed as a single group of bleeders or non-bleeders . Follow-up data at day four were available for 11 bleeders and 10 non-bleeders . Bleeders , but not the non-bleeders , remained having a significantly reduced platelet reactivity in the follow-up on day 4 compared to healthy controls ( S2A Fig ) . Plasma P-selectin also remained significantly increased at day 4 in bleeders ( S2B Fig ) . Binding of VWF to platelets is associated with platelet clearance , platelet activation and also possibly platelet dysfunction [15 , 29] . We determined platelet-VWF binding using flow cytometry and measured plasma VWF levels , VWF activation factor and ADAMTS13 activity . Bleeders had a significantly higher platelet-VWF binding ( MFI 9069 , 7470–10554 ) than non-bleeders ( MFI 6999 , 4707–8250; P = 0 . 01 ) and healthy controls ( MFI 7296 , 6877–7867; P = 0 . 002 ) ( Fig 3A ) . Upon activation of VWF by ristocetin , the bleeding patients demonstrated the highest increase in platelet-VWF binding ( Fig 3B ) . These flow cytometric findings were consistent with the observation that bleeders had the highest plasma VWF concentration ( Fig 3C ) and that the VWF activation factor was about twofold higher in both the bleeding and non-bleeding leptospirosis patients than in healthy controls , indicating that a higher amount of the circulating VWF was in an active , platelet-binding conformation ( Fig 3D ) . ADAMTS13 functions as a natural regulator that de-activates the VWF by proteolysis [30] and can be consumed by high levels of circulating VWF [12] . ADAMTS13 activity levels were decreased in leptospirosis patients and were lowest in bleeders ( Fig 3E ) . Three patients in the bleeders group had ADAMTS13 activity level lower than 10% . An ADAMTS13 activity below 50% was found in 11 ( 73% ) patients in the bleeders group and only in three ( 25% ) patients in the non-bleeders group . In the leptospirosis group as a whole , platelet-VWF binding correlated positively with the VWF activation factor ( R = 0 . 62; P = 0 . 001; Fig 3F ) . Additionally , whereas ADAMTS13 activity did not correlate with VWF activation factor , it had an inverse correlation ( R = -0 . 65; P = 0 . 0009 ) with plasma VWF levels , suggesting consumption of ADAMTS13 by VWF ( data for correlation between ADAMTS13 and plasma VWF shown in Fig 3F ) . These correlations were consistent across the bleeders and non-bleeders group , as shown in S3A and S3B Fig . Additional analyses of the MAT-positive and MAT-negative subgroups are presented in S4 Fig while data on follow-up measurements on day 4 are presented in S5 Fig . Next , we checked associations between parameters to explore possible mechanisms underlying the leptospirosis-associated thrombocytopenia and platelet dysfunction ( Fig 4 ) . First , there was a strong inverse correlation of platelet count with platelet-VWF binding and to a lesser extent with the platelet membrane expression of P-selectin ( Fig 4A and 4B , day 4 data presented in S5 Fig ) . In contrast , plasma levels of the coagulation activation marker TAT complex did not correlate with platelet numbers ( Fig 4C ) . Next , we explored whether platelet-VWF binding inhibited the platelet αIIbβ3 signaling pathway , as recently reported for von Willebrand disease type 2B [15] . However , in contrast to our hypothesis , platelet-VWF binding correlated positively with platelet-fibrinogen binding in unstimulated samples ( Fig 4D; Spearman R = 0 . 42; P = 0 . 02 ) , and there was a trend for a positive correlation with high dose TRAP- and ADP-stimulated fibrinogen binding ( R = 0 . 33; P = 0 . 19 for ADP and R = 0 . 30; P = 0 . 13 for TRAP ) . Another possible cause of platelet dysfunction is uremia due to the presence of uremic toxins in the blood [31] . Although we did observe a trend for higher urea levels in bleeders compared to non-bleeders ( P = 0 . 06 ) , we did not find any associations between ureum levels with either P-selectin at baseline ( Spearman R = -0 . 17 , P = 0 . 36 ) or upon stimulation with ADP ( R = -0 . 16 , P = 0 . 43 ) or TRAP ( R = -0 . 11 , P = 0 . 58 )
Our study reveals four important findings . First , platelet activation is a feature of severe , probable leptospirosis , especially in cases with bleeding manifestations . Second , bleeding complications are predominantly associated with platelet dysfunction rather than absolute platelet count . Third , circulating platelets in probable leptospirosis patients bind more VWF and this has a strong negative association with platelet number , in contrast with the plasma coagulation marker TAT complexes . Fourth , platelet-VWF binding did not diminish with agonist-induced platelet-fibrinogen binding ( Fig 4D ) , disproving our hypothesis that increased platelet-VWF binding underlies the inhibition of the platelet αIIbβ3 signaling pathway , as recently reported for von Willebrand disease type 2B [15] . To the best of our knowledge , this is the first study measuring platelet activation and reactivity in patients with probable leptospirosis . Platelet function studies are logistically challenging , as blood samples need to be processed without delay . In conditions with thrombocytopenia , aggregometry is also less reliable and flow cytometry-based assays are preferred [8] . The findings of our study are consistent with those from an experimental leptospirosis guinea pig model in which platelets were found in hepatic sinusoids [32] and in which thrombocytopenia was not related to disseminated intravascular coagulation . Another animal study , using a virulent serovar of Leptospira interrogans in gerbils , reported increased levels of platelet-activating factor acetylhydrolase ( PAF-AH ) , which might contribute to inhibition of platelet activation [33] . Our observation that platelet function , rather than the absolute platelet count , determined the bleeding risk is consistent with increasing evidence from patients with immune thrombocytopenic purpura ( ITP ) that has identified platelet function as an important determinant of bleeding risk [34–36] . Some of the platelet parameters did not show statistically significant differences between the bleeding and non-bleeding groups . For example , platelet reactivity to ADP stimulation was lower in those with bleeding , but this did not reach statistical significance ( Fig 1A ) . Except for the small sample size , lack of statistical difference may also reflect the fact that factors other than platelet dysfunction may contribute to bleeding , including inflammation , endotheliopathy and coagulopathy . High plasma VWF levels , together with elevations in other endothelial cell activation markers , were recently reported in patients with leptospirosis [37] . Our findings add to this by showing that the circulating VWF is in an active , GPIbα-binding conformation , and that circulating platelets indeed have more VWF on their surface . Most VWF is derived from endothelial cells . However , platelets also contain VWF in their granules [38] and to what extent this contributes to the increased VWF binding on the platelet membrane is unknown . We also found a concurrent decrease in ADAMTS13 activity levels . This enzyme regulates the multimeric size and function of VWF through the cleavage of VWF within the A2 domain [39] . A severe reduction in ADAMTS13 activity as a result of auto-antibodies is a hallmark of the rare disease thrombotic thrombocytopenic purpura ( TTP ) [40] . Cases of leptospirosis-associated TTP have also been described , including that with severely reduced ADAMTS13 activity [41 , 42] . Infections may lead to significant reductions in ADAMTS13 as a result of different mechanisms , as recently reviewed by Schwameis [43] . Multiple studies have shown that conditions with increased VWF release are associated with secondary ADAMTS13 consumption , such as in severe systemic infections [44] and after desmopressin-induced VWF release [45] . Inhibition of ADAMTS13 activity can also occur due to inflammation-induced IL-6 release [46] or proteolytic cleavage of ADAMTS13 by neutrophils [47] . And lastly , competition of ADAMTS13 with thrombospondin-1 for the interaction with the VWF-A3 domain may slow the proteolysis of ultra large ( UL ) -VWF multimers [43] . The mechanisms underlying the observed platelet activation and platelet dysfunction in our study population remain to be elucidated . It is unknown whether pathogenic Leptospira interrogans strains are able to directly interact with and activate platelets . Leptospiral lipopolysaccharide ( LPS ) was shown to be a ligand of Toll-like receptor ( TLR ) -2 and TLR4 in human whole blood and in mice [48 , 49] . Platelets harbor both TLRs and the ligation of TLR-2 especially leads to a strong thrombotic platelet activating response [50] . Alternatively , increased platelet-VWF binding may activate platelets [51] . In patients with von Willebrand disease type 2B ( VWD type 2B ) , a disease characterized by gain-of-function mutations in VWF that enhance its spontaneous binding to the platelet GPIbα , increased platelet-VWF binding is associated with thrombocytopathy due to inhibition of αIIbβ3 activation [15] . We also hypothesized that this might underlie thrombocytopathy in leptospirosis , but this is unlikely with our finding of a positive correlation of platelet-VWF binding with fibrinogen binding to the activated αIIbβ3 receptor in response to platelet agonists ( Fig 4D ) . Another explanation might be that excessive platelet activation is responsible for less reactive and functional platelets . The exposure of platelets to inducers of platelet activation such as thrombin [52] or VWF [53] may lead to a partial release or incomplete degranulation of platelets , leading to impairment of their hemostatic effectiveness [16] . The combination of a higher platelet activation status of circulating platelets in the bleeding leptospirosis patients with reduced reactivity to ex vivo activation ( Fig 1A ) would certainly fit this hypothesis . A similar platelet phenotype was previously found in patients with other hemorrhagic infectious diseases , such as severe dengue [17] . In hantavirus-infected patients , platelet reactivity to agonist stimulation was significantly lower during disease compared to follow-up [54] . Impaired platelet dysfunction appears specific for these ‘hemorrhagic’ diseases and does not appear to result from more general factors such as inflammation , as most other infections increase platelet reactivity . For example , we found increased platelet reactivity using similar assays as employed in the current study in patients with sepsis due to common Gram-positive pathogens [55] , pigs with pneumococcal bacteremia [56] and HIV-infected individuals on antiretroviral therapy [22] . In addition , we did not find systemic platelet activation in volunteers participating in a controlled human malaria infection , despite developing thrombocytopenia , which may explain why malaria is rarely complicated by bleeding [57] . There was a strong inverse correlation of VWF-platelet binding with platelet number , suggesting that VWF bound to the platelet membrane is involved in platelet clearance . Increased platelet-VWF interaction in VWD type 2B results in increased platelet clearance by the liver [58] . How the VWF-platelet complexes are being cleared remains to be determined . Evidence suggests that glycans on GPIbα are critical in mediating platelet clearance via receptors containing carbohydrate-binding domains on the macrophage αMβ2 integrin and the hepatic Ashwell-Morell receptor [29 , 59 , 60] . One plausible mechanistic explanation is that the increased platelet-VWF binding results in a structural unfolding of the GPIbα extracellular domain and triggers signaling into the platelet , desialylation of the platelet surface and platelet clearance [61] . The limitations of our study include the small number of patients tested positive with the MAT and the fact that patients were included solely based on a positive result of a rapid IgM lateral flow test combined with clinical manifestations consistent with leptospirosis , classifying the cases as probable leptospirosis . Individuals in the control group were all IgM lateral flow negative . The diagnosis of leptospirosis is challenging with current gold standard tests such as the MAT being imperfect and technically demanding [19] . At the time of the study , other diagnostic tests such as an IgM ELISA or PCR were not available in our setting . It is also uncertain whether PCR would have yielded a high sensitivity in our cohort as most cases were enrolled only in the second week of illness when the PCR is frequently already negative . The low number of patients with a positive MAT might be explained by the fact that the MAT also has limited sensitivity [19] . MAT gives a large number of false negative results in the early course of infection , as IgM antibodies detectable by MAT only appear after day 8 of illness and reach the peak by week 4 [62 , 63] . MAT requires collection of paired sera at appropriate time intervals for the most accurate interpretation of results . Therefore , although it is of high value for epidemiological purposes , its value in the acute clinical setting is limited [64] . Many cases in our study could thus only be classified as probable leptospirosis . Subgroup analysis , however , did not show any differences in platelet parameters between those with a positive or negative result of the MAT . In addition , even though leptospirosis is a common cause of undifferentiated fever in Semarang , Indonesia , where our study was performed , we cannot exclude other co-infections , such as murine typhus [65] . In conclusion , we found that platelet activation and platelet dysfunction are features of patients with probable leptospirosis diagnosis that are associated with the severity of bleeding events . Circulating platelets also bind more with VWF , and although this does not explain the observed platelet dysfunction , it may play a role in platelet activation and clearance . Bleeding is a serious , life-threatening complication of leptospirosis and our findings warrant further study on the clinical utility of platelet function tests , as it is thrombocytopathy rather than thrombocytopenia that is associated with the severity of bleeding events . In severe bleeding , platelet transfusion may temporarily reverse platelet dysfunction . In addition , given the presumed role of excessive VWF-platelet interaction in thrombocytopenia in severe leptospirosis , novel therapies aimed at preventing this interaction , including recombinant ADAMTS13 [66] , might also have some therapeutic value . This needs to be addressed in future studies . | Bleeding is a frequent complication of leptospirosis , a disease caused by the pathogenic spirochaete Leptospira . Although thrombocytopenia is common , studies have shown that it does not fully explain the bleeding events seen in these patients . We hypothesized that platelet dysfunction plays a role in the development of bleeding complications . The present study involved 33 hospitalized patients with probable leptospirosis and 25 healthy controls . We report that circulating platelets from patients with severe , probable leptospirosis were activated , but less reactive to ex vivo activation . The degree of this platelet dysfunction was associated with bleeding , in contrast to the degree of thrombocytopenia . Platelets of leptospirosis patients also demonstrated increased binding with von Willebrand factor ( VWF ) , and a strong negative correlation with platelet count suggesting that this binding is important for platelet clearance . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"fibrinogen",
"platelet",
"aggregation",
"tropical",
"diseases",
"platelet",
"activation",
"bacterial",
"diseases",
"signs",
"and",
"symptoms",
"platelets",
"neglected",
"tropical",
"diseases",
"glycoproteins",
"infectious",
"diseases",
"zoonoses",
"thrombocytopenia",
"animal",
"cells",
"hematology",
"leptospirosis",
"blood",
"plasma",
"biochemistry",
"blood",
"coagulation",
"diagnostic",
"medicine",
"blood",
"anatomy",
"cell",
"biology",
"physiology",
"hemorrhage",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"vascular",
"medicine",
"glycobiology"
] | 2017 | Platelet dysfunction contributes to bleeding complications in patients with probable leptospirosis |
The eukaryotic TFIIH complex is involved in Nucleotide Excision Repair and transcription initiation . We analyzed three yeast mutations of the Rad3/XPD helicase of TFIIH known as rem ( recombination and mutation phenotypes ) . We found that , in these mutants , incomplete NER reactions lead to replication fork breaking and the subsequent engagement of the homologous recombination machinery to restore them . Nevertheless , the penetrance varies among mutants , giving rise to a phenotype gradient . Interestingly , the mutations analyzed reside at the ATP-binding groove of Rad3 and in vivo experiments reveal a gain of DNA affinity upon damage of the mutant Rad3 proteins . Since mutations at the ATP-binding groove of XPD in humans are present in the Xeroderma pigmentosum-Cockayne Syndrome ( XP-CS ) , we recreated rem mutations in human cells , and found that these are XP-CS-like . We propose that the balance between the loss of helicase activity and the gain of DNA affinity controls the capacity of TFIIH to open DNA during NER , and its persistence at both DNA lesions and promoters . This conditions NER efficiency and transcription resumption after damage , which in human cells would explain the XP-CS phenotype , opening new perspectives to understand the molecular basis of the role of XPD in human disease .
Accuracy of DNA enzymatic processes , such as transcription , replication and repair , is essential to guarantee genome integrity and , at a higher scale , cell and organism fitness . Such processes are functionally connected to checkpoint mechanisms that respond to DNA damage and stresses compromising cell cycle progression [1] . One relevant player in DNA repair and the maintenance of genome integrity is the multifunctional eukaryotic complex TFIIH . It is formed by 10 subunits and functions in Nucleotide Excision Repair ( NER ) , transcription initiation and transactivation . During NER , bulky adducts that distort the DNA helix are recognized as lesions to which TFIIH binds to allow DNA unwinding , damaged DNA strand recognition and recruitment of the specific nucleases that excise the damaged DNA segment . During transcription , TFIIH facilitates DNA strand opening at promoter regions allowing full association of the transcription machinery and transcription initiation . Promoter escape , which allows transition from transcription initiation to elongation , is achieved by the ability of the cAMP-kinase CAK subcomplex of TFIIH to phosphorylate the C-terminal domain of RNA polymerase II ( RNAPII ) [2] , [3] . During transactivation , TFIIH phosphorylates nuclear receptors to allow their entry into the nucleus , which in turn activates expression of downstream genes . Central to TFIIH performance is Rad3/XPD ( as named in yeast/mammals ) , an essential and conserved eukaryotic protein with 5′>3′ DNA helicase activity . During NER , Rad3 catalyzes DNA-strand opening . This creates the substrate for the action of the DNA-incision endonucleases Rad1-10/XPF-ERCC1 and Rad2/XPG . It is believed that removal of TFIIH is required to allow re-filling of the ssDNA gap generated by the endonucleases [4] . In contrast , the role of Rad3 in transcription initiation is structural . The activity required to open the promoter is provided by a second helicase present in TFIIH , Rad25/XPB [5] . Rad3 serves as a bridge between the core TFIIH and the CAK subcomplex . Since , as mentioned above , CAK phosphorylates RNAPII to clear the promoter and is responsible for the phosphorylation of nuclear receptors during transactivation , Rad3 integrity is fundamental for CAK attachment to TFIIH and its correct performance during transcription and transactivation . Altogether , this explains why mutations in XPD/RAD3 may lead to NER failures as well as transcriptional and developmental defects . In humans , XPD mutations lead to Xeroderma pigmentosum ( XP ) and trichothiodystrophy ( TTD ) , as well as combinations of XP with Cockayne Syndrome ( XP-CS ) and with TTD ( XP-TTD ) . The clinical features of XP patients are explained by a NER deficiency , while TTD is seen as a consequence of transcriptional defects . However , our understanding of the XP-CS clinical features is less clear [6] . CS phenotypes are attributed to the inability to perform transcription-coupled repair ( TCR ) , a NER subpathway in which lesions in the transcribed DNA strand encountered by an elongating RNAPII are efficiently repaired as compared to those of the non-transcribed strand [7] , [8] . In a simplified way , XP-D/CS patient phenotypes could thus be explained by a TCR defect and/or a transcription defect , which would also explain their developmental defects and extreme sunlight sensitivity . Nevertheless , additional issues complicate this view: DNA breaks happen in trans upon UV irradiation in XP-D/CS cells [9] and the cancer proneness of XP-D/CS mice exceeds that of the most NER-deficient mice , those lacking XPA [10] . Therefore , there is still a lack of comprehension regarding the molecular basis of XPD-associated XP-CS phenotypes . A particularly interesting class of rad3 mutations in Saccharomyces cerevisiae is that comprising the semi-dominant rad3-101 ( Rad3-A237T ) and rad3-102 ( Rad3-H661Y ) alleles [11] . They were named rem alleles , as they displayed increased levels of recombination and mutation [12] , [13] . rem mutants differ from canonical NER-deficient RAD3/XPD mutants in their moderate UV sensitivity , increased levels of allelic recombination in heterozygous diploid cells , and inviability in the absence of the homologous recombination ( HR ) factor Rad52 [13] , [14] . We have previously shown that , in contrast to most NER-deficient mutations , the rad3-102 ( Rad3-H661Y ) allele blocks NER at a post-incision step causing an extended retention of TFIIH at the damaged DNA that in turn provokes replication fork breakage and channeling of bulky lesions into HR-mediated Double Strand Break ( DSB ) repair [15] . The longer stay of Rad3-H661Y-containing TFIIH complexes at the site of NER action may be explained by an elevated affinity for ssDNA , given that the rad3-102 mutation lies in the ATP-binding groove of Rad3 and , when ATP hydrolysis by TFIIH is prevented , a gain of affinity for ssDNA manifests [16] . We have proposed that a parallelism may exist between rad3-102 cells and the mutations causing XP-CS in humans [15] because of several reasons . First , re-creation in Sulfolobus acidocaldarius of the equivalent human XPD-G675R mutant protein , associated with XP-CS disease , lies at the ATP-binding groove and displays a ssDNA-binding affinity 164% above that of the WT [17] . Second , this same mutation causes DNA breaks upon UV irradiation [9] , reminiscent of rad3-102 cells . Third , as in rad3-102 , removal of early NER proteins , such as XPA in mice , suppresses the phenotype of break accumulation conferred by the XP-D/CS mutation XPD-G602D [14] , [18] . To gain insight into the molecular nature of the rem mutations and the possibility that they could relate to specific defects such as XP-CS , we analyzed two other yeast rem mutants , rad3-101 [12] , [14] and the new and uncharacterized rad3-107 ( Rad3-E236G; this paper ) . Both mutations also lie in the ATP-binding groove of the protein and , relevantly , all above-mentioned mutations are perfectly conserved between S . cerevisiae and humans ( S1 Figure ) . We first determined the levels of NER deficiency and the dependency on HR of these two rem mutants , establishing a gradient of phenotypes . In vivo experiments show that when the Rad3 ATP-binding groove is mutated , TFIIH gains a higher affinity for DNA . Since mutations in the ATP-binding groove of Sulfolobus acidocaldarius XPD may provoke both a loss of helicase activity and a gain of ssDNA affinity [17] , our results suggest that the balance between these two effects determines the ability of TFIIH to open DNA during NER and its recruitment to both DNA lesions and promoters . This would impair transcription resumption after DNA damage and NER , consistent with the CS phenotype . Last , we extended our study to human cells and show that the mutation equivalent to yeast rad3-102 in human cell lines recapitulates the XP-D/CS phenotypes , opening a parallelism between S . cerevisiae rem alleles and XP-D/CS mutations .
To define the molecular basis of the different phenotypes of rad3-101 and rad3-107 , we first studied the UV response of rad3-101 and rad3-107 mutants in comparison with that of the WT strain and the NER-deficient mutant rad3-2 . Notably , rad3-101 cells respond to UV as the WT strain and in contrast to rad3-102 cells , which were slightly UV sensitive to increasing doses of UV . Instead rad3-107 , as rad3-2 , was highly UV-sensitive ( Fig . 1A ) . We have reported that rad3-102 was moderately resistant to UV because DNA gaps generated by an unfinished NER reaction could be resolved during the S phase via recombination [15] . It is thus possible that UV lesions are differently processed in each of the rad3 mutants analyzed , which would explain the different degrees of UV sensitivity . To test this possibility , we analyzed cell cycle progression through the S phase of cells synchronized in G1 with α-factor , irradiated with a 40 J/m2 UV-C dose and released 2 hours later from the G1 arrest . Without UV irradiation none of the mutant strains showed a cell cycle delay . However , while rad3-102 cells were able to progress through S phase almost as readily as the WT after a similar UV dose [15] , UV-treated rad3-101 and rad3-107 cells were not able to progress throughout the S phase ( Fig . 1B ) . Since FACS analysis cannot differentiate between a block in G1 or early S phase , next we analyzed replication fork progression by PFGE . This technique allows us to determine the fraction of chromosomes that are under active replication as the fraction of DNA unable to enter the gel , staying stacked in the gel well during electrophoresis [15] . In agreement with the FACS analysis , replication kinetics was mostly unchanged without irradiation in the different assayed strains ( Fig . 1C ) . When UV-irradiating the cells , the analysis reveals that rad3-101 cells are able to initiate replication . Up to 70% of the DNA molecules were stacked in the well 120 min after G1 release ( Fig . 1D ) . Nevertheless , replication in rad3-101 was much slower than in the WT as it took longer to accumulate replicating chromosomes . Instead , the same UV dose seems to fully prevent the rad3-107 UV-sensitive cells from initiating replication . In this case accumulation of replicating chromosomes in the well was poor ( Fig . 1D ) . This rad3-107 phenotype is reminiscent of that of the canonical NER-deficient mutant rad3-2 , which is unable to progress into S phase after UV irradiation [15] . Therefore , the different replication efficiencies seem to match the distinct abilities of TFIIH to resolve DNA lesions via NER . According to this hypothesis , we would expect that , at higher UV doses , the UV-resistant rad3-102 mutant should show a similar S-phase delay to that of the rad3-101 and rad3-107 . On the contrary , no arrest during the S phase would be expected for rad3-101 cells at lower UV doses . Indeed , PFGE analysis showed a strong DNA retention in the well in rad3-102 cells in early S phase at UV doses of 100 J/m2 , reaching up to 80% of DNA molecules ( Fig . 1B and D ) . Instead , after a 20 J/m2 UV dose , rad3-101 and rad3-102 cells entered S phase and progressed normally throughout the cell cycle , while rad3-107 cells were still incapable to do so ( S2 Figure ) . Altogether , these results indicate that NER is differently affected in the three mutants analyzed , the intensity of the defect correlating with a different degree of replication impairment . The spontaneous and UV-induced hyper-recombination of rad3-102 mutants has been explained by the accumulation of ssDNA gaps derived from NER abortive reactions that are converted by replication into DSBs that are repaired by HR [15] . If this is the case for all rem-like mutants , the different amounts of DNA lesions accumulated in S phase as a consequence of abortive NER would be revealed by the levels of recombination . As genetically scored recombination events only inform about the DNA breaks that are successfully repaired by HR , we analyzed the global accumulation of Rad52 recombination foci as a way to evaluate whether a correlation existed between UV sensitivity and the accumulation of HR intermediates . Spontaneous and UV-induced Rad52 foci at 2 hours after irradiation with 10 or 20 J/m2 UV-C were thus analyzed and only cells in S and G2 phase of the cell cycle were considered . Both rad3-101 and rad3-107 cells accumulated more spontaneous Rad52 foci than wild-type cells ( Fig . 2A ) , as previously shown for rad3-102 ( [15] and Fig . 2A ) . However , after 10 J/m2 of UV-C dose , rad3-101 and rad3-107 displayed a 4- and 2-fold increase with respect to the wild type , respectively ( Fig . 2A ) , both mutants having reached a maximum level of Rad52 foci . After a 20 J/m2 UV-C dose , foci increased only in the wild type ( Fig . 2A ) , consistent with the data showing that rad3-101 and rad3-107 cells did not progress through S phase properly ( Fig . 1B ) . Notably , Rad52 foci accumulated in rad3-107 despite its strong UV sensitivity , which contrasts with the canonical NER-deficient mutant rad3-2 [15] . Altogether , these results suggest that a specific feature of rem mutants is their ability to convert bulky DNA adducts into DNA breaks that are repaired by HR , even though this may not be equally efficient in all mutants . Consequently , we wondered whether early recombinational DSB repair functions such as Rad52 and the MRX complex become essential in rad3-101 and rad3-107 mutants , as previously shown for rad3-102 and in contrast to rad3-2 [13]-[15] . First we asked whether rad3-101 and rad3-107 relied on HR for their viability . rad3-101 was either lethal or showed a synthetic growth defect phenotype when combined with mre11Δ in non-irradiated cells ( Fig . 2B ) , consistent with the previous lethality reported for rad3-101 rad52-1 [13] . The results suggest that , in rad3-101 , spontaneous and UV-induced DNA breaks accumulate at a high frequency . rad3-107 also proved to be dependent on HR functions for viability , as rad3-107 mre11Δ double mutants showed a clear synthetic growth defect ( Fig . 2B ) . Moreover , surviving rad3-101 mre11Δ cells and rad3-107 mre11Δ cells were extremely UV-sensitive ( S3 Figure ) . Data are consistent with the idea that a specific feature of rem mutants is the accumulation of DNA breaks that need to be repaired by HR , which thus becomes essential for viability , even though to different extents in different mutants . Our previous findings that rad3-102 is not viable if both Rad51 and Pol32 are removed suggest that these proteins control two different but non-mutually exclusive Rad52-dependent HR pathways for the repair of replication-mediated DNA breaks [15] . To assay whether this was also the case for the DNA lesions accumulated in rad3-101 and rad3-107 mutants , we characterized the corresponding double and triple mutants with rad51Δ and pol32Δ . As previously shown for rad3-102 [15] , double rad3-101 rad51Δ and rad3-107 rad51Δ mutants were viable . In addition , rad3-101 rad51Δ was clearly UV sensitive when compared with each of the single mutants ( Fig . 2C ) . As can be seen in Fig . 2B , both triple mutants rad3-101 rad51Δ pol32Δ and rad3-107 rad51Δ pol32Δ were not viable , as previously reported for rad3-102 . To assay whether Rad51 becomes critical for the repair of broken forks and viability under HU-induced replicative stress , we deleted it in rad3-101 and rad3-107 cells . Notably , when 40 mM HU was added to asynchronous cultures of both rad3-101 rad51Δ and rad3-107 rad51Δ double mutants , cells arrested in late S/G2 phase , in contrast to WT and single rad3 mutants ( Fig . 2D ) , as previously shown for rad3-102 [15] . This suggests that cells are unable to progress through the S phase , likely due to the incapacity of the broken forks to re-start in a Rad51-dependent manner . The results are consistent with the idea that , in rem mutants , replication forks break at the damaged sites as a consequence of unfinished NER , channeling repair of the resulting DSBs to HR , which in turn becomes essential . As mentioned above , the three rem mutations so far identified localize at the ATP-binding groove of Rad3 ( S4A Figure ) . Defects in Rad3 ATP binding cause an ATP-hydrolysis defect , which is known to increase the affinity of TFIIH for DNA [16] . This could explain the low efficiency of late NER steps in rad3-102 cells [4] , [15] . Therefore we wondered whether the different levels of damage capable of being repaired by HR in the three rem mutants would correlate with a gain of DNA affinity of the Rad3 mutant proteins . We hypothesized that any ATP-binding groove mutant should show a rem-like phenotype . Since ATP hydrolysis failure should also compromise the helicase activity , the exception would be the helicase-null mutants , in which the incision step of the NER reaction cannot occur and would therefore behave as NER-null mutants . This would be the case of rad3-2 , also located in the ATP-binding groove ( S4A Figure ) , which is unable to excise the damaged ssDNA [19] . First , we asked whether mutations in the ATP-binding groove of Rad3 , such as those of the rem strains studied here , could cause a gain in DNA affinity in vivo , independently of whether or not being masked by a helicase activity defect . For this we analyzed TFIIH retention at promoters , in which the helicase activity needed to open the DNA is provided by Rad25 and not by Rad3 [5] . We performed Tfb4-TAP chromatin immunoprecipitation ( ChIP ) in asynchronous cultures of the wild-type strain , the three rem mutants and the NER-null mutant rad3-2 , plus the rad3-25 mutant carrying a E548K amino acid change that maps at the DNA binding channel ( S4A Figure ) , outside of the ATP-binding groove and that was therefore expected not to show a significant gain of ssDNA affinity . To minimize any possible effect caused by transcription , we determined TFIIH binding at the ALG9 promoter , since ALG9 is constitutively expressed at a constant rate independently of environmental and cellular conditions [20] and we verified that it was transcribed in all mutants to similar levels as the WT ( S4B Figure ) . The ChIP analyses show that TFIIH is more abundant at the ALG9 promoter in the three rem mutants , while in the rad3-25 control recruitment was indistinguishable from the WT ( Fig . 3A ) . The rad3-2 NER-null mutant displayed the strongest promoter retention of all ATP-groove mutants , up to 3-fold the WT levels ( Fig . 3A ) . Then we analyzed promoter occupancy upon UV irradiation , as UV is known to drive TFIIH out of the promoters presumably to facilitate its action at NER sites [21] . We took samples at different time-points from asynchronous cultures during 30 minutes after UV irradiation of the WT and the strains showing a significantly enhanced promoter retention in Fig . 3A , namely rad3-102 , rad3-107 and rad3-2 mutants , all of them mutated in the ATP-binding groove . We assayed TFIIH recruitment to the GRX1 promoter , where we found the same relative increase as in the ALG9 promoter under conditions of no irradiation . The high basal transcription levels of GRX1 allow a better detection of falling off promoters upon UV treatment . Recruitment values for the control rad3-25 were similar as those of the WT ( S4C Figure ) . After an 80 J/m2 UV dose , the relative fall-off promoters experienced by TFIIH in each mutant background was similar ( Fig . 3B ) , implying that the shut-off of transcription in response to UV to accomplish NER is intact . Nevertheless , a significant amount of TFIIH remained bound to the GRX1 promoter in the rad3-102 , rad3-107 and rad3-2 ATP-binding groove mutants up to 30 minutes , whereas almost most TFIIH was released from the promoter in WT cells ( Fig . 3B ) . Altogether , these results indicate that mutants in the ATP-binding groove of Rad3 experience a gain in DNA affinity in vivo and , in particular , for promoters both with and without UV irradiation . We had established the affinity for DNA in vivo of the mutant Rad3 proteins by studying the residence of TFIIH at promoters . We then assayed globally the capacity of mutant TFIIH to bind to DNA at NER sites in vivo by using Fluorescence Recovery After Photo-bleaching ( FRAP ) using the tagged Tfb4-yEGFP protein . The rationale behind was that an impairment in the ATPase activity would lead to helicase activity defects and consequently to a defective performance of the protein during the repair reaction . Therefore , after UV , if rad3 mutations prevented these activities of the protein , a bigger diffusing fraction should be observed for the mutants . We first confirmed that Tfb4-yEGFP behaves as a wild-type non-tagged Tfb4 by showing that the EGFP signal was detected all over the nuclei , that cells were as UV-resistant as WT cells and that an expected kinetics of fluorescence recovery was observed after photo-bleaching ( Fig . 4A , and S5A and S5B Figure ) . In untreated WT cells , full recovery of fluorescence was observed in less than 12 seconds after bleaching ( Fig . 4A and S5C Figure ) , consistent with the observation that most of the TFIIH complex within the nucleus is diffusible [22] . After UV irradiation a variable fraction of TFIIH is expected to move to the sites of DNA lesions to be engaged in their repair , leading to a low-diffusible fraction that reduces fluorescence recovery after bleaching . Accordingly , fluorescence recovery reached only up to 80% in cells treated with an 80 J/m2 UV dose ( S5C Figure ) . In spontaneous conditions , the TFIIH complex in ATP-groove mutants showed a similar dynamics to that of the WT ( Fig . 4A ) . Nevertheless , after an 80 J/m2 UV dose , two observations could be made . First , fluorescence recovered completely in all mutants , indicating that the diffusing TFIIH fraction is bigger than in the WT ( Fig . 4B ) . Second , the maximal recovery of fluorescence is achieved in shorter times in all mutants with respect to WT ( Fig . 4B ) . Thus , it took only 8 seconds in rad3-2 cells for maximal recovery , in sharp contrast to the 24 seconds needed for the WT . This is better appreciated when each time point is plotted relative to an identical normalized maximum ( S5D Figure ) , in which it can be seen the difference in the slopes of the initial time points among the different strains . This result is consistent with the idea that a defect in the ATP-binding groove of Rad3 may hinder TFIIH ability to bind to repair sites , leading to a larger diffusion fraction . Accumulation of DNA breaks upon UV irradiation has been previously reported in XPD-deficient human XP8BR ( XP-D/CS ) cells [18] bearing a mutation in the ATP-binding groove of XPD . Interestingly , the equivalent mutant protein in Sulfolobus acidocaldarius exhibits an increased affinity for ssDNA [17] . Moreover , removal of the early-acting NER protein XPA suppressed the accumulation of DNA breaks [18] . Given that yeast rem mutants seem reminiscent of XP-D/CS cells , we investigated this putative parallelism . In the first place , we analyzed the ability of rem mutants to activate the checkpoint in response to UV insults , since NER-deficient mutants do not accomplish damage processing , which in turn prevents checkpoint activation [23] . This feature is recapitulated in human cell lines defective for NER , including XPD-defective ones . Interestingly , cells from XP-D/CS patients , contrary to what was just mentioned , manage to activate the checkpoint [24] . Therefore , if XP-D/CS and rem mutations functionally relate , rem mutants should lead to checkpoint activation in response to UV . This prediction should apply to rad3-101 and rad3-102 mutants , who display an acute HR dependency , implying that initial NER steps are accomplished and therefore checkpoint activation can presumably occur . We synchronized cells in G1 and irradiated them with 100 J/m2 as previously described [23] . We monitored checkpoint activation in response to UV by following Rad53 phosphorylation . We could observe the expected phosphorylation of Rad53 in the WT strain ( Fig . 4C ) . In agreement with the prediction , rad3-101 and rad3-102 displayed an even better , or slightly worse , respectively , Rad53 phosphorylation when compared with the WT ( Fig . 4C ) . Very UV-sensitive mutants as rad3-107 and rad3-2 , related to poor damage processing , displayed negligible or absent checkpoint activation , respectively ( Fig . 4C ) . As a whole , these data suggest a parallelism between rem and XP-D/CS-causing mutations and a molecular explanation for the reported checkpoint activation after UV described for XP-D/CS cells [24] . In the second place , we wanted to test whether the rem alleles had the same impact on human cells as in S . cerevisiae . For this , we first assayed whether the yeast rad3-102 feature of accumulation of spontaneous DNA breaks was recreated in human cells . To achieve it , we overexpressed an XPD allele , XPD-102 ( XPD-H659Y ) , carrying the equivalent of the yeast semi-dominant rad3-102 ( rad3-H661Y ) mutation ( Fig . 5A ) . As a control we overexpressed the wild-type version of XPD from the same plasmid . mRNA levels of XPD and XPD-102 increased 300-fold with respect to cells transfected with the empty vector after 24 hours of transfection , and this correlated with increased expression at the protein level ( S6A Figure ) . As an additional control , we verified that XPD-102 overexpression did not alter basic transcriptional patterns of the cell . Analysis by qPCR of levels of two relevant housekeeping mRNAs denoted no change in transcription between control and XPD-102-overexpressing cells ( S6B Figure ) . As DNA breaks accumulate in yeast rad3-102 cells [15] , we analyzed by immunofluorescence 53BP1 foci , known to accumulate early at sites of DSBs [25] . U2OS cells overexpressing wild-type XPD show a similar percentage of 53BP1 foci-containing cells as U2OS cells transfected with the empty plasmid ( 22% and 26% , respectively ) . However , the percentage of cells with 53BP1 foci was clearly increased in cells overexpressing XPD-102 ( 35% ) ( Fig . 5A ) . For further evidence of the accumulation of DNA breaks , we directly analyzed the accumulation of broken DNA fragments using the single cell gel electrophoresis assay ( comet assay ) under alkaline conditions to detect both single-stranded and double-stranded DNA breaks . In these assays , XPD-102 cells showed a larger tail moment than XPD and control cells ( Fig . 5B ) , which demonstrates that physical breaks are spontaneously produced in these cells . Conclusions were confirmed in U2OS cells by additional immunofluorescence analysis . While the percentage of U2OS cells containing γH2AX foci was similar in control and XPD-overexpressing cells , XPD-102 U2OS cells showed a significantly higher accumulation ( Fig . 5C ) . We also validated this notion in HeLa cells , where the γH2AX signal increased 1 . 5 times after XPD-102 overexpression with respect to XPD-overexpressing cells as determined by FACS ( S6C Figure ) . As thus far experiments were performed overexpressing XPD-102 in XPD+/+ cells , next we asked whether results were the same in primary XP16BR ( XPD-R683W/R616P , XP-D ) fibroblasts , in which one XPD allele is hypothesized to be null and the other one severely affected in its enzymatic activity [26] . In these primary fibroblasts , 53BP1 foci and comet tail moments were similar to those obtained in XPD+/+ U2OS cells , with a 1 . 7-fold increase above the control cells transfected either with the empty vector or the one overexpressing the wild-type allele of XPD ( Fig . 5A and 5B ) . Altogether , these results imply that the basal level of damage recruiting TFIIH is sufficient to uncover the effects caused by the action of mutant XPD-102 , which results in the production of breaks . We therefore conclude that the XPD-102 mutation causes similar effects both in humans and yeast . The breaks described to occur in XP-D/CS human XPD-G675R cells and in mouse XpdG602 cells are UV-induced , and in the latter case have been shown to depend on the NER-initiating factor XPA [18] . To prove the analogy between the rem-like mutation XPD-102 and XP-D/CS cells , we first assessed whether XPA depletion by siRNA suppressed the breaks provoked by XPD-102 without UV irradiation . XPA mRNA levels were similarly reduced in all cells after 96 hours of siRNA depletion , and this correlated with a decrease at the protein level ( S6D Figure ) . XPA depletion did not have any suppressive effect on the number of γH2AX foci in the control or in cells overexpressing XPD ( Fig . 5C ) . In contrast , the increase in the percentage of γH2AX foci-containing U2OS cells upon XPD-102 expression was reduced back to the level of cells overexpressing wild-type XPD and depleted of XPA ( Fig . 5C ) . This argues in favor of the idea that , as in XP-D/CS cells , the accumulation of DNA breaks in XPD-102 cells depends on a functional NER pathway . If our hypothesis is correct , the XPD-102-induced breaks should be exacerbated by UV treatment , as more substrates would become available for the mutant XPD-102 protein . Following the same approach as above , the percentage of γH2AX foci-containing U2OS cells was studied . UV light greatly increased γH2AX foci in all cells ( compare Figs . 5C and 5D ) . We differentiated pan-nuclear staining , a response to UV irradiation dependent on initial attack of the damage by the NER components , from discrete foci formation , a readout of replication-associated DSBs , among others [27] . While pan-nuclear staining was virtually similar in all assayed cellular contexts , the increase of discrete γH2AX foci was significantly exacerbated for cells overexpressing XPD-102 in comparison with both controls ( Fig . 5D ) . Altogether , the results suggest a functional relationship between human XP-D/CS and yeast rem mutations , in particular rad3-102 , all of them affecting the ATP-binding groove .
The TFIIH complex has central roles in cell physiology , as it is involved in both transcription and DNA repair . This explains that defects in any of its subunits lead to inherited diseases in humans of important clinical relevance . Understanding the molecular basis underlying the phenotypes shown by TFIIH patients bearing a combination of both Xeroderma pigmentosum and Cockayne Syndrome has been long pursued . The classical view invokes a defect in TCR that accounts for both the NER deficiency and the transcriptional defects [6] . We have previously discussed several similarities between XP-D/CS-causing mutations and the S . cerevisiae rad3-102 rem allele [28] . Nevertheless , there are also important differences between them . Thus , rad3-102 cells are slightly sensitive to UV , while XP-D/CS cells are strongly UV-sensitive . Also , rad3-102 cells do not show clear transcriptional defects , while XP-D/CS cells do . In this work , by addressing directly the molecular basis for the analogies and differences between several yeast rem mutants ( rad3-101 , rad3-102 and the newly constructed rad3-107 ) and by characterizing the effects of XPD-102 in human cells , we show that rem mutants can be used as a model to understand XP-D/CS-associated molecular phenotypes . We first show that the rad3-102 ability to tolerate UV irradiation is not by itself a rem feature . On the contrary , there exists a gradient of UV sensitivity depending on the rem mutant analyzed ( Fig . 1 ) . rad3-102 tolerance to UV is based on the fact that TFIIH is able to remain bound to the damage site longer , thus inhibiting gap filling and generating a DSB during replication . As a result HR becomes essential for survival [13]-[15] . This is also the case of rad3-101 cells , which display UV resistance comparable to that of the WT strain and exhibit full dependence on HR factors Rad52 and the MRX complex for survival ( Fig . 2B ) . Instead , rad3-107 cells , which are extremely UV-sensitive , display only a synthetic growth defect in the absence of HR factors ( Fig . 2B ) . A lower ability of TFIIH to load onto DNA and to remain bound , thus resulting in a lower frequency of NER reactions ending in DSBs , would explain the inverse correlation between the need of HR and UV sensitivity . Nevertheless , even if the amount of damage leading to DSBs is different in each rem mutant , replication forks could still break in most cases . Consistently , removal in the three rem mutants tested of both Pol32 and Rad51 , which block HR-mediated fork restart in S . cerevisiae , lead to inviability ( Fig . 2B ) [15] . Therefore , UV-resistance is not an obligatory feature of rem mutants , their variable degree of UV sensitivity depending on the balance between the amount of NER-repairable damage that remains unprocessed and the amount that is directed into HR repair . As rem mutations map to the ATP-binding groove of Rad3 ( S4A Figure ) , and are fully conserved ( S1 Figure ) , they provide a useful tool to understand the possible consequences of impairing ATP binding on NER processing . XPD alterations leading to ATP-binding defects may compromise helicase activity and , consequently , could influence helix opening and helicase translocation on the DNA [29] . Indeed , the mutants tested displayed levels of fluorescence recovery after UV superior to the WT , as determined by FRAP analysis ( Fig . 4B ) , a measure indicating a problem to attach to damage sites . Additionally , difficulties in hydrolyzing ATP would also lead to a putative TFIIH gain in ssDNA affinity [16] . Since , during transcription , Rad25 helicase activity is in charge of promoter opening , while Rad3 is only needed for structural reasons [5] , it was possible to use promoters to detect putative TFIIH retentions at DNA . Notably , at basal transcription levels , all ATP-binding groove mutants , including the rad3-2 NER-null mutant , show higher levels of TFIIH recruitment to promoters than the WT strain or than a UV-sensitive strain [30] whose mutation did not map to the ATP-binding groove , a behavior maintained after UV irradiation ( Fig . 3A and 3B ) . Nevertheless , the relative fall-off promoters upon UV irradiation experienced by all mutants tested was comparable to that of the WT , indicating a normal response to abrogate transcription and migrate to NER sites ( Fig . 3B ) . These data support the notion that the TFIIH complexes in rem ATP-binding groove mutants display an enhanced affinity for DNA . Remarkably , all XPD mutations causative of XP-CS known to date are located in the ATP-binding groove ( S4A Figure ) . Re-creation of these four mutations in Sulfolobus acidocaldarius XPD demonstrated a dramatic loss of both ATPase and helicase activities . Importantly , half of them exhibited an increased ssDNA affinity , a property not seen in any other category of XPD mutants [17] . Our first molecular observation to functionally link Rad3 rem mutations and XP-CS-causing XPD ones is the ability to activate the checkpoint in response to UV of both XPD-CS cells [24] and HR-dependent rem mutants ( Fig . 4C ) . This is a remarkable parallelism since canonical rad3 NER-deficient yeast and XPD-deficient human cells are unable to activate the checkpoint upon UV insult [23] , [24] . Moreover , this provides an explanation for the mechanism leading to such checkpoint activation in XP-D/CS cells: partial processing of the lesion by accomplishment of the initial steps of NER , as it occurs in HR-dependent rem cells . To search for additional molecular support for the rem/XP-D/CS link , we moved to human cells . Overexpression of an XPD-102 ( XPD-H659Y ) mutation leads to DNA break accumulation , already in the absence of UV , as detected by single-cell electrophoresis as well as 53BP1 and γH2AX foci accumulation ( Fig . 5 and S6C Figure ) in three different human cell lines . This detection is possible since there exist substrates for NER under basal conditions that are processed by the defective XPD-102 . Therefore , we are able to re-create in human cells this known feature of yeast rad3-102 . Yet , if the notion that rem and XP-D/CS mutations are equivalent is correct , several predictions should prove right . First , such an accumulation of DNA breaks should be XPA-dependent . Indeed , depletion of XPA led to a reduction in the accumulation of DNA breaks to levels comparable to those caused by overexpression of the control XPD ( Fig . 5C ) , as seen in XP-D/CS [18] and rad3-102 cells , where early-acting factors such as Rad4 suppress this defect [14] . Second , the formation of breaks should be further increased by providing the cells with more substrates , namely by UV-irradiation . Again , a significant fraction of γH2AX foci accumulated specifically upon overexpression of XPD-102 , while overexpression of the wild type allele of XPD did not even increase this value up to the level seen in the control itself ( Fig . 5D ) . Last , even though XP-D/CS breaks were originally described as being transcription-dependent [18] , this notion was recently dismissed using XpdG602D mouse cells , which mimic an XP-D/CS mutation [31] . Indeed , it has been demonstrated that early steps of the DNA damage-signaling cascade , as those under study here , still occur even if transcription is inhibited , and that the action of the mutant protein has been exerted [31] . Altogether , the results support the view that rem alleles and human mutations causing XP-D/CS phenotype are functionally equivalent . The explanation underlying the molecular basis of XP-CS has remained elusive . The XP defect is explained by deficient NER , but the explanation for the CS phenotype , generally invoking a defect in TCR , is unclear . In our view , an integrated mechanism for DNA damage removal inability and transcription retardation can be proposed . In WT cells , TFIIH will promote NER and then resume transcription ( Fig . 6 , middle ) . Rad3/XPD null mutants will be unable to open DNA at a damaged site therefore not affecting transcription resumption ( Fig . 6 , left ) . However , in mutants of the ATP-binding groove ( rem-like mutants , Fig . 6 , right ) , there would co-exist an enhanced DNA affinity and a reduced DNA opening capacity . The increased DNA affinity would easily manifest at locations where TFIIH binding does not depend on Rad3/XPD , as it is the case of promoters . The enhanced affinity for other DNA sites , such as damaged DNA , nevertheless , would depend on the ability of TFIIH to open the DNA first . If the opening occurs , TFIIH could be engaged in NER but will stay bound to the damaged DNA for longer due to a gain in affinity for DNA . This may delay transcription resumption and also provoke replication fork collapse and breakage that would demand the intervention of the HR machinery . If this process were efficient , UV sensitivity would be mild . However , mutations in the ATP-binding groove may also have a strong impact , so that TFIIH may stay associated with promoters ( Fig . 6 , right , dashed lines ) . This model would be in agreement with the proposal that genomic DNA is cut in trans upon transfection of damaged plasmids into XP-CS cells [9] . In our view , therefore , the transcription impairment after DNA damage would occur in a TCR-independent manner . In further agreement with this view , recent data reveal that RNA recovery defects following UV in XP-CS cells are restricted to genes whose expression was shut-off specifically in response to UV , leaving damage-inducible genes unaffected , thus arguing against a general defect in TCR , which impairs all types of transcription [32] . Moreover , authors demonstrate a specific heterochromatinization happening at promoters that do not resume transcription [32] , temptingly as a consequence of a too lasting period without TFIIH components coming back . In conclusion , we propose that , as in the rem alleles , the XP features seen in XP-D/CS patients arise from the inability of their cells to initiate and/or accomplish a proficient NER reaction , whereas the CS features would be the consequence of increased levels of the repair conformation of TFIIH that may compromise resumption of transcription . Consistent with our model , it has recently been observed in XpdG602D mice cells mimicking an XP-D/CS mutation that defective NER leads to the accumulation of long stretches of ssDNA upon UV , suggesting that the long-lasting aberrant NER intermediate extends the time of repair and therefore may inhibit transcription for long periods [31] . Subsequent accumulation of replication-mediated DNA breaks that demand an intact HR for its repair and replication restart would explain the high levels of genetic instability that characterizes both rem yeast and XP-CS human cells .
Strains used are described in S1 Table . The rad3-107 ( rad3-E236G ) mutation was constructed by oligonucleotide directed mutagenesis using the primer 5'TTTTGATG ( CGT ) AGCGCACA3' and a plasmid pPF1 containing the BamHI-SalI fragment of RAD3 cloned into pGEM7Zf ( Promega Biotech , Madison , WI ) . The E236G mutation was confirmed by sequencing and was used to substitute the wild type BamHI-SalI fragment of pBM3 [14] to make pBM3-107 . Plasmids pBM3-101 and pBM3-107 , containing the rad3-101 and rad3-107 mutant alleles , were used to substitute the endogenous RAD3 gene by the respective mutant alleles followed by the hygromycin resistance cassette to generate strains YREC57-41 and YREC57-45 . Plasmid pKT127 [33] was used to construct Tfb4-yEGFP-based strains . All other single , double and triple mutants were obtained by backcrosses . Plasmid pWJ1344 carrying the RAD52-YFP construct has been previously described [34] . The XPD cDNA sequence was obtained by PCR from the RZPD/ImaGenes clone IRATp970F12108D ( Berlin , Germany ) . The XPD-102 allele containing the C1980T substitution was obtained by directed mutagenesis using two overlapping PCR primers ( GGACCTGTAAGGCTCGAGATGAAGCTCAACGTGGA and GGGCCGCGTAGCGCATGGCATCGAAGGTAAGAAA for the 5′ site and ACTTTCTTACCTTCGATGCCATGCGCTACGCGGCCCAGT and GCGCTCGATACGGAATTCTCAGAGCTGCTGAGCAA for the 3′ site ) sharing the mutation . Both cDNAs were cloned into the XhoI-EcoRI sites of the pIRES2-EGFP vector ( Clontech , Palo Alto , CA , USA ) . U2OS and HeLa cells were cultured in DMEM ( Gibco , NY ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) . Primary human XP16BR fibroblasts ( provided by Dr . A . R . Lehmann ) were cultured in Eagle's minimum essential medium ( Biowest ) with 15% heat-inactivated FBS . All cells were maintained at 37°C and 5% CO2 . Short interfering RNAs ( siRNA ) used were on-target plus non-targeting pool ( siC ) and on-target plus smartpool human XPA ( siXPA ) ( Dharmacon ) . Cells were transfected with plasmid ( 2 µg/ml ) or siRNA ( 100 nM ) using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) or DharmaFECT 1 ( Dharmacon ) respectively , according to the manufacturer's instructions . Immunostaining and single-cell electrophoresis assays were performed 24 or 96 hours after the plasmid or siRNA transfection , respectively . Strains were cultured until 0 . 7O . D . 600nm in SC . A 50 mL sample was taken for the non-irradiated control experiments . The culture was centrifuged , resuspended in distilled , sterile water , and irradiated in plates using a 80 J/m2 UV dose . Cells were immediately resuspended in fresh medium , and 50 mL samples were taken every 10 min . Samples processing was performed as described [35] . IgG Sepharose ( GE Healthcare ) was incubated with samples over night to precipitate TAP-tagged Tfb4 . The WizardR SV DNA clean-up system ( Promega ) was used for the last DNA purification step . Quantitative PCR ( qPCR ) was performed against the ALG9 or GRX1 loci promoters in Matα cells . Normalization was done with values of amplification at the MFA2 promoter , as described for Matα cells in order to study TFIIH recruitment in the absence of transcription [36] . Damage of templates by UV irradiation did not impede proper qPCR amplification , since controls at sites of active transcription displayed high amplification signals , and in vitro repair of templates prior to qPCR as described [37] did not alter results . Primers used were: ALG9-fw: TGGCTCTTTTTTCACCCTGAA; ALG9-rv: TGGTTACCGCCTTGCAATTC; MFA2-fw: TGCATGTCAGAGGAAAAAGAACAAAG; MFA2-rv: CGGTGAACGACAGAAGAAGTGG; GRX1-fw: TCACGTGAATCAGGAGGCG; GRX1-rv: GGCGTTTCCAGATTGCGAT . Overnight Tfb4-yEGFP mid-log cultures , grown in YPAD , were either non-treated or 80 J/m2-UV-C-irradiated . FRAP was performed on a Leica TCS SP5 confocal microscope at room temperature . A 0 . 6 µm2 area into the nucleus was bleached at 50% laser intensity . Recovery of fluorescence into this area was monitored with 1 second intervals at 5% laser intensity . Images were acquired with a 488 nm laser . Fluorescence intensities were measured in bleached and unbleached areas with the MetaMorph v7 . 5 . 1 . 0 . software . Normalized fluorescence in each point was calculated as follows: Irel = ( ( N0 - B0 ) × ( It – Bt ) ) / ( ( Nt – Bt ) × ( I0 – B0 ) ) where I0 , N0 and B0 are the average intensity of the bleached region , an area in the same nucleus , or a randomly selected region outside of the cell during prebleach , respectively . It , Nt and Bt are the average intensity of the bleached region , an area in the same nucleus , or a randomly selected region outside of the cell at each time point , respectively . At least 10 cells for each condition were analyzed . Cells were cultured on glass coverslips and transfected at 70-80% confluence . For UV irradiation , cells were washed with PBS , irradiated with a 5 J/m2 UV-C dose after PBS removal , further incubated and collected after 5 hours . Cells were fixed in 2% formaldehyde in phosphate-buffered saline buffer ( PBS ) for 20 min and permeabilized with 70% ethanol for 5 min at −20°C , 5 min at 4°C , and washed twice in PBS . After blocking with 3% bovine serum albumin ( BSA ) in PBS , the coverslips were incubated with rabbit polyclonal anti-53BP1 ( NB100-304 Abyntec Biopharma ) or mouse monoclonal anti-γH2AX ( JBW301 , 05-636 Millipore ) primary antibodies ( 1∶500 ) diluted in 3% BSA in PBS for 1h at room temperature . Secondary goat anti-rabbit antibody conjugated with Alexa Fluor 568 or goat anti-mouse antibody conjugated with Alexa Fluor 546 ( Invitrogen ) in 3% BSA in PBS were used . Transfection efficiency was monitored by transfection into cells of pIRES2-EGFP , pIRES2-EGFP-XPD or pIRES2-EGFP-XPD-102 plasmids . DNA was stained with DAPI . Images were captured with a Leica DM6000 microscope equipped with a DFC390 camera ( Leica ) . Data acquisition was performed with LAS AF ( Leica ) and analyzed with the MetaMorph v7 . 5 . 1 . 0 . software . More than 100 cells from each experiment were analyzed . Comet assay was performed using a commercial kit ( Trevigen , Gaithersburg , MD , USA ) following the manufacturer's protocol . Images were acquired as described above and analyzed with the Comet-score software ( version 1 . 5 ) . More than 100 cells from each experiment were scored . cDNA was synthesized from total RNA extracted using RNeasy Mini Kit ( Qiagen ) ( 1 µg ) by reverse transcription using Super-Script TM First strand synthesis for RT-PCR ( Invitrogen , Carlsbad , CA ) and random primers . RT-qPCR was performed with SYBR qPCR Mix and analyzed on an ABI Prism 7000 ( Applied Biosystems , Carlsbad , CA ) . mRNA expression of the indicated genes were normalized with mRNA expression of the HPRT housekeeping gene . For the analysis of DHFR and GAPDH mRNA expression , data were normalized to the 18S RNA levels . Primers used were AAAGTGTCCGAGGGAATCGA and GGGACGCCAAACATGATGA for XPD , GAACCACTTTGATTTGCCAACTT and TTGCCTCTGTTTTGGTTATAAGCTT for XPA , GGACTAATTATGGACAGGACTG and TCCAGCAGGTCAGCAAAGAA for HPRT , TGCCACCAACTATCCAGACCA and CCTGGTTCTCCATTCCTGAGA for DHFR , CGACCACTTTGTCAAGCTCA and TACTCCTTGGAGGCCATGTG for GAPDH , and ATTCGAACGTCTGCCCTATCA and GTCACCCGTGGTCACCATG for 18S . γH2AX signal in HeLa cells by FACS was measured using a commercial kit ( FlowCellect Multi-Color DNA Damage Response Kit , Millipore , Germany ) following the manufacturer's protocol . The methods for the analyses of yeast UV survival curves , cell cycle profiles , Pulsed-Field Gel Electrophoresis and spontaneous and UV-induced Rad52 foci have been previously described in detail [15] . Cell treatment , protein extraction and Western Blotting for Rad53 activation has been performed strictly as previously described [23] . Rad53 antibody used was previously described [38] . For XPD and XPA Western blots , 25 µg of total amount of protein , extracted from U2OS cells , were used . Antibodies XPD ( abcam ab54676 , 1∶5000 ) , XPA ( abcam ab2352 , 1∶1000 ) and β-Actin ( abcam ab8226 , 1∶5000 ) diluted in TBS-Tween 0 . 1% with 5% milk were incubated overnight at 4°C . | TFIIH is a protein complex that functions in the repair of bulky adducts distorting the DNA via the pathway of Nucleotide Excision Repair , and in transcription initiation and transactivation , the latter being a specific transcription activation process occurring in response to hormones . We have taken advantage of the powerful genetics and molecular biology of the model organism Saccharomyces cerevisiae to characterize the impact on cell fitness of a particular kind of mutations of one of the two helicases of the TFIIH complex , Rad3 , called rem mutations for their increased levels of recombination and mutation . We have realized that these mutations affect a particular site of the protein , its ATP-binding groove , and modify the dynamics of TFIIH , leading to unfinished repair reactions and DNA break accumulation . Finally , we recreated these mutations in the human homolog XPD protein and found that their phenotypes recapitulated those of human mutations leading to a combination of the two hereditary diseases Xeroderma pigmentosum and Cockayne syndrome ( XP-D/CS ) , whose molecular basis remains elusive . As these mutations also affect the ATP-binding groove of XPD , this study permits to propose a model to explain the molecular basis of XP-D/CS . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"dna",
"damage",
"biochemistry",
"nucleotide",
"excision",
"repair",
"genetic",
"disorders",
"dna",
"replication",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"repair",
"dna",
"genetics",
"of",
"disease",
"molecular",
"biology"
] | 2014 | The rem Mutations in the ATP-Binding Groove of the Rad3/XPD Helicase Lead to Xeroderma pigmentosum-Cockayne Syndrome-Like Phenotypes |
Dengue illness causes 50–100 million infections worldwide and threatens 2 . 5 billion people in the tropical and subtropical regions . Little is known about the disease burden and economic impact of dengue in higher resourced countries or the cost-effectiveness of potential dengue vaccines in such settings . We estimate the direct and indirect costs of dengue from hospitalized and ambulatory cases in Singapore . We consider inter alia the impacts of dengue on the economy using the human-capital and the friction cost methods . Disease burden was estimated using disability-adjusted life years ( DALYs ) and the cost-effectiveness of a potential vaccine program was evaluated . The average economic impact of dengue illness in Singapore from 2000 to 2009 in constant 2010 US$ ranged between $0 . 85 billion and $1 . 15 billion , of which control costs constitute 42%–59% . Using empirically derived disability weights , we estimated an annual average disease burden of 9–14 DALYs per 100 000 habitants , making it comparable to diseases such as hepatitis B or syphilis . The proportion of symptomatic dengue cases detected by the national surveillance system was estimated to be low , and to decrease with age . Under population projections by the United Nations , the price per dose threshold for which vaccines stop being more cost-effective than the current vector control program ranged from $50 for mass vaccination requiring 3 doses and only conferring 10 years of immunity to $300 for vaccination requiring 2 doses and conferring lifetime immunity . The thresholds for these vaccine programs to not be cost-effective for Singapore were $100 and $500 per dose respectively . Dengue illness presents a serious economic and disease burden in Singapore . Dengue vaccines are expected to be cost-effective if reasonably low prices are adopted and will help to reduce the economic and disease burden of dengue in Singapore substantially .
Dengue and dengue hemorrhagic fever ( DF and DHF , respectively ) are substantial public health threats throughout the tropical and subtropical regions [1] , [2] . The distribution of dengue and its vectors has expanded dramatically over the last 30 years , among other reasons because of insufficient mosquito control , increasing urbanization and air travel [3] , [4] . As a result , about 2 . 5 billion people worldwide are threatened by dengue infection , with an estimated 50–100 million infections and 12 , 000 deaths , mainly among children , annually [5] , [6] . Determining the disease and economic burden of dengue is crucial in the allocation of scarce public health resources among competing health problems , and to allow for evaluations of the cost-effectiveness of interventions . However , few studies have estimated the economic impact and disease burden of dengue at the national level – while some studies have focused on resource-limited Latin American [7] , [8] , [9] , [10] , [11] , [12] and Asian countries [10] , [13] , [14] , [15] , [16] , [17] , the broad geographic range of the Aedes mosquito vectors also encompasses highly resourced countries and countries that will become highly resourced over the decades ahead . Studies of the health economics of dengue in such settings are scarce , even though the impact of dengue there is substantial . Singapore presents unique characteristics of dengue infection . Vector control programs introduced in the 1970s led to a considerable decline in vector density and DHF cases [18]; but despite the effectiveness of the vector control programs in reducing vector indices , dengue resurged in Singapore in the 1990s , due to a number of factors chief of which is the reduction of the herd protection in the 1970s and 1980s [19] . As a result , in contrast to other countries in Southeast Asia where dengue is primarily a pediatric disease , over 85% of the reported dengue cases in Singapore are young adults , and the incidence of dengue in the elderly is also growing [18] . Cyclical epidemics have occurred since the 1990s , peaking in 2005 when the incidence of reported confirmed DF was 335 per 100 , 000 population [20] . Several complexities bedevil the estimation of the economic impact of dengue at the national level . One of the main difficulties is the large proportion of cases that are not reported to national surveillance systems [1] . It is therefore necessary to adjust national statistics using independent cohort or serological studies [21] , [22] . Another complexity resides in the heterogeneity of costs: to obtain reliable estimates , it is necessary to combine medical costs with indirect costs borne by the individual , society ( e . g . school loss , work absenteeism ) , and vector control costs . In addition , due to the cyclic nature of dengue epidemics [23] , [24] , there is no single representative year for dengue infection in a particular region . To stabilize the estimates , projections need to be based on multi-year epidemic cycles [11] . In Singapore , the availability of serological and epidemiological studies independent of the national surveillance system provides a unique opportunity to understand the costs of dengue and allocate resources to control effectively . At the time of writing , there are tetravalent dengue vaccine candidates in various phases of clinical trials [25] , [26] , [27] , and the determination of cost-effectiveness of these vaccines has been identified as an urgent research need [25] . To address these issues , we performed an estimate of the economic impacts and disease burden of dengue illness in Singapore from 2000 to 2009 .
Annual national age-dependent DF and DHF cases reported from 2000 to 2009 were obtained from the national surveillance system [28] , [29] . Reporting of DF and DHF laboratory diagnosed cases to the Ministry of Health is legally mandated in Singapore . The cases notified by registered medical practitioners and accredited laboratories are collated and totals published weekly by the Communicable Diseases Division of the Ministry of Health [28] . Notification data were complemented with two dengue studies: ( a ) the prospective Early Dengue ( EDEN ) Infection and Outcomes study [20] , [30] that studied 455 individuals with undifferentiated fever at presentation and ( b ) the Adult Retrospective Dengue Study at Tan Tock Seng Hospital ( ARDENT ) that compiled characteristics of dengue patients who presented there from 2004 to 2008 . That hospital treated circa 40% of all reported dengue cases over this time period . Epidemic and economic parameters were obtained from EDEN and ARDENT , the literature , official sources and consultation with the National Environment Agency that is responsible for vector control ( Tables 1 and 2 ) . Underreporting was corrected using expansion factors [21] ( EF ) to scale reported cases . As more severe cases , such as those hospitalized , are much more likely to be reported than mild cases treated in ambulatory care , we distinguished between two expansion factors: EFh for hospitalized cases ( EFh was conservatively estimated from the lower bound estimates from the literature [22] , [31] ) ; and EFai for ambulatory cases in age group i . To estimate EFai for different age groups , we employed the results from a serological study in 2004 among 18 to 74 year olds as part of the National Health Survey [32] . The sampling was considered representative of the population because participants were recruited from different sentinel sites across the country , and selected by a combination of stratified and systematic sampling . The study results were used to infer total prevalence of dengue infection in each age group . The total dengue symptomatic prevalence in each age group was then estimated by multiplying the total number of serologically identified dengue cases by symptomatic rates . Given the uncertainty regarding symptomatic rates , we considered two main scenarios: ( i ) an age-dependent symptomatic rate [33]; and ( ii ) a constant range of symptomatic rates [34] , [35] . Seroconversion for children during that period was not available and we therefore assumed that the expansion factor for the young adults applied also for children . We considered both medical and non-medical direct costs . Direct medical costs were calculated for hospitalized and ambulatory cases . Daily hospitalization costs were obtained from the distribution of hospital bills per dengue patient provided by public Singaporean hospitals in 2010 for unsubsidized wards , divided by the median length of stay [36] . The median and 90th percentile daily costs per patient were used to construct a normal distribution ( Table 2 ) . The costs of ambulatory cases were obtained by multiplying the average number of visits per case by the unit costs of each visit ( Tables 1 and 2 ) . The costs included consultation fees , tests performed , and treatment costs ( Table 2 ) . Non-medical direct costs include individual and family transport costs ( Table 2 ) , and control costs which were obtained from the National Environment Agency . All costs were expressed in 2010 US dollars . Indirect costs were expressed per unspecified day and included reduction of work productivity , reduction of household services , loss of schooling , and increased need for caregivers . To estimate work productivity loss , the World Health Organization ( WHO ) proposes two main methods , both of which we used: the human capital and the friction cost method [37] . The human capital method values lost time or premature death using the individual's gross earnings , derived from the gross domestic product per capita . The more conservative ( lower cost ) friction cost method acknowledges that job absenteeism or death lead to productivity losses that can be temporarily offset by colleagues or by hiring new labour [38] , so that the loss of productivity occurs only during a friction time period ( assumed to be in our case 30 days for fatalities and to last as long as symptoms in non-fatal cases ) and productivity losses are offset according to the elasticity of annual labour time versus labour productivity ( Table 1 ) . Friction costs were then calculated by multiplying the length of the friction period with the expected average gross earnings in the period and the elasticity of annual labour time versus labour productivity . The costs of school days lost were estimated from the expenditures on schools in Singapore per student per day [10] , [39] . We also estimated the impact on household services , which are not paid for but represent important economic activity ( e . g . cleaning , cooking , caring for children and the elderly ) ( Table 2 ) [40] . Losses of household services affect not only the working population but also the young and the elderly [40] . We assumed that symptomatic children with two working parents but without household help caused further job absenteeism . For the elderly , only those outpatients living alone were assumed to require a caregiver ( Table 2 ) . For cases where care was given by a member of the family not actively working , the care givers incurred a loss of household services . Different disability weights for DF and DHF have been used in previous studies . For comparison , we employ three sets of disability weights: the first , based on recent literature estimates , reflects that all symptomatic cases are incapable of carrying out normal daily activities during illness [9] , [16] , [41] , [42]; the second based on WHO disability weights [43]; and the third has weights obtained in a empirical study that measured daily the losses in quality of life through the course of the infection using the visual thermometer-like scale technique [44] , [45] ( Table 1 ) . A disability weight of 1 was used for premature death . DALYs lost by each case were calculated using [41]:where D is the disability weight; r is the social discount rate; a is the age of the individual at the onset of symptoms; L is the duration of the disability or the years of life lost due to premature death expressed in years; C is the age-weighting correction constant; and β is the parameter from the age-weighting function . The age-weighting function represents the value of life at different ages [41] ( Table 1 ) . Because the eventual price of the vaccine is very uncertain , instead of assuming one single price we estimated the threshold price above which vaccination programs of different characteristics would not be cost-effective [25] . We compared the cost-effectiveness of the vaccines with the current vector control program ( $4 , 740 per DALY averted [46] ) and the criterion for cost-effective health interventions of WHO ( cost per DALY averted below 3 times the gross national income per capita [47] ) . We considered a scenario of mass vaccination allocated at random to a proportion of the population . The vaccination program could require two or three doses and could confer lifetime or only ten years immunity , leading to a total of four combinations of vaccine characteristics . Vaccine cost-effectiveness was evaluated for a time period of 75 years equivalent to the country's average life expectancy . Average annual estimates of DALYs and economic impacts were estimated per capita for each age group from 2000 to 2009 and used to project economic impacts and DALYs using the population levels and age structure in Singapore as predicted in the United Nations World Population Prospects 2010 Revision from 2012 to 2086 [48] . By 2086 , Singapore is expected to increase its population from 5 . 3 million of 6 . 5 million and to increase the proportion of habitants above 65 years old from 11% to 40% [48] . To estimate the critical vaccination coverage ( fc ) we considered the largest dengue epidemic in Singapore during the last 10 years , which occurred in 2005 [49] . It has been estimated that the basic reproductive number ( R0 , where an outbreak with an R0 below 1 dies out naturally [50] ) fell in the range 1 . 89–2 . 23 [49] . The vaccine coverage fc to bring the basic reproduction number R0 below 1 with a vaccine of efficacy γ is:we assumed that vaccine programs attaining herd protection greater than or equal to fc would prevent epidemics of dengue in Singapore ( ignoring localised non-sustainable outbreaks following importation ) .
The serological study in 2004 tested for IgG and IgM antibodies among 4152 individuals . Of the study population , 59 . 0% and 2 . 6% tested positive for dengue IgG and IgM that are indicative of past and recent infection ( within the last three months ) , respectively . The rate of recent infection ranged from 1 . 2% in individuals from 15 to 24 years old to 3 . 2% in individuals from 45 to 54 years old [32] . We assume that the number recently infected in the time period of the study is representative of the proportion infected in the country for that time period . From the 2004 population age structure , we estimated that 71 , 134 individuals were recently infected – encompassing symptomatic and asymptomatic cases – nationally in the period of the study . The number of reported cases during the same time period was 3104 . To obtain the number of symptomatic infected individuals , we multiplied the estimated number of individuals recently infected with symptomatic rates . Due to uncertainty in the asymptomatic rates in each age group , we considered two scenarios to obtain expansion factors . In the first scenario , we multiplied the expected number of infected individuals with age-dependent symptomatic rates obtained from a logistic model [33] . In a second scenario , we multiplied by a range of constant symptomatic rates for all ages of 24% to 53% [34] , [35] . We obtained two sets of expected number of infected symptomatic cases per age group , and compared this with the cases reported per age group . In the first scenario , the expansion factors ranged from 3 . 8 in the youngest group ( 0–24 years ) to 50 in the oldest group ( >55 years ) ( Table 1 ) . The second scenario yielded expansion factors ranging from 1 . 7–3 . 6 for 0–24 years to 12 . 2–26 . 5 for >55 years . The proportion of underreporting increased with age in both scenarios . The mean economic impact was mostly driven by the number of cases per year , resulting in high variability ( Figure 1 ) . For instance , combining the human capital method and non-age-dependent symptomatic rate scenarios during the 2005 epidemic led to costs of US $160 million , more than double the cost in 2000 ( $64 million , Figure 1 ) . Using the human capital method and non-age-dependent symptomatic rates , the distribution of costs from 2000 to 2009 excluding control costs had a mean of $415 million ( $41 . 5 million per year ) with 5th and 95th percentiles of $299 and 569 million ( Table 3 ) . Using the friction cost method , the mean was $351 million with 5th and 95th percentiles of $236 and 504 million . Total control costs were $500 million . Hence the total economic costs from 2000 to 2009 were $0 . 91 billion using the human capital method or $0 . 85 billion using the friction cost method . Using age-dependent symptomatic rates , the total cost estimates increased to $1 . 06 billion by the friction cost method and $1 . 15 billion by the human capital method ( Table 3 ) . The costs due to deaths decreased considerably under the friction cost method ( Table 3 ) . Whereas age-dependent symptomatic rates led to a higher proportion of costs due to ambulatory cases , hospitalized cases represented the largest share of costs when constant symptomatic rates were used ( Table 3 ) . The relative percentage of costs due to hospitalized cases and deaths decreases with respect to ambulatory costs when considering age-dependent symptomatic rates ( Table 3 , columns 5th and 6th ) . The reason is that , whereas using age-dependent symptomatic rates leads to higher expansion factors estimated for ambulatory cases than using constant symptomatic rates , the number of fatalities and the expansion factors for hospitalized cases does not vary . Using empirically derived disability weights [44] , average DALYs per 100 , 000 population were 8 . 7 ( 5th and 95th percentiles of 8 and 10 ) when using constant symptomatic rates and 14 ( 5th and 95th percentiles of 13 and 16 ) when using age-dependent symptomatic rates ( Table 4 ) . DF made up 24–32% of the disease burden , non-fatal DHF 33–57% , and dengue related deaths 9–43% ( Table 4 ) . For comparison with previous studies we repeated the analysis with disability scores from WHO [51] ( Table 4 , 8–8 . 9 DALYs per 100 , 000 population ) and with literature disability scores ( 16–27 DALYs per 100 , 000 population ) . We conservatively evaluated the cost-effectiveness of vaccines using constant symptomatic rates and empirically derived disability weights . Assuming the worst dengue epidemic of R0 = 2 . 5 and a vaccine of efficacy γ = 0 . 8 ( to reflect the difficulty to obtain a vaccine effective to the four serotypes ) , the critical herd protection needed against the four serotypes to prevent dengue epidemics ( fc ) would be 75% . The actual herd protection in Singapore is uncertain . Under the conservative assumption of a completely dengue-naïve population , a general vaccination program covering 75% of the population would be expected to prevent dengue epidemics within one year of completion . Conservatively assuming that vector control costs remain constant , we evaluated the vaccine programs' cost-effectiveness with increasing vaccine prices ( Figure 2 ) . The threshold price beyond which vaccines would not be cost-effective increased when fewer doses were needed and longer immunity was conferred . For low prices , vaccines presented net savings per DALY averted ( avoided costs were greater than vaccination costs ) and were very cost-effective . The price per dose threshold beyond which vaccines stopped being more cost-effective than the current vector control program ranged from $53 for mass vaccination requiring 3 doses and only conferring 10 years of immunity to $287 for vaccination requiring 2 doses and conferring lifetime immunity ( Figure 2 A ) . The thresholds for vaccine program cost-effectiveness in Singapore ranged from $95 and $491 per dose respectively ( Figure 2 B ) . For sensitivity analysis purposes , assuming instead that the population size and age structure remained constant in the future in Singapore , the thresholds for these vaccine programs to not be cost-effective in Singapore were lower ( $70 and $212 respectively ) due to their lower potential avoidance of the economic burden of dengue . We evaluated the sensitivity of the mean estimated disease burden , total costs and the benefit-cost ratio of the vaccination programs to the model parameters considering both ten-year and lifelong immunity . We performed univariate sensitivity analysis where all parameters were increased by 30% to evaluate their relative importance . The analysis showed that disease burden estimates were sensitive to the parameters: length of symptoms of DHF cases ( increase of 16% ) , disability weight for DF cases ( increase of 19% ) and the proportion of DHF cases ( increase of 10% ) . The total cost estimations were sensitive to the expansion factor used for hospitalized cases ( increase of 14% ) , hospitalization costs per day ( increase of 11% ) and length of hospital stay ( increase of 10% ) . The benefit-cost ratios of a mass vaccination program conferring lifelong immunity decreased when increasing the discount rate used ( 18% ) , costs of overhead , vaccine storage and distribution ( 22% ) , the number of doses needed ( 28% ) and the required herd protection to drive R0 below 1 ( 27% ) . The same direction in sensitivity was obtained for vaccine programs conferring ten-year immunity . However , the magnitude of the effects increased by 5% , on average , compared to the estimates for lifelong immunity .
The burden of disease due to dengue infections is high across at-risk areas of the world . Even with good vector control , as Singapore has , permanent reduction of dengue epidemics has proven to be impossible , and vaccines may be the only hope for sustained control . Our analysis demonstrates that dengue imposes a significant disease and economic burden in Singapore . The cost-effectiveness of vaccines will depend on their price and characteristics . To be able to estimate how cost-effective the vaccines will be , a baseline price can be used . Considering a price per dose of $5 ( based on the projected price of a dose of pneumococcal vaccine ) from a cost-effectiveness study for dengue vaccines in Panama , a middle income country [52] , all the vaccination programs considered would be very cost-effective and would provide net savings per DALY averted , which is in stark contrast with current costs of $4 , 740 per DALY averted by the vector control program [46] and with a vaccine cost-effectiveness evaluation of $50 per DALY averted with prices of routine vaccines in resource-limited settings ( $0 . 50 per dose in the public sector [53] ) . However , the price of recently developed vaccines in Singapore is much higher ( e . g . US $124 per dose of pneumoccal vaccine for 3 required doses ) [54] . If we use the considerably higher price of US $124 per dose as the baseline price , for mass vaccination to be cost-effective , it would have to guarantee lifetime immunity . At this high price the vaccination programs involving 3 doses and conferring only 10 years of immunity would not be more cost-effective than the vector control program ( Figure 2 ) ; however , the other programs involving lifetime immunity or only two doses would be more cost-effective than vector control . The comparison with the cost-effectiveness of the vector control program , however , is only illustrative: a vaccination program might still be preferred as long as the cost per DALY averted is below three times the gross national income per capita , since deaths due to dengue will be avoided and they would have been unavoidable under the current vector control program . This reflects the importance of the substantial incremental costs of the vector control program to attain lower than current disease burdens . Using three times per capita gross national income as the cost-effectiveness threshold [47] , the price threshold of the vaccines is very high . For instance , a vaccine involving three doses and conferring only ten years of immunity would be cost-effective up to a price threshold of $95 per dose ( Figure 2 ) . Our results on total costs were sensitive to hospitalization costs . This reflects the high hospitalization costs of Singapore relative to other South East Asian countries , e . g . Thailand , where non-hospitalized cases represented a substantial proportion of the overall burden of the disease [13] . Ambulatory cases , however , also represent a large share of the total costs due to dengue in Singapore ( Table 3 ) . The disease burden of dengue in Singapore ( 9–14 per 100 , 000 population ) using empirically derived disability weights is comparable to diseases like hepatitis B or syphilis ( 10 and 9 DALYs per 100 , 000 respectively ) . Using disability weights from the literature [9] , [16] dengue is comparable to meningitis and multiple sclerosis ( 22 and 19 DALYs per 100 , 000 , respectively , versus our estimated 16–27 ) [43] . It is , though , lower than other tropical and subtropical countries ( e . g . 66 in Puerto Rico [9] , 42 . 7 in Thailand [16] and 26 . 5 DALYs per 100000 in Brazil [11] , where the estimates were obtained using the same disability weights from the literature ) . Different estimates were also obtained when using WHO disability weights ( Table 4 ) , and consensus would be necessary for results to be comparable across studies . The lower disease burden per capita in Singapore compared to other studies may be due to its intensive vector control program , which represents the greatest component of dengue costs ( 42–59% ) . This may indicate that vector control in Singapore is attaining its maximum expected effectiveness . Given the high endemicity levels of dengue in Southeast Asia and the constant movement of persons and commodities between the countries in the region , increasing the efforts in vector control would likely meet with diminishing returns in dengue incidence . Hence , an effective dengue vaccine remains an attractive option for long-term and sustainable dengue prevention . We found that for reasonably low prices , vaccines are a promising and cost-effective option to reduce cases further . However , the extent to which vaccination might reduce necessary vector control expenditures is unknown , as vector control would still be necessary to prevent outbreaks of other mosquito-borne diseases e . g . chikungunya , which reached Singapore in 2008 [55] . On the other hand , if vector control activities were reduced as a result of the vaccination program , the cost-effectiveness of the vaccines would be higher . We preferred , however , to adopt a conservative approach by considering no reductions in the costs of the vector control as a result of the vaccination program . At the same time , improvements in vector control technology such as application of genetic modification techniques to the Sterile Insect Technique [56] or the introduction of the bacterium Wolbachia in mosquito populations [57] might be attractive alternatives or complements to vaccination , especially when the timeline for availability of vaccines , their eventual efficacy and length of protection are unknown . The main limitations of the study reside in the presence of uncertainty regarding key parameters . For instance , the vaccine might be less effective than assumed and be associated with high post-implementation costs . These factors would reduce the price threshold for which the vaccine would be cost-effective , but given the large margin of error for the vaccine to be cost-effective and the conservative approach adopted , we are confident that for reasonably low prices , the vaccine will be cost-effective in Singapore . We have not evaluated the cost-effectiveness of purely pediatric vaccines since their implementation would involve only partial protection of the population , and to estimate their cost-effectiveness would require an epidemic model capturing the dynamics of dengue in Singapore and able to relate partial population immunity with disease prevalence would be necessary . The construction of such an epidemic model would be a complex undertaking given the high uncertainty regarding the mechanisms that drive dengue dynamics in Singapore , and so this was left for future research . We postulate however that pediatric vaccines are likely to be also cost-effective [53] although it might take 10 to 20 years to notice their effect on disease burden reductions . The estimation of the economic and disease burdens also presented limitations . We were unable to estimate the intangible costs due to the extra burden of dengue epidemics to the health system; we also could not find a significant relationship between dengue cases and volume of tourism or other economic sectors in Singapore . The exclusion of these economic impacts makes our estimate of the economic burden conservative . Uncertainty was also present in the estimation of underreporting , or expansion factors . We were unable to estimate expansion factors for hospitalized cases and had to rely on existing literature . To gauge the lower bound of our estimates , assuming that all hospitalized cases are reported ( EFh = 1 ) , the total costs would be reduced by 18% . For ambulatory cases , the availability of national serological surveys compared with nationally reported cases gives strong confidence in our estimates . The symptomatic rate estimates however , presented high variability per age group and were scarce in the literature , leading to rather different disease burden estimates . To account for this uncertainty , two scenarios were considered , with broadly similar findings . Nonetheless , further research on symptomatic rates per age group would be beneficial to derive future estimates . Using age-dependent symptomatic rates , our estimates of expansion factors for age groups below 44 years old ( 3 . 8 , 13 . 1 and 24 . 3 ) were approximately equivalent to those in other studies , e . g . Brazil ( 2 . 1–10 ) , Colombia ( 4 . 5–18 ) or Puerto Rico ( 10–27 ) [11] but were higher in older age groups ( 45 . 3 and 50 ) . Using constant asymptomatic rates , the expansion factors matched these estimates from the literature . Comparison between studies is difficult because age-dependent expansion factors for multiple age-groups are rarely calculated . One exception is Meltzer et al . [9] , who estimated expansion factors of 10 for 0–15 years old and 27 for cases above 15 years old , which is in agreement with our results regarding increasing underreporting with age . The reason for underreporting increasing with age might be due to parental influence for the young [9] and/or atypical disease manifestations of the elderly [58] . In summary , we demonstrated the high economic and disease burden of dengue in Singapore and our results strongly support the implementation of vaccination programs if reasonably low prices are adopted . Vaccines will assist in Singapore as a mean to curb the economic and health burden of dengue illness . | Dengue illness is a tropical disease transmitted by mosquitoes that threatens more than one third of the worldwide population . Dengue has important economic consequences because of the burden to hospitals , work absenteeism and risk of death of symptomatic cases . Governments attempt to reduce the disease burden using costly mosquito control strategies such as habitat reduction and spraying insecticide . Despite such efforts , the number of cases remains high . Dengue vaccines are expected to be available in the near future and there is an urgent need to evaluate their cost-effectiveness , i . e . whether their cost will be justified by the reduction in disease burden they bring . For such an evaluation , we estimated the economic impacts of dengue in Singapore and the expected vaccine costs for different prices . In this way we estimated price thresholds for which vaccination is not cost-effective . This research provides useful estimates that will contribute to informed decisions regarding the adoption of dengue vaccination programs . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"socioeconomic",
"aspects",
"of",
"health",
"environmental",
"health",
"science",
"policy",
"and",
"economics",
"non-clinical",
"medicine",
"health",
"economics",
"cost-benefit",
"analysis",
"science",
"policy",
"dengue",
"fever",
"neglected",
"tropical",
"diseases",
"cost",
"effectiveness"
] | 2011 | Economic Impact of Dengue Illness and the Cost-Effectiveness of Future Vaccination Programs in Singapore |
The recently identified restriction factor tetherin/BST-2/CD317 is an interferon-inducible trans-membrane protein that restricts HIV-1 particle release in the absence of the HIV-1 countermeasure viral protein U ( Vpu ) . It is known that Tantalus monkey CV1 cells can be rendered non-permissive to HIV-1 release upon stimulation with type 1 interferon , despite the presence of Vpu , suggesting species-specific sensitivity of tetherin proteins to viral countermeasures such as Vpu . Here we demonstrate that Tantalus monkey tetherin restricts HIV-1 by nearly two orders of magnitude , but in contrast to human tetherin the Tantalus protein is insensitive to HIV-1 Vpu . We have investigated tetherin's sensitivity to Vpu using positive selection analyses , seeking evidence for evolutionary conflict between tetherin and viral countermeasures . We provide evidence that tetherin has undergone positive selection during primate evolution . Mutation of a single amino acid ( showing evidence of positive selection ) in the trans-membrane cap of human tetherin to that in Tantalus monkey ( T45I ) substantially impacts on sensitivity to HIV-1 Vpu , but not on antiviral activity . Finally , we provide evidence that cellular steady state levels of tetherin are substantially reduced by Vpu , and that the T45I mutation abrogates this effect . This study provides evidence that tetherin is important in protecting mammals against viral infection , and that the HIV-1 Vpu–mediated countermeasure is specifically adapted to act against human tetherin . It also emphasizes the power of selection analyses to illuminate the molecular details of host–virus interactions . This work suggests that tetherin binding agents might protect it from viral encoded countermeasures and thus make powerful antivirals .
Retroviruses are obligate cellular parasites and as such rely on a wide variety of host proteins and pathways to complete their lifecycle . Moreover , they are subject to a variety of cellular antiviral activities that they must either overcome or avoid in order to successfully infect a cell . Together these positive and negative acting host factors combine to give primate lentiviruses narrow host ranges . For example , HIV-1 can only replicate in humans , chimpanzees and possibly gorillas [1] . A particular class of interferon inducible , cellular , innate immune factors , active against retroviruses , is referred to as restriction factors . These include TRIM5α [2] , APOBEC3G ( A3G ) , APOBEC3F ( A3F ) [3] , [4] and tetherin/BST2/CD317 [5] , [6] . Tetherin has been demonstrated to tether nascent retroviral virions to the plasma membrane , preventing their release from the infected cell . Instead , they are recruited back into the cell in endosomes for eventual destruction in the lysosome [5]–[8] . Tetherin has predicted trans-membrane and coiled coil regions as well as a predicted GPI anchor site [9] . It has also been shown to exist as a dimer and is glycosylated at two sites in its extracellular domain [10] although glycosylation does not appear to be important for restriction of Lassa or Marburg virus [11] . In a striking parallel to the antagonistic relationship between the antiviral A3G/F proteins and their HIV-1 encoded countermeasure Vif , the HIV-1 viral protein U ( Vpu ) counteracts the antiviral activity of tetherin [5] , [6] . Vpu is a 16 kilodalton oligomeric type 1 trans-membrane protein encoded by an alternative reading frame in the env gene [12] . The antagonistic relationship between innate antiviral proteins , and the viruses that they restrict , is an excellent example of the Red Queen hypothesis [13] . This hypothesis proposes that pathogens and their hosts are locked in evolutionary conflict , each subject to selective pressure from the other to gain the advantage . This evolutionary arms race leads to alternate change followed by advantage and an overall maintenance of the relationship between host and pathogen . In support of this hypothesis , proteins such as TRIM5α and APOBEC3G have been shown to be under strong positive selection pressure throughout primate evolution , presumably from viruses that they target [14] , [15] . Indeed , the study of adaptive selection and the analysis of species-specific restriction have illuminated details of the evolution of antiviral proteins as they change in response to rapidly evolving pathogens . Here we provide evidence for positive selection of tetherin and demonstrate that positively selected residues impact on sensitivity to Vpu but not on tetherin's anti HIV-1 activity . Furthermore , we show that mutation of a single residue renders human tetherin resistant to HIV-1 Vpu-mediated cellular depletion .
To measure tetherin antiviral activity and its abrogation by the viral Vpu protein , we utilised HIV-1 YFP encoding vectors . Transfection of 293T cells with HIV-1 vector plasmids leads to production and release of HIV-1 virions containing a YFP encoding genome . The efficiency of virion release can then be measured by titration of the 293T supernatant on permissive target cells . Expression of viral proteins and release of virions from transfected cells was also assayed by western blot , detecting HIV-1 capsid in extracts from the transfected cells and the supernatant respectively . Expression of HIV-1 vector plasmids in the absence of tetherin or Vpu led to production of HIV-1 YFP with a titer of around 5×107 infectious units/ml . Co-expression of human tetherin significantly reduced infectious HIV-1 vector production and co-expression of HIV-1 Vpu completely rescued HIV-1 YFP release ( Figure 1A ) . These data , demonstrating restriction of HIV-1 by human tetherin , and the ability of HIV-1 Vpu to act as countermeasure to tetherin , are concordant with described observations [5] , [6] . Tantalus monkey CV1 cells are able to release HIV-1 in a Vpu insensitive way [8] . Furthermore , after interferon treatment these cells support reduced HIV-1 release . We hypothesised that this might be due to interferon induced expression of a tetherin protein that was insensitive to HIV-1 Vpu . To test this we cloned the Tantalus monkey tetherin from CV1 cells and co-expressed it with HIV-1 vector plasmids as above . Indeed , expression of the Tantalus tetherin protein restricted HIV-1 YFP release , by almost 2 orders of magnitude ( Figure 1A ) . Importantly , and in concordance with HIV-1 Vpu's inability to stimulate HIV-1 release from CV1 cells [8] , restriction by Tantalus tetherin was insensitive to co-expression of HIV-1 Vpu . This observation suggests that the Vpu mediated tetherin countermeasure is species-specific and that the HIV-1 Vpu protein cannot counteract the antiviral activity of the Tantalus tetherin protein . Measurement of Gag levels in the supernatant and transfected cells by western blot with a p24 CA antibody demonstrated that viral titers ( Figure 1A ) reflect the amount of p24 released into the supernatant ( Figure 1B ) and that tetherin expression did not impact on Gag expression levels ( Figure 1C ) . β actin was measured as a loading control ( Figure 1D ) . We note that there is evidence for increased levels of protease cleaved Gag in the cell extracts in the presence of tetherin restriction , consistent with the notion that maturing particles are tethered to the surface of the restrictive cells . It is possible that Tantalus tetherin is insensitive to Vpu because it is expressed more efficiently than the human protein and it therefore saturates the HIV-1 Vpu protein . To test this we titrated both human ( Figure 1E ) and Tantalus tetherin ( Figure 1F ) against a fixed dose of Vpu and measured the titer of the released virus . In fact human tetherin was counteracted by Vpu at high or low doses whereas Tantalus tetherin was not significantly counteracted by HIV-1 Vpu , even when the dose of tetherin was low . These data are consistent with the notion that Tantalus tetherin is insensitive to the HIV-1 encoded tetherin countermeasure Vpu . Species specificity of the tetherin/Vpu interaction is reminiscent of the species specificity of HIV-1 Vif activity against primate APOBEC3G proteins , as well as the species specificity of TRIM5α against retroviruses . In both of these examples the determinants of specificity can be revealed by analysis of positive selection in the species-specific variants of each restriction factor [14]–[17] . We therefore gathered tetherin sequences from a variety of primates and aligned them to the Tantalus monkey tetherin sequence ( Figure 2A ) . The alignment revealed that primate tetherin sequences are divergent ( mean pairwise genetic difference of 0 . 116 nucleotide substitutions per site , standard deviation 0 . 085 substitutions/sites ) , yet 93 out of 180 amino acid sites are conserved along the primate alignment , excluding positions with gaps . We examined the alignment of primate tetherin sequences for evidence of heterogeneity of synonymous ( dN ) and non-synonymous ( dS ) substitution rates , indicative of adaptive selection . An excess of non-synonymous substitutions , which lead to protein sequence change , compared to synonymous substitutions , which do not , ( dN/dS>1 ) is traditionally regarded as indicative of positive ( or adaptive ) selection . Conversely , a dN/dS<1 suggests negative ( or purifying ) selection . Although the average rates of synonymous changes exceeded rates of non-synonymous changes across the sequence alignment , reflecting a predominance of purifying selection on the tetherin genes ( average dN/dS = 0 . 93; 95% Confidence Intervals = 0 . 76; 1 . 11 ) , evidence for positive selection was found when maximum likelihood models allowing variable dN/dS ratios among sites were applied to the data . The model allowing sites to evolve under positive selection had a significantly better fit to the data than the model assuming no positive selection ( likelihood ratio test with 2 degrees of freedom; p = 0 . 018 ) . Furthermore , analyses of codon-specific positive selection in the primate lineage revealed fifteen residues potentially under adaptive selection , five of which , positions 24 , 26 , 30 , 36 and 45 in the human tetherin sequence , were found in or bordering the predicted trans-membrane domain ( Figure 2A , Table 1 ) . We note that the three selected trans-membrane residues present in the central helix are predicted to be on the same side of the helix and therefore in close proximity to one another ( Figure 2B ) . Additional sites showing evidence of positive selection were found in the predicted cytoplasmic and C terminal extra-cellular domains ( Table 1 ) . Knowing that HIV-1 Vpu counteracts human tetherin we hypothesised that the differences between primate tetherin sequences might be due to selective pressure from pathogenic retroviruses encoding tetherin countermeasures . We focused on the trans-membrane domain as a likely site for Vpu interaction because both tetherin and Vpu are integral membrane proteins and replacing the Vpu trans-membrane domain with that from CD8 causes mislocalisation and loss of Vpu activity [7] . Furthermore , a non-functional Vpu with a scrambled trans-membrane region co-localised less extensively with tetherin [6] . We hypothesised that changing the trans-membrane region residues in the human tetherin protein to those in the Tantalus monkey protein should impact on sensitivity to HIV-1 Vpu . Of positions 24 , 26 , 30 , 36 and 45 , which are in or bordering the predicted trans-membrane region ( Figure 2 ) , position 24 is conserved between human and Tantalus monkey . We therefore made a human quadruple tetherin mutant I26V , V30G , I36L , T45I and tested its antiviral activity and sensitivity to HIV-1 Vpu . The wild-type human tetherin suppressed HIV-1 release reducing infectious titer by 78 fold and release was completely rescued by HIV-1 Vpu expression ( Figure 3A ) . Conversely , the human tetherin quadruple mutant ( Quad ) was able to potently suppress HIV-1 release but was only weakly rescued by co-expression of HIV-1 Vpu , 4 fold vs 78 fold rescue for the wild-type protein ( Figure 3A–3D ) . Importantly , the mutations did not significantly reduce tetherin's antiviral activity on HIV-1 release ( Figure 3A , black bars ) . In order to examine the contribution of each selected residue to Vpu sensitivity we tested single mutants for antiviral activity and Vpu sensitivity . In fact , mutating human residue 30 ( V30G ) moderately reduced its Vpu sensitivity ( from 78 to 15 fold ) whereas mutating residue 45 ( T45I ) had a similar impact as mutating all 4 residues ( 5 fold versus 4 fold rescue on Vpu expression ) . Remarkably , it appears that human tetherin can escape the HIV-1 encoded tetherin countermeasure Vpu and restrict HIV-1 if a single tetherin amino acid is changed to reflect the Tantalus monkey sequence . Indeed , the evidence for positive selection suggests that the tetherin gene has been under pressure to change at this position during primate evolution . For all of the experiments in Figure 3A we measured Gag levels by western blot in the cells and p24 levels in the supernatant ( Figure 3B–3D ) . In each case p24 levels in the supernatant reflected the titer of the virus as plotted ( Figure 3B ) Furthermore , Gag expression levels were similar in the cells and unaffected by tetherin expression ( Figure 3C ) . β actin levels in cell lysates were measured as a loading control ( Figure 3D ) . It is formally possible that the tetherin mutations responsible for reduced sensitivity to Vpu impacted on its expression levels . In order to control for this possibility , and in the absence of a tetherin antibody , we appended an N terminal epitope tag to the wild-type and quadruple mutant human tetherin proteins and performed an assay for tetherin function and Vpu sensitivity , as above . Surprisingly , we found that the tag slightly reduced human tetherin's antiviral activity , compare Figure 4A to Figure 3A . Assay of p24 in supernatant ( Figure 4B ) and cell lysate ( Figure 4C ) confirmed this observation . Nonetheless , we measured the expression levels of the tagged tetherin proteins by western blot , reasoning that if expression levels were changed by the four mutations then this would be evident in expression levels of the tagged proteins , despite their reduced activity . In fact , the wild-type and mutant unglycosylated proteins were expressed at similar levels in the absence of Vpu ( Figure 4D ) . Importantly , co-transfection of Vpu led to reduction in the amount of tetherin detected , both in a cleared RIPA cell lysate supernatant and the associated pellet as described [18] ( Figure 4D and 4F ) . β actin was measured as a loading control in both supernatant and pellet ( Figure 4E and 4G ) . These observations are concordant with those reported by Bartee et al who reported lower levels of tetherin protein in the presence of Vpu [19] . We obtained similar results when the experiment was carried out using the human single point mutant of tetherin T45I demonstrating that this single mutation can render tetherin insensitive to Vpu ( Figure 5 ) . These experiments suggest that the steady state level of tetherin is reduced by co-expression of HIV-1 Vpu , and that mutation at positively selected sites leads to a persistence of the tetherin protein presumably due to impaired interaction with the tetherin trans-membrane region . Next we considered whether inhibition of the proteasome impacts on the Vpu mediated reduction of tetherin steady state levels . We co-expressed HIV-1 vectors and N-terminally tagged wild-type human tetherin , and examined the impact of HIV-1 Vpu-HA co-expression in the presence and absence of the proteasome inhibitor MG132 ( Figure 6 ) . Consistent with previous observations , MG132 lowered infectious titres , a phenomenon attributed to depletion of ubiquitin pools required for HIV-1 maturation and release [20] ( Figure 6A ) . In addition , MG132 increased cellular levels of tetherin in the absence of Vpu , consistent with the notion that tetherin ( like many other cellular proteins ) is cycled within the host cell using ubiquitin dependent pathways . MG132 also significantly increased levels of Vpu ( Figure 6C , compare left and right panels ) , consistent with previous observations [21] . Vpu leads to a loss in tetherin expression levels and a rescue of HIV-1 titre in the supernatant ( Figure 4 and Figure 5 ) . However , treatment with the proteasome inhibitor MG132 reversed the depletion of tetherin levels induced by Vpu . The drug reduced tetherin antagonism by Vpu although tetherin continued to partially inhibit the release of HIV-1 , despite Vpu's presence ( Figure 6B , compare left and right panels ) . This observation suggests that the proteasome is involved in Vpu mediated reduction of tetherin levels . Moreover , Vpu's ability to partially rescue HIV-1 release , despite being inhibited for tetherin degradation , suggests that it can inhibit tetherin via sequestration or mislocalisation . Indeed , Vpu has been demonstrated to reduce tetherin's cell surface expression and MG132 treatment did not completely restore it to the surface levels seen in the absence of Vpu , again suggesting sequestration or mislocalisation of tetherin by Vpu [6] . Since inhibiting the proteasome impacts on the levels of free ubiquitin it is also possible that tetherin is degraded by a proteasome independent pathway such as trafficking to lysosomes via endosomal sorting pathways , which are known to depend on ubiquitination [22] . We also note that MG132 treatment increases tetherin levels , raising the possibility of Vpu saturation . However , the fact that we still see maximal Vpu activity when the Tetherin plasmid dose is increased from 100 to 400 nanograms ( Figure 1 ) , as well as increased levels of Vpu ( Figure 6 ) , suggests that increased levels of tetherin protein are unlikely to explain the inhibition of Vpu mediated tetherin depletion by MG132 treatment .
Here we provide evidence that the tetherin/CD317/BST-2 host restriction factor has been subject to positive selection during mammalian evolution . We hypothesised that the selected changes might impact on sensitivity to viral encoded tetherin countermeasures and in support of this human tetherin becomes largely insensitive to the HIV-1 encoded countermeasure , the Vpu protein , when it is mutated to represent the Tantalus monkey sequence at a single position ( T45I ) . A second positively selected residue in the trans-membrane region V30 also impacts on sensitivity to HIV-1 Vpu , although less dramatically than T45I , when mutated to glycine . We also show that the single point mutant T45I is able to render human tetherin resistant to Vpu-mediated cellular depletion , and furthermore that the mechanism involves the proteasome or a ubiquitin-dependent pathway . This is consistent with the observation that Vpu recruits CD4 to the βTrCP subunit of the SCF ( βTrCP ) ubiquitin ligase complex leading to degradation via the proteasome [23] . Concordantly , Goffinet and colleagues have recently reported that HIV-1 Vpu mediates proteasomal degradation of human but not rodent tetherin proteins and that this is abrogated by inhibition of the proteasome with ALLN or clasto-lactacysteine [24] . In the final stages of preparation of this manuscript , McNatt and colleagues reported the findings of a similar study on species-specificity of tetherin's responsiveness to HIV-1 Vpu [25] . In contrast to our approach these investigators used chimeric constructs to show that the TM region conferred sensitivity to HIV-1 Vpu , before locating specific sensitivity determinants using systematic mutagenesis . Subsequent positive selection analysis was consistent with our findings concluding that tetherin has been under positive selection in primates . The observation that a single mutation ( T45I ) can render tetherin largely insensitive to HIV-1 Vpu mediated degradation is reminiscent of point mutations impacting on APOBEC3G's sensitivity to HIV-1 Vif [26] as well as point mutations in either capsid [27] , [28] , TRIM5 [29] , TRIMCyp [30] , or Fv1 [31] , [32] , which strongly impact on sensitivity to restriction . Indeed , it appears to be a common theme of the interaction between restriction factors and viral proteins that only one or two amino acids dictate the difference between replication and restricted infection . In this study we have focused on the trans-membrane domain of tetherin . It seems likely that other tetherin sensitive viruses and their countermeasures may have caused selection at the positions outside of the trans-membrane domain . Vpu has been shown to facilitate the release of distantly related viruses including the gamma retrovirus murine leukaemia virus , as well as the sheep lentivirus maedi-visna virus [5] , [33] . Vpu has also been shown to improve release of VLPs derived from the filovirus ebola [8] and Marburg and Lassa viruses [11] . These viruses do not appear to encode Vpu homologues and it is unclear whether they encode tetherin countermeasures . However , there is evidence that certain viruses have tetherin countermeasures unrelated to HIV-1 Vpu . For example , Kaposi's sarcoma associated herpes virus ( KSHV ) encodes a protein K5 , known to reduce tetherin cell surface expression [19] . Moreover , some primate lentiviruses , such as HIV-2 , are thought to have anti-tetherin function mediated by their envelope protein [34]–[36] . Moreover , Ebola virus glycoprotein has recently been shown to counteract tetherin antiviral activity [37] . We therefore speculate that viruses with countermeasures unrelated to HIV-1 Vpu are responsible for the positive selection of tetherin outside of the trans-membrane region . Our observations are evidence for a dynamic evolutionary arms race , as described by the Red Queen hypothesis , between tetherin and virus encoded countermeasures such as Vpu . They are strong evidence for tetherin having a critical role in innate immunity against retroviral infection throughout mammalian evolutionary history and underline the utility of seeking evidence for positive selection to reveal details of host virus interactions . The details of the antiviral mechanism of tetherin have been partially uncovered . Tetherin restricted viruses are prevented from leaving the surface of infected cells and are subsequently endocytosed in a Rab5a dependent way [5]–[8] . The restricted viruses achieve a very late stage of viral budding and can be released by proteolytic cleavage from infected cells [5] , [7] . Vpu appears to counteract tetherin by sequestering it from the cell surface [5] , [6] and our data support recent findings that Vpu causes tetherin degradation via the proteasome . This observation suggests that Vpu may work in the same way as Vif and act as an adapter protein that recruits tetherin to be degraded [38] , [39] . Future work will include identification of countermeasures from other viruses , which are likely to have independent mechanisms for antagonising tetherin . The potential for translational application of these findings is substantial . Identification of inhibitors for Vpu , or indeed other virus-encoded countermeasures , could have powerful therapeutic potential . The multifunctional nature of Vpu , for example its ability to reduce CD4 surface expression [40] , will presumably improve potency of Vpu inhibition . We also envisage tetherin binding drugs that protect it from multiple viral encoded counter measures and are therefore broadly active against different classes of enveloped viruses .
Primate tetherin sequences were retrieved using BLAST [41] and manually aligned . Sequences used were Homo sapiens human ( NM004335 ) , Pan troglodytes chimpanzee ( XM_512491 ) , Macaca fascicularis cynomolgus macaque ( CJ479048 ) , Macaca nemestrina pigtailed macaque ( DY743778 ) , Macaca mulatta rhesus macaque ( CB554098 ) , Chlorocebus pygerythrus vervet monkey . Tetherin sequences from orangutan and marmoset were inferred using BLAT ( http://genome-mirror . duhs . duke . edu/cgi-bin/hgBlat ) on the Pongo pygmaeus abelii orangutan genome and Callithrix jacchus marmoset genome . Orangutan sequence was confirmed by PCR cloning individual exons and sequencing . Tantalus monkey and pig tetherin cDNAs were PCR cloned from the Chlorocebus tantalus ( Tantalus monkey ) CV1 cell line or the porcine cell line ST IOWA respectively , as described [42] using Tantalus monkey primers Fwd 5′ - CGATGCGGCCGCCCACCATGGCACCTATTTTGTATG Rev 5′ – GCCGATCTCGAGTCACAGCAGCAGAGCGCTCAAGC and pig primers Fwd 5′-ATGTCACCTAGTTTGTATTCC-3′ and Rev 5′-ACACCTCAGGTCAGCAG-3′ and inserted into pcDNA3 . 1 ( Clontech ) . Cv1 cells are assumed to be derived from Tantalus monkey due to characteristic polymorphism in the CCR5 gene [43] . Four independent clones of each cDNA were sequenced . Tetherin sequences have accession numbers FJ345303 Tantalus monkey and FJ527910 pig . Site directed mutagenesis was performed using QuikChange ( Stratagene ) . Human wild-type and mutant tetherin proteins were epitope tagged by cloning into the pCDNA4 vector encoding an N terminal Xpress epitope tag ( Invitrogen ) between the Not1 and Xho1 sites . Pairwise genetic distances between the nine primate tetherin sequences were calculated under the General Time Reversible model of nucleotide substitutions with proportion of invariable sites and gamma-distributed rate heterogeneity , using the program Paup* [44] . Evidence for positive selection in the tetherin gene along the primate lineage was sought by comparison of synonymous ( dS ) and non-synonymous ( dN ) substitution rates using the program codeML from the PAML package [45] and the Random Effect Likelihood ( REL ) [46] method implemented by the Datamonkey web-based facility [47] . An excess of non-synonymous substitutions compared to synonymous substitutions ( i . e . dN/dS>1 ) is thought to be indicative of positive ( or diversifying ) selection , whereas dN/dS<1 suggests negative ( or purifying ) selection . In codeML , the sequence alignment and a corresponding neighbor-joining phylogeny were successively submitted to a model in which sites are distributed into categories where dN/dS is beta-distributed between 0 and 1 ( M7 ) and to a model in which sites are distributed into categories where dN/dS is beta-distributed between 0 and 1 , with an extra category where dN/dS is freely estimated ( M8 ) . A significant better fit of M8 than M7 , as indicated by a likelihood ratio test with 2 degrees of freedom , was taken as an evidence of positive selection . The REL algorithm was used to identify potential codon positions evolving under positive selection . After estimating branch lengths and substitution rates under the Hasegawa-Kishino-Yano ( HKY85 ) model of evolution , the MG94xHKY85 codon model [48] was fitted to the data to obtain independent rate distributions for dN and dS . For each codon , Bayes Factors for the events that dN<dS ( indicative of negative selection ) and that dN>dS ( indicative of positive selection ) at that site were estimated . A Bayes Factor of 20 or more in favor of dN>dS was considered strong support for adaptive selection at that site . Preparation of VSV-G pseudotyped , YFP encoding HIV-1 has been described [49] . Briefly 106 293T cells per well were cotransfected in six well plates using 6 µl Fugene-6 ( Roche ) with the gag-pol expression vector p8 . 91 ( 250 ng ) [50] , pMDG encoding the Vesticular Stomatis Virus G glycoprotein ( VSV-G ) ( 250 ng ) [51] and HIV-1 vector encoding YFP ( 375 ng ) [52] . 100 ng of tetherin constructs were co-transfected along with either 200 ng of HIV-1 Vpu or empty vector ( pCDNA3 . 1 , Invitrogen ) . After 48 hours the supernatant was harvested , filtered and titered on 293T cells as described [49] . HIV-1 p24 was measured in supernatants or cell pellets by western blot as described [18] using HIV-1 p24 monoclonal antibody ( 183-H12-5C ) , a gift from the NIH AIDS Research and Reference Reagent Programme . Membranes were then stripped and reprobed for β actin as a loading control . Tetherin extracts were made by lysing cells in RIPA buffer . Cleared lysate was added to laemmli buffer and the pellet was solubilised in laemmli buffer by sonication . Samples were then boiled before separation by SDS PAGE , all as previously described [18] . Xpress epitope tag was detected using mouse anti-Xpress antibody ( Invitrogen ) . HA epitope tag was detected using mouse anti-HA antibody ( Covance ) . | Pathogenic viruses have been infecting mammals throughout their evolution , exerting selective pressure to evolve systems to limit or eliminate these parasites . For example , intracellular proteins called restriction factors specifically restrict viral infection by targeting important viral processes . The restriction factor tetherin tethers newly formed HIV-1 virions to the surface of infected cells , preventing egress and further infection . In order to counteract tetherin , HIV-1 encodes a membrane-associated protein called Vpu that abrogates tetherin activity . Here we show that HIV-1 Vpu is inactive against tetherin from Tantalus monkeys and that this is due to a single amino acid that differs between human and tantalus monkey tetherin sequences . Evidence for positive selection at this position suggests that viral infections have provided the Darwinian selective pressure leading to this change . We also show that Vpu expression leads to a loss of tetherin protein in cells . Mutation of human tetherin protects it from HIV-1 Vpu activity , allowing functional protein expression and restriction of viral release . This study underlines the utility of selection analyses to reveal determinants of antiviral specificity and is strong evidence for the host–virus arms race described by the Red Queen hypothesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics",
"infectious",
"diseases",
"virology/virulence",
"factors",
"and",
"mechanisms",
"evolutionary",
"biology/human",
"evolution",
"virology/virion",
"structure,",
"assembly,",
"and",
"egress",
"virology/immunodeficiency",
"viruses",
"virology/animal",
"models",
"of",
"infection",
"evolutionary",
"biology/genomics",
"virology/mechanisms",
"of",
"resistance",
"and",
"susceptibility,",
"including",
"host",
"genetics",
"infectious",
"diseases/hiv",
"infection",
"and",
"aids",
"evolutionary",
"biology/bioinformatics",
"evolutionary",
"biology",
"molecular",
"biology",
"virology/host",
"antiviral",
"responses"
] | 2009 | Mutation of a Single Residue Renders Human Tetherin Resistant to HIV-1 Vpu-Mediated Depletion |
Characterisation of the T cell receptors ( TCR ) involved in immune responses is important for the design of vaccines and immunotherapies for cancer and autoimmune disease . The specificity of the interaction between the TCR heterodimer and its peptide-MHC ligand derives largely from the juxtaposed hypervariable CDR3 regions on the TCRα and TCRβ chains , and obtaining the paired sequences of these regions is a standard for functionally defining the TCR . A brute force approach to identifying the TCRs in a population of T cells is to use high-throughput single-cell sequencing , but currently this process remains costly and risks missing small clones . Alternatively , CDR3α and CDR3β sequences can be associated using their frequency of co-occurrence in independent samples , but this approach can be confounded by the sharing of CDR3α and CDR3β across clones , commonly observed within epitope-specific T cell populations . The accurate , exhaustive , and economical recovery of TCR sequences from such populations therefore remains a challenging problem . Here we describe an algorithm for performing frequency-based pairing ( alphabetr ) that accommodates CDR3α- and CDR3β-sharing , cells expressing two TCRα chains , and multiple forms of sequencing error . The algorithm also yields accurate estimates of clonal frequencies .
The ability of T cells to recognise antigens is conferred by a process of gene rearrangement that generates a diverse repertoire of T cell receptors ( TCR ) , or clonotypes . Identifying the clonotypes involved in responses against pathogens and tumours or those involved in autoimmune disease can guide the design of vaccines and immunotherapies . In addition , the breadth of a T cell response correlates positively with the efficiency of control in many viral infections [1–3] . Thus , a method to characterise the diversity of antigen-specific responses—that is , the participating TCRs and their relative abundances—may yield potential correlates of protection . The αβ TCR is a heterodimer , generated by a combination of ordered recombination of V , D , and J gene segments for the β chain and V and J gene segments for the α chain , together with random nucleotide insertions and deletions between the gene segments . The hypervariable CDR3α and CDR3β regions contact the peptide-loaded MHC ( pMHC ) most closely and so are considered the primary source of specificity in binding . From hereon we will use the term ‘chain’ interchangeably with the CDR3 region of the TCRα or TCRβ . Historically , the CDR3β has been thought to contribute more to the interaction with pMHC due to its greater theoretical diversity . However , studies of crystal structures have demonstrated that CDR3α loops can have equal or greater contact with pMHC , as measured by buried surface area [4] . Epitope-specific immune responses also show biases for certain V and J segments in both α and β chains [5 , 6] , suggesting both chains contribute to the binding affinity . The α chain may even play a dominant role in the recognition of certain antigens [7] . Characterising the true extent of clonal diversity within T cell populations therefore requires resolving the paired CDR3α and CDR3β sequences within them . Standard methods of multiplex PCR and high-throughput sequencing lose this pairing information and as a result are commonly used to analyze either the α or β chains alone [8–11] . More recent studies have used single-cell sequencing approaches to identify TCRαβ pairs , and , analogously , the paired CDR3 sequences from the heavy and light chains of the B cell receptor . These approaches include using single-cell sorting and RT-PCR [12–14] , also with barcoding [15–18]; and variations of emulsion techniques to isolate single cells and amplify with PCR [18–20] . Drawbacks of these techniques include limited scalability , the risk of undersampling rare clones and so underestimating diversity , imprecise information regarding clonal abundances , and the need to use customised equipment [18 , 21] . An alternative strategy is to use statistical methods to associate the CDR3α and CDR3β sequences obtained from bulk sequencing of multiple subsamples of T cells taken from the parent population of interest [22] . This approach exploits the fact that paired chains will tend to appear together in samples and uses the frequencies of these co-occurrences to associate them . A similar approach has been used to pair the heavy and light chains of B cells [23] . Because frequency-based pairing can be applied to large samples of cells , it has the potential to recover antigen receptors in greater depth and more economically than single-cell approaches , as well as providing more precise estimates of clonal frequencies . However , several properties of antigen-specific T cell populations present difficult challenges to this method . First , there is accumulating evidence from single-cell sequencing studies that , within an individual , T cell clonotypes specific for a given pMHC can exhibit sharing of both α and β chains [13 , 14 , 17 , 19] . Second , between 10–30% of T cells possess two productive α chains [13 , 24 , 25] and 6–7% of T cells possess two productive β chains [25 , 26] . The combination of sharing of α or β chains , dual TCRs , and sequencing errors can confound frequency-based methods that assume unique pairings . To illustrate , frequent co-occurrences of the three chains α1α2β in samples may derive from a single clone possessing two α chains or two clones α1β and α2β present at similar abundances , and the two possibilities are difficult to distinguish . Here we describe a novel approach to frequency-based pairing that addresses these issues and identifies TCRαβ clones and their relative abundances using high-throughput sequencing of CDR3α and CDR3β regions . Our approach is optimised for antigen-specific populations and designed for use with cells recovered from typically-sized human blood samples . It is specifically designed to deal with promiscuity in αβ pairing , dual TCRα clones , and high rates of sequencing errors . By drawing on bulk sequencing data , we increase the efficiency of detection of rare responding clones and reduce the costs associated with single-cell high-throughput sequencing methods . The method also goes beyond other currently available approaches , yielding estimates of the frequencies of clones within their parent populations .
Performing frequency-based pairing is in principle relatively straightforward if each clone is identified by two unique TCRα and TCRβ chains . However , single-cell analyses of epitope-specific T cell populations in mice and humans have revealed significant levels of sharing of both CDR3α and CDR3β sequences at the amino acid level across clones within individuals ( Table 1 ) . The current upper limits on estimates of the number of unique TCRβ chains in the naive CD4 or CD8 pools are 106 in mice [27] and 108 in humans [28] . As a consequence , sequencing of samples of naive T cells typically results in nearly every cell possessing a unique TCRβ ( see S1 Text , Section 1 ) . Nevertheless , the true diversity of the naive repertoire may be even greater; due to the sequence of events involved in the generation of the TCR in the thymus , we expect each TCRβ to be shared with many TCRα within the naive CD4 and CD8 T cell pools . In mice , thymocytes undergo 6–9 divisions following TCRβ rearrangement at the DN3 stage [29–32] , generating 64–512 cells which then undergo independent TCRα rearrangements . Assuming 5% of these TCRαβ precursors survive selection [33–36] leaves TCRβ clone sizes of 3–25 cells post-selection [27] . Thymocytes may undergo 1 or 2 divisions at the single-positive CD4 or CD8 stage before leaving the thymus [36]; if we assume a 2-fold expansion here on average , each αβ T cell precursor at DN3 generates 6–50 new naive cells with identical TCRβ chains , comprising 3–25 unique TCRαβ clones of typically 2 cells . Comparable estimates of TCRβ clone sizes have been obtained elsewhere [27 , 32] . There is also evidence that TCRβ-clone sizes can be augmented by convergent recombination of the TCRβ chain [8 , 37] . If a particular CDR3β contributes strongly to the affinity of binding to a given peptide-MHC , then because the recruitment of naive antigen-specific T cells appears to be highly efficient [38] , our rough quantification of TCRαβ clonality in thymopoesis is consistent with the observation that TCRβ-sharing is commonly found within epitope-specific populations ( Table 1 ) . Because the rearrangement of the TCRα follows that of the TCRβ , any sharing of CDR3α sequences across clones presumably arises from convergent recombination . Sharing then would be expected to arise most frequently for sequences that are close to germline , containing relatively few random N-nucleotide insertions . To examine this possibility , we immunised an HLA-A2 human volunteer with the live attenuated yellow fever vaccine YFV-17D , took a peripheral blood sample 15 days post-vaccination , and used dextramer staining and single-cell RNAseq to recover paired TCRαβ sequences from CD8+ T cells specific for the immunodominant epitope HLA-A02:01/LLWNGPMAV ( see Methods; data provided in S1 Dataset ) . Out of 256 cells , we observed 169 unique CDR3α , with 15 ( 8 . 9% ) of them shared between two or more clones ( Fig 1A ) . We examined the numbers of nucleotide insertions at the V-J junction of the CDR3α and indeed saw significantly fewer in CDR3α sequences that were shared between two or more clones ( mean 2 . 04 insertions , n = 23 ) than in sequences that were unique to a single clone ( mean 3 . 62 insertions , n = 154; p < 0 . 005 , Wilcoxon rank sum test; Fig 1B ) . In summary , it appears that convergent TCRα recombination may derive at least in part from the reduced junctional diversity of clones possessing CDR3 regions that are closer to germline . Motivated by this promiscuity of TCRα and TCRβ pairings , we developed a semi-heuristic procedure alphabetr ( ALgorithm for Pairing alpHA and BEta T cell Receptors ) that recovers TCRαβ pairs from high-throughput sequencing data . Fig 2 shows the algorithm schematically . The experimental procedure is to sequence the CDR3α and CDR3β regions from multiple samples of T cells from the same parent population ( Fig 2A–2C ) . The input to the algorithm is a list of these unpaired sequences ( Fig 2C ) , each associated with the sample it belonged to ( e . g . a given well in one or more 96-well plates ) . Fig 2C illustrates amino acid sequences as inputs , but the algorithm can be applied equally well to data comprising nucleotide sequences and/or the addition of V ( D ) J segment information . The number of cells in each well can be freely varied , and indeed as we describe below , varying the sample size across the plate ( s ) helps to increase both the number and accuracy of pairings . Given this information , alphabetr then calculates association scores between every α and every β chain found in a randomly chosen subsample of wells . This score is the sum of the number of co-occurrences of chains in each well , each weighted inversely by the total number of chains recovered from that well ( Fig 2D ( ii ) ) . The weighting factor reflects the intuitive idea that our confidence that a co-occurring α and β pair derive from the same clone decreases as the number of unique chains recovered from that well increases . The algorithm then solves a linear sum assignment problem within each well based on these plate-wide association scores to generate a list of candidate pairs of α and β sequences within each well ( Fig 2D ( iii ) ) . This is a list of αβ pairs in which each α is paired with only one β , and vice versa , such that the sum of the association scores is maximised . After repeating this assignment for every well in the subset , we generate a matrix of dimensions n × m where n and m are the total numbers of unique α and β chains recovered across the plate ( s ) , respectively , and whose entries are the number of times that each candidate pair αi βj ( i ∈ {1…n} , j ∈ {1…m} ) have been associated . Sharing of chains across clones is now possible in this list . Those αβ pairs that appear in a number of wells greater than the mean of the non-zero elements of this matrix are retained as a refined list of candidate pairs . The pairing and filtering process is repeated on subsets of the data ( Fig 2D ) , and a consensus list of putative paired CDR3 sequences comprises those appearing in more than a threshold proportion of these lists ( Fig 2E ) . This pseudo-jacknife procedure acts to reduce the effect of very common clones pushing up the threshold for inclusion in the filtered list and increases the efficiency of pairing of rarer clones , while minimising the inclusion of incorrect αβ pairs . Steps A-D are described in more detail in Methods . The algorithm then uses a maximum likelihood approach to estimate the relative frequencies of the clones associated with each candidate αβ pair ( Fig 2F; Methods ) . These estimated frequencies are then used with the patterns of co-occurrences of chains to distinguish between β-sharing and dual TCRα clones ( see Methods ) . This step also yields refined estimates of the frequencies of dual TCRα clones . The output of the algorithm is a list of single or dual TCRα clones together with estimates of their abundances within the parent population ( Fig 2G ) . To test the performance of alphabetr , we first used artificially generated datasets mimicking the bulk sequencing of CDR3α and CDR3β regions from polyclonal T cell populations . We assumed skewed distributions of clone sizes , with between 5 and 50 clones comprising the most abundant 50% of the population and the remainder , approximately 2000 clones , forming a flat tail at low frequency ( see Methods ) . These distributions were chosen to reflect plausible immunodominance hierarchies within T cell responses , motivated by analysis of epitope-specific cells recovered from human subjects immunised with live attenuated yellow fever virus vaccine ( our analysis and ref . [11] ) . We also analysed different sizes of parent populations ( see S1 Text , Section 2 ) . Within these hierarchies we allowed the virtual clones to exhibit sharing of CDR3α and CDR3β at ranges of frequencies consistent with published single-cell TCR sequencing studies ( Table 1 ) and our own data ( Fig 1A ) . We also allowed between 10% and 30% of clones to express two productive TCRα chains and 6% of clones to express two productive TCRβ chains . The sequences in each ‘well’ were then generated by sampling between 10 and 300 T cells from the parent population with replacement . Selecting an optimal pattern of sampling is an issue we return to below . To assess the robustness of alphabetr , we simulated the properties of two forms of sequencing error: dropping of chains and productive in-frame sequencing errors . Dropping of chains represents the failure of CDR3α and/or CDR3β regions to amplify or be detected , a process which likely has both purely random and clone-specific elements [22] . To model this , each clone was assigned a drop rate at random from a lognormal distribution with mean 0 . 15 and standard deviation of 0 . 01 , with the rate capped at 0 . 9 . Each instance of a CDR3α and CDR3β from that clone was then removed from the well with probability equal to the drop rate . To model productive in-frame sequencing errors , every unique CDR3α and CDR3β was assigned an error rate randomly drawn from a lognormal distribution with mean 0 . 02 and standard deviation 0 . 005 . Each instance of a sequence at the per-cell level was replaced at random by one of three erroneous ‘daughter’ sequences , unique and specific to the parent sequence , with probability equal to the sequence-specific in-frame error rate . Thus on average each CDR3α and CDR3β generated mutant offspring sequences at the rate of 2% per instance in each cell in the plate ( s ) . We then assigned identifiers to the remaining CDR3α and CDR3β sequences , associating them with the sample’s location in a virtual 96-well plate . The input to the algorithm is the list of these unpaired CDR3α and CDR3β sequences together with their well-identifiers . This process was repeated for different sampling strategies ( varying the sample sizes within each well , and using one or five 96-well plates ) ; different clonal size distributions; and different degrees of CDR3α and CDR3β sharing . Under these ranges of conditions , the algorithm was tested for the following: alphabetr does not attempt to identify dual TCRβ expressing cells because dealing with this relatively infrequent phenomenon together with dual TCRα chains and sharing of both TCRα and TCRβ chains across clones is extremely challenging algorithmically . However , we include dual TCRβ cells in our simulated data at the level of 6% to establish their impact on the algorithm’s performance .
Applying high throughput single-cell sequencing technologies to very large numbers of T cells is becoming increasingly within reach , but smaller-scale solutions using frequency-based sampling potentially remain far more economical . While another implementation of this strategy exists [22] , the promiscuous nature of TCRα and TCRβ usage within epitope-specific populations presents multiple challenges to frequency-based methods that have not been addressed to date , to our knowledge . The combination of alphabetr and relatively low-cost sequencing strategies addresses these issues , being capable of handling a wide range of clonal structures—skewed abundances , dual TCRα , sharing of both TCRα and TCRβ between clones—as well as providing estimates of clonal abundances . The algorithm is available as a documented package in R [44] from http://github . com/edwardslee/alphabetr . Single-cell technologies clearly allow the identification of large clonal expansions within populations . Our algorithm offers the potential to both identify these common clones as well as achieve depths of coverage of rarer clones that far exceed those currently possible with reasonable levels of single-cell sequencing . Given the correlation between diversity of immune responses and protection , this characterisation of the full diversity of T cell responses may be a better prognostic indicator than simply identifying common clones . Further , establishing the levels of TCRα- and TCRβ-sharing within populations sheds light on mechanisms of antigen recognition , repertoire diversity , and the efficiency of recruitment into immune responses . Our analysis demonstrates that the most difficult of these challenges is to reliably distinguish between abundant TCRβ-sharing or dual TCRα clones within highly skewed populations because the expected patterns of co-occurrences of the three chains under the two alternatives are very similar when sequencing samples of a few tens of cells per well; all three chains typically appear in nearly all the wells . The difference in patterns can be magnified to an extent by sampling very few numbers of cells per well , but this solution comes with the cost of a reduction in total sample size , sacrificing depth of recovery of rarer clones . One might suppose that the high prevalence of dual TCRα clones in the naive T cell pool favours that scenario over TCRβ-sharing . However , our immunological intuition here may be misleading . Naive T cell precursor numbers may be in the range 10–1000 cells in mice [45–47] , which we estimate is comparable to or larger than the size of TCRβ-sharing populations exported from the thymus . If the sharing of a TCRβ between clones confers overlap in their TCR specificities , and if recruitment into immune responses is efficient , we might expect to see significant levels of TCRβ-sharing within expanded , epitope-specific populations . Indeed , as shown in Table 1 , TCRβ-sharing has been seen to reach levels of up to 25% in responses to influenza epitopes in naive mice [13 , 14] and almost 40% in secondary responses [14] . It also occurred at a level of 2% in our analysis of TCRα and TCRβ usage among CD8+ cells specific for a YFV epitope in a human volunteer . The TCRβ-sharing/dual TCRα ambiguity is therefore a robust feature of epitope-specific responses , and is challenging to unravel fully with statistical approaches . There are at least three ways to address this problem . One solution is to pair alphabetr with , for example , one plate of single-cell samples . Since the ambiguity is only manifest strongly with common clones , this limited amount of extra information may serve to resolve the issue . A second approach is to exploit the fact that 30%-40% of clones will yield both an in-frame and an out-of-frame CDR3α sequence [13] . Currently , out-of-frame sequences are not utilised by alphabetr; one could extend it to include them and associate clones with their out-of-frame sequences . Clones possessing one in-frame and one out-of-frame CDR3α could then be excluded from the list of dual TCRα candidates , which would assist β-sharing/dual TCRα discrimination . A third possibility is to extend the algorithm to exploit the sequence information itself . If dealing with epitope-specific populations , we might expect more sequence similarity in the CDR3α in two β-sharing clones than in a dual TCRα case . In the latter , the two CDR3α sequences are likely unrelated because presumably only one of the TCRα chains is involved in antigen recognition and they rearrange independently . In practice , one needs a strategy for implementing alphabetr on a given sample of T cells with no a priori knowledge of the number or size distribution of clones . Assuming the number of cells is not limiting , we advocate a high-mixed sampling approach that involves sampling 20–300 cells per well and deals efficiently with a wide range of clonal abundances . When alphabetr is implemented as described here , a standard desktop computer with 16 Gb of RAM is able to handle samples from parent distributions of up to 4000 clones . When sampling populations with much fewer clones , lower numbers of cells/well are needed to avoid high false pairing rates . Assuming cell numbers are not limiting , bulk sequencing of the β chain could be used to gain a rough estimate of the richness of the parent distribution and so indicate when a sparse sampling strategy would be beneficial . In situations where cell numbers are limiting , one approach could be to begin with a single plate of 10 cells/well to obtain a rough lower bound on the richness of the distribution and apply a low or high mixed sampling strategy with the remaining cells from the sample , as appropriate . The single plate of 10 cells/well is then still usable for the pairing process and for frequency estimation . While we have framed our analysis around the sequencing of epitope-specific populations , alphabetr can equally well be applied more generally to T cell populations of restricted and potentially skewed polyclonality , such as tumour infiltrating lymphocytes or T cells extracted from sites of autoimmune responses . It therefore has immediate applications in cancer immunotherapy and other personalised immunomodulatory treatments . Until single-cell sequencing becomes more affordable , frequency-based pairing methods provide a rapid and economical means of characterising the clonal structure of T cell populations .
All experimental procedures were approved by the Regional Ethical Review Board in Stockholm , Sweden: 2008/1881-31/4 , 2013/216-32 , and 2014/1890-32 . Our approach exploits the fact that TCRα and TCRβ sequences ( referred to as α and β chains ) will tend to appear together in wells . Let Nα be the total number of unique α chains , Nβ be the total number of unique β chains , and the α and β chains found in the data set be labelled from 1 to Nα and from 1 to Nβ respectively . The degree of association between chains αi and βj is measured by a score Sij , S i j = ∑ k = 1 W δ i j k c α k + δ i j k c β k , ( 1 ) where the wells in the data are labelled from 1 , 2 , … , W , the numbers of distinct α and β chains in well k are c α k and c β k respectively , and δ i j k is 1 if both αi and βj are found in well k and 0 otherwise . Eq 1 sums the co-appearances in wells , each weighted inversely by the total number of α and β chains recovered from the well . The scaling accounts for the fact that the larger the number of unique chains in a well , the lower our confidence that a co-occurring α and β pair derive from the same clone . The algorithm begins by sampling a proportion pJ of the wells in the data without replacement . For all analyses presented here , we used pJ = 0 . 75 , which provided a good balance between depth and false pairing rate . The algorithm computes the association scores between every unique α and β chain using Eq 1 based on the sampled subset of wells . Let A k denote the set of A distinct α chains found in well k , that is A k = { α m 1 k , α m 2 k , … , α m A k } , where the m i k ∈ { 1 , … , N α } are integers that denote the labels of the A TCRα chains found in well k . Similarly , let B k denote the set of B distinct β chains found in well k , that is B k = { β n 1 k , β n 2 k , … , β n B k } , where the n i k ∈ { 1 , … , N β } subscripts denote the labels of the B TCRβ chains found in well k . The algorithm solves the following linear assignment problem using the Hungarian algorithm [39]: maximize∑αi∈Ak∑βj∈BkSijxijsubject to∑αi∈Akxij=1 for βj∈Bk∑βj∈Bkxij=1 for αi∈Akxij≥0 , αi∈Bk , βj∈Ak , ( 2 ) where xij = 1 indicates that αi and βj are assigned as a candidate TCR pair and xij = 0 otherwise . A pair αiβj is defined as an assigned pair of well k if xij = 1 for Eq 2 associated with well k . The number of assignments made for every pair of α and β is recorded as Xij , i . e . Xij equals the number of times xij = 1 from the solutions of Eq 2 for each well in the subset . We then calculate a filter level F that determines the minimum number of assignments required for an assigned candidate pair of α and β chains to be determined as a true TCR pair . The filter-level F is chosen to be the mean of the elements of the set {N ( i , j ) : N ( i , j ) > 0 , i ∈ 1 , 2 , … , Nα , j ∈ 1 , 2 , … , Nβ} , where N ( i , j ) is the number of times αi βj are assigned to each other , The output of this algorithm is then a list of candidate αβ pairs that may be associated with T cell clone . At this stage , dual TCRα cells are not identified; thus a clone α1α2β may be represented in this list as one or both of α1β and α2β . The procedure above is performed Nr times on random subsets of the wells ( all simulations in this paper use Nr = 100 ) , and each replicate yields a list of candidate αβ pairs . We then perform a filtering or consensus step in which only αβ pairings that appear in more than a threshold proportion T of these lists are retained as candidates . The simulations we present in the text explore thresholds of T = 0 . 3 , 0 . 6 , and 0 . 9 . We use maximum likelihood to infer clonal frequencies based on the number of wells in which a pair of α and β chains both appear . Let N = {n1 , n2 , … , ns} be the set of s distinct sample sizes ( ni cells per well ) in all of the wells and W = {w1 , w2 , … , ws} where wi represents the number of wells with samples of size ni cells . Let cij denote the clone with chains αi and βj and let k i j l denote the number of wells of sample size nl cells per well that contain chains αi and βj . The likelihood of the observations k i j ( . ) , given that the clone cij is present at frequency fij within the population , is L ( observed incidence of clone c i j | f i j ) = ∏ l = 1 s w l k i j l 1 - q l k i j l q l w l - k i j l ( 3 ) where ql is the probability of clone cij not being found in well l and is given by q l = 1 - f i j n l + ∑ m = 1 n l 2 ϵ m - ϵ 2 m n l m f i j m 1 - f i j n l - m . ( 4 ) Here ϵ is the average probability that a CDR3 sequence in a cell fails to be amplified and sequenced . For every clone cij , the algorithm maximises Eq 3 to estimate its frequency fij , and 95% confidence intervals are defined by the frequencies yielding log L = log L max - 1 . 96 . Details of the derivation of Eqs 3 and 4 are given in Section 4 of S1 Text . This procedure is applied to every αβ pair identified in the first phase of the algorithm . These estimated frequencies are used to distinguish TCRβ-sharing clone pairs from single TCR clones expressing two TCRα . This procedure is described in the following section . When a clone with two TCRα is identified , we revise the frequency estimate as follows . Let c ( ij ) t denote a clone with chains αi , αj , and βt , and k ( i j ) t l denote the number of wells of size nl that contain chains αi , αj , and βt . The likelihood of the observations given that clone c ( ij ) t has a frequency f ( ij ) t ∈ ( 0 , 1] is L ( observed incidence of clone c ( i j ) t | f ( i j ) t ) = ∏ l = 1 s w l k ( i j ) t l 1 - q l k ( i j ) t l q l w l - k ( i j ) t l ( 5 ) where ql is the probability of clone c ( ij ) t not being found in well l and is given by q l = 1 - f ( i j ) t n l + ∑ m = 1 n l 3 ϵ m - 3 ϵ 2 m + ϵ 3 m n l m f ( i j ) t m 1 - f ( i j ) t n l - m ( 6 ) where ϵ is the mean drop rate as described above . Eq 5 is then maximised to estimate f ( ij ) t , and again log L = log L max - 1 . 96 is used to calculate 95% confidence intervals . If the algorithm yields two clones that appear to share a TCRβ ( α1β and α2β ) , we must decide whether this is indeed a β-sharing pair of clones or that the association derives from one dual TCRα clone ( α1α2β ) . To do this , we use the likelihoods of observed co-occurrences of the three chains to assess the relative support for the two alternatives . Let cij = ( αi , βj ) and ckj = ( αk , βj ) be two putative clones with a common TCRβ chain βj . We count the number of wells containing all three-way , two-way , and single appearances of the three chains . We then calculate the ‘full’ likelihoods of this pattern of occurrences under two hypotheses: ( A ) that cij and ckj are indeed two β-sharing clones , with frequencies fij and fkj estimated using Eq 3; and ( B ) that the chains derive from one dual TCRα clone c ( ij ) k present at frequency f ( ij ) k , estimated using Eq 5 . If the difference log L B - log L A ≥ 10 , we assume the three chains derive from dual TCRα clone . The calculation of these full likelihoods is in Section 6 of S1 Text but is computationally tractable only for wells with less than 50 cells due to the need to calculate large multinomial coefficients . The full-likelihood method is therefore only appropriate for estimating frequencies of those relatively abundant clones that are commonly found in the wells with smaller sample sizes . We use a more restricted likelihood-based approach for discriminating β-sharing and dual TCRα among rare clones , which tend to appear only in larger samples . Let clones cij = ( αi , βj ) and ckj = ( αk , βj ) be two clones with a common beta chain βj , and let fij and fkj be their estimated frequencies . The algorithm calculates the ratio r i k j of the observed to the expected number of wells in which all three chains from the putative β-sharing pair cij and ckj co-appear , under the hypothesis that they are indeed two clones and not a dual TCRα: R = r i k j = A c i j , c k j E c i j , c k j : i ≠ k , j ∈ 1 , 2 , … , N β ( 7 ) where A ( cij , ckj ) is the number of times clones cij and ckj are observed to appear in the same well and Nβ is the number of distinct β chains , and the expected number is E c i j , c k j = ∑ l = 1 s w l 1 - 1 - f i j n l - 1 - f k j n l + 1 - f i j - f k j n l ( 8 ) ( see S1 Text , Section 5 for a derivation and discussion of this equation ) . We then partition the set of ratios R into two groups C1 and C2 using k-means clustering , where the mean of ratios of C1 is greater than the mean of the ratios of C2 ( see S1 Text , Fig G for an example ) . The clones associated with the ratios in C1 are chosen as dual TCR clones , such that if r i k j ∈ C 1 , then clones cij and ckj are removed from the list of TCR pairs and replaced with a dual TCRα clone αi αk βj . We created synthetic data sets reflecting the properties of antigen-specific T cell populations and sequencing errors . The data sets were sampled from a population of T cell clones where a significant proportion of α and β chains are shared and 10%-30% of clones have dual TCRα chains ( e . g . three clones can have the following chains: αi βk , αj βk , and αj αh βl ) . The sharing of β chains was set such that 85 . 9% of β chains were uniquely from one clone , 7 . 6% shared by two clones , 3 . 7% shared by three clones , 1 . 9% by four clones , and 0 . 9% by five clones . The sharing of α chains was set such that 81 . 6% of α chains were uniquely from one clone , 8 . 5% shared by two clones , 2 . 1% shared by three clones , 0 . 7% shared by four clones , 3 . 3% shared by five clones , 0 . 5% shared by six clones , and 3 . 3% shared by seven clones . We determined these levels of sharing by averaging those from the published single-cell data shown in Table 1 . The frequencies of the N clones were drawn from a skewed distribution in which ns clones comprise a proportion ps of the population and the other N − ns clones evenly represent 1 − ps of the population . The clone ranked ith in abundance then has frequency fi where f i = f 1 + r i - 1 if i = 1 , 2 , … , n s p s / ( N - n s ) if i = n s + 1 , n s + 2 , … , N ( 9 ) where the frequency of the largest clone f1 and the step size r are determined by solving the equations ∑ i = 1 n s f i = p s , f n s = 1 . 1 × p s N - n s . ( 10 ) The frequency of the smallest clone in the top 50% , fns , is set to be 10% higher than the frequency of the clones in the tail . All simulations were based on ps = 0 . 5 . We varied the number of top clones ns between 5 to 50 to test how skewness in the antigen-specific T cell population impacts the performance of the algorithm . In order to make the simulated data more realistic , experimental noise was included in the forms of ‘dropped’ chain errors and in-frame sequencing errors . Dropped chains are CDR3 sequences that fail to be sequenced due to PCR errors and/or sorting problems , and studies utilising both single-cell and many-cell techniques have reported average drop rates of 8% to 10% [17 , 22] . In the simulations , each clone was assigned a drop rate from a lognormal distribution with a mean of 0 . 15 and standard deviation of 0 . 01 , and every TCRα and TCRβ chain belonging to that clone was assigned that drop rate . In-frame errors cause a CDR3 sequence to be falsely identified with an incorrect productive nucleotide and/or amino acid sequence . In the simulations , each distinct sequence was assigned an in-frame error rate drawn from a lognormal distribution with a mean of 0 . 02 and a standard deviation of 0 . 005 . The error model was simulated as follows: when a cell is sampled into a virtual well , each of its chains fails to be sequenced with probability equal to the pre-assigned , clone-specific drop rate . Every surviving chain produces one of three randomly chosen , distinct , and chain-specific false sequences with probability equal to that chain’s pre-assigned in-frame error rate . A human volunteer was identified as HLA-A2+/HLA-B7+ and received the live attenuated yellow fever vaccine ( YFV-17D ) . On day 15 post-vaccination , peripheral blood samples were taken , and live CD3+CD8+ T cells were isolated by negative selection using magnetic columns ( Miltenyi Biotec , CD8+ T cell negative isolation kit ) . Cells were labeled with a panel of antibodies and the HLA-A02:01/LLWNGPMAV dextramer representing the immunodominant response . Single dextramer-specific CD3+CD8+ T cells were sorted into individual wells in 96 well plates containing a lysis buffer ( 0 . 4% Triton , RNAse inhibitor , dNTP , OligodT ) and immediately stored on dry ice . Single cell transcriptome libraries were subsequently generated from these cells using an adapted version of the SMRT-Seq2 protocol [48] . Libraries were prepared for sequencing by tagmentation and labelling individual single cell transcriptomes with a custom Tn5 enzyme [49] and Nextera XT dual indexes . Pooled libraries were then sequenced using an Illumina Hiseq2500 on high output mode ( 2 × 100bp or 2 × 125bp reads ) , and individual TCRα and TCRβ chains were identified using the MiTCR algorithm with default parameters . The default settings for MiTCR were used to align the CDR3 sequences . These were then manually filtered to remove erroneous sequences ( e . g . early stop codons and CDR3 sequences that were greater than 30 amino aids in length ) , and then BLAST was used on the remaining sequences to check for mapping to other parts of the genome , removing as appropriate . All clones used in the comparative analysis of CDR3α lengths were curated manually to exclude the possibility of contaminating TCR sequences . CDR3 amino acid sequences are provided as a CSV file in S1 Dataset , and the raw reads are deposited in the Gene Expression Omnibus ( GEO ) , GSE75659; Sequence Read Archive ( SRA ) , SRP066963 . | Our repertoires of T cell receptors ( TCR ) give our immune system the ability to recognise a huge diversity of foreign and self antigens , and identifying the TCRs involved in infectious disease , cancer , and autoimmune disease is important for designing vaccines and immunotherapies . The majority of T cells express a TCR made up of two chains , the TCRα and TCRβ , and high-throughput sequencing of samples of T cells results in the loss of this pairing information . One can identify TCRαβ clones using single-cell sequencing , but this is costly and typically probes only part of the diversity of T cell populations . Statistical approaches are potentially more powerful by sequencing the TCRα and TCRβ in multiple samples of T cells and pairing them using their frequency of co-occurrence . However , T cells involved in immune responses frequently share TCRα and TCRβ chains with other responding cells . This promiscuity , combined with a high prevalence of T cells with two TCRα chains and sequencing errors , presents significant challenges to frequency-based pairing methods . Here we present a new algorithm that addresses these challenges and also provides accurate estimates of the abundances of T cell clonotypes , allowing us to build a more complete picture of T cell responses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"blood",
"cells",
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"applied",
"mathematics",
"immunology",
"cloning",
"simulation",
"and",
"modeling",
"algorithms",
"mathematics",
"molecular",
"biology",
"techniques",
"cytotoxic",
"t",
"cells",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"white",
"blood",
"cells",
"animal",
"cells",
"proteins",
"antigens",
"t",
"cells",
"molecular",
"biology",
"immune",
"response",
"biochemistry",
"signal",
"transduction",
"t",
"cell",
"receptors",
"cell",
"biology",
"high",
"throughput",
"sequencing",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"immune",
"receptors",
"physical",
"sciences"
] | 2017 | Identifying T Cell Receptors from High-Throughput Sequencing: Dealing with Promiscuity in TCRα and TCRβ Pairing |
The apple is the most common and culturally important fruit crop of temperate areas . The elucidation of its origin and domestication history is therefore of great interest . The wild Central Asian species Malus sieversii has previously been identified as the main contributor to the genome of the cultivated apple ( Malus domestica ) , on the basis of morphological , molecular , and historical evidence . The possible contribution of other wild species present along the Silk Route running from Asia to Western Europe remains a matter of debate , particularly with respect to the contribution of the European wild apple . We used microsatellite markers and an unprecedented large sampling of five Malus species throughout Eurasia ( 839 accessions from China to Spain ) to show that multiple species have contributed to the genetic makeup of domesticated apples . The wild European crabapple M . sylvestris , in particular , was a major secondary contributor . Bidirectional gene flow between the domesticated apple and the European crabapple resulted in the current M . domestica being genetically more closely related to this species than to its Central Asian progenitor , M . sieversii . We found no evidence of a domestication bottleneck or clonal population structure in apples , despite the use of vegetative propagation by grafting . We show that the evolution of domesticated apples occurred over a long time period and involved more than one wild species . Our results support the view that self-incompatibility , a long lifespan , and cultural practices such as selection from open-pollinated seeds have facilitated introgression from wild relatives and the maintenance of genetic variation during domestication . This combination of processes may account for the diversification of several long-lived perennial crops , yielding domestication patterns different from those observed for annual species .
Domestication is a process of increasing codependence between plants and animals on the one hand , and human societies on the other [1] , [2] . The key questions relating to the evolutionary processes underlying domestication concern the identity and geographic origin of the wild progenitors of domesticated species [3] , the nature of the genetic changes underlying domestication [4] , [5] , the tempo and mode of domestication ( e . g . , rapid transition versus protracted domestication ) [6] and the consequences of domestication for the genetic diversity of the domesticated species [7] , [8] , [9] , [10] . An understanding of the domestication process provides insight into the general mechanisms of adaptation and the history of human civilization , but can also guide modern breeding programs aiming to improve crops or livestock species further [11] , [12] . Plant domestication has mostly been studied in seed-propagated annual crops , in which strong domestication bottlenecks have often been inferred , especially in selfing annuals , such as foxtail millet , wheat and barley [11] , [13] , [14] , [15] , [16] , [17] . Genetic data have suggested that domestication or the spread of domesticated traits has been fairly rapid in some annual species ( e . g , maize or sunflower ) , with limited numbers of populations or species contributing to current diversity [10] , [18] , [19] , [20] , [21] , [22] . In contrast , a combination of genetics and archaeology suggested a protracted model of domestication for other annual crops , and in particular for the origin of wheat or barley in the Fertile Crescent [11] , [23] . However , the genetic consequences of domestication have been little investigated in long-lived perennials , such as fruit trees [24] , [25] , [26] . Trees have several biological features that make them fascinating and original models for investigating domestication: they are outcrossers with a long lifespan and a long juvenile phase , and tree populations are often large and connected by high levels of gene flow [27] , [28] . Differences in life-history traits probably result in marked differences in the mode and speed of evolution between trees and seed-propagated selfing annuals [27] , [28] , [29] . For example , outcrossing may tend to make domestication more difficult , in part because the probability of fixing selected alleles is lower than in selfing crops [6] , [13] . The combination of self-incompatibility and a long juvenile phase also results in highly variable progenies , making breeding a slow and expensive process , and rendering crop improvement difficult . The development of vegetative propagation based on cuttings or grafting has been a key element in the domestication of long-lived perennials , allowing the maintenance and spread of superior individuals despite self-incompatibility [30] . However , the use of such techniques has further decreased the number of sexual cycles in tree crops since the initial domestication event , adding to the effect of long juvenile phases in limiting the genetic divergence between cultivated trees and their wild progenitors [30] , [31] , [32] , [33] . Thus , domestication can generally be considered more recent , at least in terms of the number of generations , in fruit tree crops than in seed-propagated selfing annuals . Given the slow process of selection and the limited number of generations in which humans could exert selection , the protracted nature of the domestication process in trees has probably resulted in limited bottlenecks [25] , [31] and in a weaker domestication syndrome [34] than in seed-propagated annuals . Nevertheless , many cultivated fruit trees clearly display morphological , phenotypic and physiological features typical of a domestication syndrome , such as large fruits and high sugar or oil content [32] , [35] . Many aspects of fruit tree domestication have been little studied [25] . Consequently , most of the hypotheses concerning the consequences of particular features of trees for their domestication/diversification remain to be tested . Recent studies on grapevines , almond and olive trees have provided illuminating insights , such as the importance of outcrossing and interspecific hybridization [36] , [37] , [38] , but additional studies of other species are required to draw more general conclusions . Here , we investigated the origins of the domesticated apple Malus domestica Borkh . , one of the most emblematic and widespread fruit crops in temperate regions [35] . A form of apple corresponding to extant domestic apples appeared in the Near East around 4 , 000 years ago [39] , at a time corresponding to the first recorded uses of grafting . The domesticated apple was then introduced into Europe and North Africa by the Greeks and Romans and subsequently spread worldwide [35] . While the ancestral progenitor has been clearly identified as being M . sieversii , the identity and relative contributions of other wild species present along the Silk route that have contributed to the genetic makeup of apple cultivars remain largely unknown . This is surprising given the potential importance of this knowledge for plant breeding and for our understanding of the process of domestication in fruit trees . The wild Central Asian species M . sieversii ( Ldb . ) M . Roem has been identified as the main contributor to the M . domestica genepool based on similarities in fruit and tree morphology , and genetic data [40] , [41] , [42] , [43] . The Tian Shan forests were identified as the geographic area in which the apple was first domesticated , on the basis of the considerable intraspecific morphological variability of wild apple populations in this region [44] , [45] . Nucleotide variation for 23 DNA fragments even suggested that M . sieversii and M . domestica belonged to a single genepool ( which would be called M . pumila Mill . ) , with phylogenetic networks showing an intermingling of individuals from the two taxa [43] . Some authors have also suggested possible contributions of additional wild species present along the Silk Route: M . baccata ( L . ) Borkh , which is native to Siberia , M . orientalis Uglitz . , a Caucasian species present along western sections of the ancient trade routes , and M . sylvestris Mill . ( European crabapple ) , a species native to Europe [46] , [47] , [48] , [49] . These hypotheses were based on the history of human migration and trade , the lack of phylogenetic resolution between M . domestica and these four wild species [41] , [42] , genetic evidence of hybridization at a local scale between domesticated apple and M . sylvestris [40] , and the recent finding of sequence haplotype sharing between M . sylvestris and M . domestica [50] . However , such secondary contributions remain a matter of debate , mostly due to the difficulty of distinguishing introgression from incomplete lineage sorting [43] , [50] , [51] . The three wild species occurring along the Silk Route all bear small , astringent , tart fruits . None of these species has the fruit quality of M . sieversii , but they may have contributed other valuable horticultural traits , such as later flowering , resistance to pests and diseases , capacity for longer storage or climate adaptation . The organoleptic properties of the fruits of these wild species may also have been selected during domestication , for the preparation of apple-based beverages , such as ciders [46] , [52] . Cider apples are indeed smaller , bitter and more astringent than dessert apples and bear some similarity to M . sylvestris apples . There is also evidence to suggest that Neolithic and Bronze Age Europeans were already making use of M . sylvestris [39] . In this study , we used a comprehensive set of apple accessions sampled across Eurasia ( 839 accessions from China to Spain; Figure 1 and Figure S1; Table S1 ) and 26 microsatellite markers distributed evenly across the genome to investigate the following questions: 1 ) Is there evidence for population subdivision within and between the five taxa M . domestica , M . baccata , M . orientalis , M . sieversii and M . sylvestris ? 2 ) How large is the contribution of wild species other than the main progenitor , M . sieversii , to the genome of M . domestica ? 3 ) Does M . domestica have a genetic structure associated with its different possible uses ( i . e . , differences between cider and dessert apples ) ? 4 ) What consequences have domestication , subsequent crop improvement and vegetative propagation by grafting had for genetic variation in cultivated apples ? Most of our samples of M . domestica corresponded to cultivars from Western Europe ( Figure 1 and Figure S1 ) , as almost all the cultivars available in modern collections ( including American , Australasian cultivars ) are of European ancestry and this region is therefore the most relevant area for the detection of possible secondary introgression from the European crabapple .
Our sampling scheme ( Figure 1 and Figure S1 ) , based on the collection of a single tree for each apple variety , was designed to avoid the sampling of clones . However , there may still be some clonality if some varieties differing by only a few mutations were propagated by grafting . We corrected for this potential clonality , using the clonal assignment procedures implemented in GENODIVE [53] . We found no pair of samples assigned to the same clonal lineage unless using a threshold of 22 pairwise differences between multilocus genotypes , indicating that our samples did not include any clonal genotypes ( the threshold corresponds to the maximum genetic distance allowed between genotypes deemed to belong to the same clonal lineage ) . Many apple cultivars , including modern cultivars in particular , share recent common ancestors , and siblings or clones of wild species can also be collected unintentionally in the field . Because these features could result in a spurious genetic structure due to the presence of closely related individuals in the dataset , we checked for the presence of groups of related individuals in our dataset between M . domestica cultivars and between the individuals of each wild species . The percentage of pairs with a pairwise relatedness ( rxy ) greater than 0 . 5 ( i . e . , full sibs ) was: 0 . 4% in M . domestica ( N = 168 pairs ) , 0 . 3% in M . sieversii ( N = 79 ) , 0 . 004% in M . orientalis ( N = 20 ) , and 0 . 7% in M . baccata ( N = 40 ) . For M . sylvestris , no individual pair with rxy>0 . 5 was identified . However , the distribution of pairwise relatedness rxy among M . domestica cultivars did not deviate significantly from a Gaussian distribution centred on 0 and with a low variance ( Fisher's exact test , P≈1 , standard deviation = 0 . 11 , Figure S2 ) . This suggests that closely related cultivars are unlikely to have biased subsequent analyses of population structure . We also checked for the limited effect of relatedness on our conclusions by performing all analyses of population subdivision on both the full dataset and a pruned dataset excluding related individuals ( see below ) . We tested the null hypothesis of random mating within each species by calculating FIS , which measures inbreeding . All five Malus species had relatively low values of FIS , although all were significantly different from zero ( Table 1 ) , suggesting that each species corresponded to an almost random mating unit . This is consistent with the self-incompatibility system of these species and indicates a lack of widespread groups of related individuals in M . domestica . Low FIS values at species level also indicate a lack of population structure within species . The higher values of FIS observed in M . baccata probably resulted from the occurrence of null alleles , as the microsatellite markers were developed in M . domestica , to which M . baccata is the most distantly related ( Table 2 ) . The lowest FIS value was that obtained for M . domestica , reflecting outcrossing between dissimilar parents in breeding programs , or that selection targeted higher levels of heterozygosity [54] . We used the ‘admixture model’ implemented in STRUCTURE 2 . 3 [55] to infer population structure and introgression . Analyses were run for population structure models assuming K = 1 to K = 8 distinct clusters ( Figure 2 ) . The ΔK statistic , designed to identify the most relevant number of clusters by determining the number of clusters beyond which there is no further increase in likelihood [56] , was greatest for K = 3 ( ΔK = 6249 , Pr|ln L = −78590 ) . However , the clusters identified at higher K values may also reveal a genuine and biologically relevant genetic structure , provided that they are well delimited [57] . The five Malus species were clearly assigned to different clusters for models assuming K≥6 clusters and for a minor clustering solution ( “mode” ) at K = 5 ( Figure 2 ) . The major mode ( i . e . , the clustering solution found in more than 60% of the simulation replicates ) observed at K = 5 grouped together M . sylvestris and M . domestica genotypes . Increasing the number of clusters above K = 6 identified no additional well-delimited clusters corresponding to a subdivision of a previous cluster . Instead , it simply introduced heterogeneity into membership coefficients , indicating that the clustering of the five Malus species into separate genepools was the most relevant clustering solution . We checked that the presence of related pairs of cultivars in our dataset did not bias clustering results , by repeating the analysis on a pruned dataset ( N = 489 ) excluding all related individuals in wild and cultivated species ( i . e . , excluding all pairs with rxy≥0 . 5 ) . Similar results were obtained , with the same five distinct clusters identified as for the full dataset . We estimated the genetic differentiation between the five Malus species by calculating pairwise FST ( Table 2 ) . All FST values were highly significant ( P<0 . 001 ) and seemed to indicate a West to East differentiation gradient of M . domestica with the wild species . The highest level of differentiation was that between M . baccata and the other Malus species , and the lowest level of differentiation was that between M . domestica and the westernmost species , M . sylvestris ( Table 2 ) . Malus domestica was markedly more differentiated from its main progenitor M . sieversii ( FST = 0 . 0639 ) than from the European M . sylvestris ( FST = 0 . 006 ) and it was only slightly less differentiated from the Caucasian M . orientalis ( FST = 0 . 049 ) . We first searched for footprints of a domestication bottleneck by comparing levels of microsatellite variation in M . domestica and wild species . There was no significant difference in genetic diversity ( as measured by expected heterozygosity , HE ) between M . domestica and M . baccata , M . orientalis or M . sieversii , but HE was significantly higher in M . sylvestris than in M . domestica ( Table 1 ) . Significant differences in allelic richness ( Ar ) were found between M . domestica and M . orientalis ( Wilcoxon signed rank test , P = 0 . 03 ) or M . sylvestris ( P<10−8 ) , but not between M . domestica and either M . baccata ( P = 0 . 9 ) or M . sieversii ( P = 0 . 9 ) ( Table 1 ) . We used the method implemented in the BOTTLENECK program [58] , comparing the expected heterozygosity estimated from allele frequencies with that estimated from the number of alleles and the sample size , which should be identical for a neutral locus in a population at mutation-drift equilibrium . Inferences about historical changes in population size are based on the prediction that the expected heterozygosity estimated from allele frequencies decreases faster than that estimated under a given mutation model at mutation-drift equilibrium in populations that have experienced a recent reduction in size . BOTTLENECK analysis showed no significant deviation from mutation-drift equilibrium in any of the five species , under either stepwise or two-phase models of microsatellite evolution ( one-tailed Wilcoxon signed rank test , P>0 . 95 ) . We therefore detected no signal of a demographic bottleneck associated with the domestication of apples . We used the admixture coefficients estimated by STRUCTURE to assess the recent contribution of the various wild species to the M . domestica genepool . STRUCTURE analyses of the full dataset showed some admixture among Malus species for the minor mode separating the five species at K = 5 . Admixture coefficients were higher between M . domestica and M . sylvestris ( α = 0 . 23 ) than between M . domestica and respectively M . sieversii ( α = 0 . 06 ) , M . orientalis ( α = 0 . 034 ) and M . baccata ( α = 0 . 032 ) . We further analysed the contribution of each wild species to the genome of M . domestica by running STRUCTURE separately on each pair of species including M . domestica ( Figure 3; Table 3 and Table S2 ) . Malus domestica genotypes with membership coefficients ≥0 . 20 in a wild species genepool were considered to display introgression . Using this somehow arbitrary cut-off value , STRUCTURE analyses revealed that 26% of M . domestica cultivars displayed introgression from the European crabapple , M . sylvestris ( Table 3 and Table S2 ) . By contrast , only 2% , 3% and 0 . 02% of the M . domestica genotypes displayed introgression from M . sieversii , M . orientalis and M . baccata , respectively ( Table 3 and Table S2 ) . The M . domestica cultivars displaying admixture with the M . sylvestris genepool were mostly Russian ( e . g . , “Antonovka” , “Antonovka kamenicka” , “Novosibirski Sweet” , “Yellow transparent” ) , French ( e . g . , “Blanche de St Anne” , “St Jean” , “Api” and “Michelin” ) and English ( e . g . , “Worcester Pearmain” and “Fiesta” ) . The M9 dwarf apple cultivar ( “Paradis jaune de Metz” , [59] ) commonly used as a rootstock also appeared to display introgression from the European crabapple ( proportion of ancestry in the M . domestica genepool: 0 . 28; Table S2 ) . When French cultivars were removed from the dataset ( N = 89 ) and pairwise STRUCTURE analyses were repeated for all species pairs including M . domestica , 18% of cultivars displayed introgression from M . sylvestris , including commercial cultivars such as Granny Smith , Michelin , Antonovka and Ajmi ( Figure S3 ) with a mean membership coefficient of M . sylvestris into M . domestica genepool of 47% . Malus sylvestris thus appears to have made a significant contribution to the M . domestica genepool through recent introgression , building on the more ancient contribution ( see below ) of the Asian wild species M . sieversii . We also note that a few M . domestica individuals appeared to display introgression from several wild species ( Table S2 ) , and that M . baccata ornamental cultivars , such as M . baccata flexilis , M . baccata Hansen's and M . baccata gracilis , were partially or even mostly assigned ( from 32% to >80% ) to the M . domestica genepool ( Table S3 ) . Previous studies [43] , [50] , [60] identified the Central Asian wild apple M . sieversii as the main progenitor of M . domestica on the basis of DNA sequences . Due to the large contribution by M . sylvestris detected in our dataset , corresponding mostly to Western European cultivars , M . domestica and M . sylvestris appeared to be the most closely related pair of species in our analyses of microsatellite markers . We investigated the more ancient contribution of M . sieversii to the M . domestica genepool , by reassessing the genetic differentiation between species in analyses restricted to “pure” individuals ( i . e . , assigned at ≥0 . 9 to their respective genepools ) from both wild and cultivated species . All FST values were highly significant ( P<0 . 001 ) , but the ranking of FST values between M . domestica and the various wild species was affected: the highest differentiation was still observed between M . domestica and M . baccata ( FST = 0 . 22 ) , but the lowest differentiation was observed between M . domestica and M . sieversii ( FST = 0 . 11 ) . Regarding the differentiation between M . sylvestris and M . domestica , we observed the opposite of what was found with the full dataset: M . sylvestris appeared to be more strongly differentiated ( FST = 0 . 14 ) from M . domestica than M . sieversii . Thus , by removing signals of recent introgression between cultivated and wild species we were able to confirm that M . sieversii was the initial progenitor of M . domestica . The finding of a significant level of introgression from wild species into cultivated apple suggested that gene flow might also have occurred in the opposite direction . STRUCTURE analyses of pairs of species confirmed this hypothesis ( Figure 3 ) , revealing possible introgression of genetic material into M . sylvestris , M . baccata , M . orientalis and M . sieversii from M . domestica ( mean proportions of ancestry in the M . domestica genepool of 0 . 12 , 0 . 10 , 0 . 03 and 0 . 23 , respectively; Table 3 ) . Considering genotypes with membership coefficients ≥0 . 9 in the M . domestica genepool as misclassified , we found a total of N = 31 misclassified wild Malus individuals . These results suggest gene flow from the domesticated apple genepool could significantly affect the genetic integrity of wild apple relatives , their future evolution and , possibly , their use as resources for crop improvement . Model-based Bayesian clustering algorithms , such as that implemented in STRUCTURE , have a high level of power only for the detection of recent introgression events [55] , [61] , [62] . We therefore investigated the contributions of M . sylvestris and M . orientalis to the M . domestica genepool using approximate Bayesian computation ( ABC ) methods that offer a more historical perspective on gene flow [63] . We used a demographic model implementing admixture events [64] . We compared several admixture models to infer what species pairs underwent introgression events and to estimate introgression rates [64] . Malus baccata was not included in these analyses because of its high level of divergence from M . domestica . We assumed , as suggested by previous studies , that M . domestica derived originally from M . sieversii . The most complex model simulated sequential admixtures between M . domestica and all wild species . Other models sequentially removed introgression with each wild species , the order being based on FST values and admixture rates inferred by STRUCTURE . The compared models were the following: ( i ) the model a assumed that M . domestica was derived from M . sieversii and that the ancestral M . domestica population was involved in reciprocal introgression events with M . orientalis and M . sylvestris , and subsequently introgressed back into M . sieversii ( Figure 4a ) , ( ii ) model b was similar to the model a , but without introgression events from M . domestica into wild species ( Figure 4b ) , ( iii ) the model c included a single introgression event , from M . sylvestris into M . domestica ( Figure 4c ) , and ( iv ) the model d simulated no admixture ( Figure 4d ) . The number of parameters estimated in the model was limited by fixing the times of admixture with M . orientalis , M . sylvestris and M . sieversii at 600 , 200 and 13 generations before the present , respectively . We used the following underlying hypotheses: ( i ) as the juvenile period of Malus lasts five to 10 years , we assumed a generation time of 7 . 5 years , ( ii ) admixture between ancestral M . domestica and M . orientalis in the Caucasus occurred approximately 4 , 500 years ago , shortly before the appearance of sweet apples in the Middle East ( 4 , 000 years ago ) , ( iii ) admixture between ancestral M . domestica and M . sylvestris in Europe occurred approximately 1 , 500 years ago , soon after the introduction of domesticated apples into Europe by the Greeks and Romans ( iv ) back-introgression into M . sieversii from M . domestica occurred approximately 100 years ago , when the cultivation of modern varieties reached Central Asia . The relative posterior probabilities computed for each model provided strongest statistical support for model c , which assumed a single introgression event , from M . sylvestris into M . domestica ( Table 4; posterior probability [p] = 0 . 67 , 95% confidence interval: 0 . 63–0 . 72 ) . Note that the model without admixture ( model d ) had the lowest relative posterior probability ( Table 4 ) . In analyses under alternative admixture models ( models a and b ) , the posterior distributions were flat for introgression between M . domestica and M . orientalis and highly skewed towards low values for introgression into M . sylvestris and M . sieversii ( not shown ) , which is consistent with statistical support being highest for model c . Given that the model c was clearly favoured , parameter estimates are shown below only for this model ( Table 5; prior distributions in Table S4 ) . The contribution of M . sylvestris to the M . domestica genepool was estimated at about 61% ( 95% credibility interval [95% CI]: 50–68% ) . We obtained estimates of effective population sizes of 3 , 520 ( 95% CI: 2 , 090–5 , 680 ) for M . domestica , 13 , 200 ( 95% CI: 6 , 920–19 , 300 ) for M . sieversii , 34 , 600 ( 95% CI: 15 , 100–48 , 000 ) for M . sylvestris , and 28 , 300 ( 95% CI: 11 , 700–64 , 000 ) for M . orientalis . Using a generation time of 7 . 5 years , the divergence between M . domestica and M . sieversii ( T3 ) was estimated to have occurred 17 , 700 years ago ( 95% CI: 6 , 225–25 , 200 ) , which is earlier than previously thought , but we note that the credibility interval is quite large . We estimated that M . sylvestris and M . sieversii diverged about 83 , 250 years ago ( T1 , 95% CI: 40 , 575–334 , 500 ) , with M . orientalis and M . sieversii diverging about 20 , 775 years ago ( T2 , 95% CI: 9 , 900–47 , 775 ) . The results above were obtained using the full dataset . We checked the validity of our inferences by conducting analyses on the dataset without admixed and misclassified individuals and using different times of admixture , by assessing the goodness-of-fit of models to data , and by checking that sufficient power was achieved to discriminate among competing models ( Text S1; Tables S5 , S6 , S7 ) . Overall , ABC analyses all provided clear support for a model with contribution of the European crabapple into the domesticates , although the estimated value of the actual contribution of M . sylvestris is probably overestimated here , and should therefore be treated with caution . Indeed , the simulation of a single introgression event hundreds of years ago most likely demanded higher rates of introgression to account for the actual genetic contribution of M . sylvestris into M . domestica than would be needed under continuous gene flow over a long period . As cider cultivars produce apples that are smaller , more bitter and astringent than dessert cultivars , we expected to observe genetic differentiation between these two groups of cultivars and a closer genetic proximity of cider cultivars to M . sylvestris [35] , [65] . Neither hypothesis was supported by our data . The classification of apples into “dessert” and “cider” varieties as prior information for STRUCTURE ( Locprior model ) revealed a very weak tendency of cider and dessert cultivars to be assigned to different clusters at K = 2 ( Figure 5 ) , but increasing K did not further result in clearer differentiation between the two types of cultivars . At K = 2 , M . domestica cider genotypes had a mean membership of 94 . 7% , and M . domestica dessert genotypes had a mean membership of 52 . 5% . However , STRUCTURE analyses without this prior information gave essentially the same clustering patterns at K = 2 ( G′ = 0 . 95 similarity to analyses using classification to assist clustering ) . The weak differentiation between cider and dessert cultivars ( FST = 0 . 02 ) and their high level of admixture in STRUCTURE analyses ( Figure 5 ) indicated a shallow subdivision of the M . domestica genepool . Analyses on a pruned dataset from which closely related individuals had been removed ( i . e . , pairs of genotypes with rxy≥0 . 5; N = 172 ) revealed the same pattern , confirming that the presence of related cultivars in the dataset did not bias clustering analyses . STRUCTURE was also run on a dataset including all M . sylvestris genotypes , to test the hypothesis that cider cultivars would display a higher level of introgression from the European crabapple . However , the opposite pattern was observed: the proportion of genotypes displaying introgression from M . sylvestris was actually significantly higher in dessert than in cider cultivars ( 36 . 4% and 15 . 5% respectively , χ2 = 16 . 9 , P = 4×10−5 ) . Finally , little genetic differentiation was observed between groups of cultivars of different geographic origins ( 95% CI: −0 . 8–0 . 6 , Table S8 ) .
Malus sieversii was previously identified as the main contributor to the M . domestica genome on the basis of morphological and sequence data [41] , [43] . The flanks of the Tian Shan mountains have been identified as a likely initial site of domestication , based on the high morphological variability of the wild apples growing in this region , and their similarity to sweet dessert apples [44] , [45] . We show here , using a set of rapidly evolving genetic markers distributed throughout the genome and a large sampling , that M . domestica now forms a distinct , random mating group , surprisingly well separated from M . sieversii , with no difference in levels of genetic variation between the domesticate and its wild progenitor . This contrasts with the pattern previously reported , based on a twenty three-gene phylogenetic network [43] , where domesticated varieties of apple appeared nested within M . sieversii . After the removal of individuals showing signs of recent admixture , M . sieversii and M . domestica nevertheless appeared to be the pair of species most closely related genetically , confirming their progenitor-descendant relationship . Apple breeding methods ( grafting and “chance seedling” selection ) , life-history traits specific to trees and/or the genetic architecture of selected traits have likely played a role in the conservation of levels of genetic diversity in cultivated apples similar to those in wild apples . Some factors , such as “chance seedling” selection [66] , may even have increased genetic diversity , by favouring outcrossing events among domesticates and introgression from wild species [39] . The low inbreeding coefficients inferred in domesticated apples and the low level of differentiation between cultivated and wild apple populations [40] , [54] , [67] , [68] indicate a high frequency of crosses between individuals of M . domestica , M . sieversii and other wild relatives hailing from diverse geographic origins . Such a high level of gene flow has likely contributed to maintenance of a high level of genetic diversity in domesticated apples . The grafting technique , which was probably developed around 3 , 000 years ago , has made it possible to propagate superior individuals clonally . The spread of grafting , together with the lengthy juvenile phase ( 5–10 years ) and the long lifespan of apples , may have imposed strong limits on the intensity of the domestication bottleneck thereby limiting the loss of genetic diversity [27] , [28] , [31] . By decreasing the number of generations since domestication , these factors have probably also helped to restrict the differentiation between domesticates and wild relatives . In theory , grafting may have limited the size of the apple germplasm dispersed early on to a few very popular genotypes , thereby provoking a sudden shrink in effective population size and a loss of diversity . However , we found no evidence that the clonal propagation of apples resulted in a long-lasting decrease in population size or clonal population structure . We can speculate that this may be due to a combination of various factors such as: gene flow with wild species , small-scale propagation ( many farmers producing a few grafts each ) , a large variation in preferences for taste and other quality characteristics between farmers and cultures , large differences in growth conditions leading to the adoption of different sets of genotypes in different regions or the typical behaviour of hobby breeders , who tend to spot particular differences and multiply them . Similarly , for grape , there are huge numbers of old varieties and as much genetic variation in cultivated varieties as in wild-relative progenitors [37] . There has been a long-running debate concerning the possible contribution of other wild species present along the Silk Route to the genetic makeup of M . domestica [40] , [46] , [47] , [65] , [69] . Our results clearly show that interspecific hybridization has been a potent force in the evolution of domesticated apple varieties . Apple thus provides a rare example of the evolution of a domesticated crop over a long period of time and involving at least two wild species ( see also the cases of olive tree and avocado [24] , [26] , [37] , [70] ) . A recent study argued that introgression from M . sylvestris into the M . domestica genepool was the most parsimonious explanations for shared gene sequence polymorphisms between the two species [50] . Using an unprecedentedly large dataset , more numerous and more rapidly evolving markers and a combination of inferential methods , we provide a comprehensive view of the history of domestication in apple . We confirm that M . sieversii was the initial progenitor and show that the wild European crabapple M . sylvestris has been a major secondary contributor to the diversity of apples , resulting in current varieties of M . domestica being more closely related to M . sylvestris than to their central Asian progenitor . This situation is reminiscent of that for maize , in which the cultivated crop Zea mays is genetically more closely related to current-day highland landraces than to lowland Z . mays ssp . parviglumis from which the crop was domesticated [71] . This pattern has been attributed to large-scale gene flow from a secondary source , a second subspecies of teosinte , Z . mays ssp . mexicana , into highland maize populations [71] . The usefulness of wild relatives for improving elite cultivated crop genepools has long been recognised and the exploitation of wild resources is now considered a strategic priority in breeding and conservation programs for most crops [11] , [12] , [44] . Domesticated apples are unusual in that the contribution of wild relatives probably occurred early and unintentionally in the domestication process , preceding even the use of controlled crosses . The use of genetic markers with lower mutation rates than our set of microsatellites might also make it possible to investigate the contribution of more phylogenetically distant apple species growing in areas away from the Silk Route to the diversification of modern apple cultivars . The Romans introduced sweet apples into Europe at a time at which the Europeans were undoubtedly already making cider from the tannin-rich fruits of the native M . sylvestris [35] , [72] . Cider is not typical of Asia [35] , but it was widespread in Europe by the time of Charlemagne ( 9th century , [73] ) . Large numbers of apple trees were planted for cider production in France and Spain from the 10th century onwards [48] , [52] . The very high degree of stringency of cider apples ( often to the extent that they are inedible ) led to the suggestion that cider cultivars arose from hybridization between M . sylvestris and sweet apples [35] , [46] , [65] . We show here that the genetic structure within the cultivated apple genepool is very weak , with poor differentiation between cider and dessert apples . Cider cultivars thus appear to be no more closely genetically related to M . sylvestris than dessert cultivars . As wild Asian apples are known to cover the full range of tastes [44] , [46] , it is possible that fruits with the specific characteristics required for cider production were in fact initially selected in Central Asia and subsequently brought into Europe . There is a long-standing tradition of cider production in some parts of Turkey [35] , for instance , which is potentially consistent with an Eastern origin of cider cultivars . However , the low level of genetic differentiation between dessert and cider apples indicates that , even if different types of apples were domesticated in Asia and brought to Europe , they have not diverged into independent genepools . This study settles a long-running debate by confirming that 1 ) M . domestica was initially domesticated from M . sieversii , and 2 ) M . domestica subsequently received a significant genetic contribution from M . sylvestris , much larger than previously suspected [35] , at least in Western Europe , where originated most of our samples and most cultivar diversity . The higher level of introgression of the European crabapple into the domesticated apple in this study than in previous studies [43] , [50] , [51] may be attributed to the use of a larger and more representative set of M . domestica genotypes coupled with the genotyping of numerous and rapidly evolving markers known to trace back more recent events . Our inferences also have important implications for breeding programs and for the conservation of wild species of apple . The major contribution of the various wild species to the M . domestica genepool highlights the need to invest efforts into the conservation of these species , which may contain unused genetic resources that could further improve the domesticated apple germplasm [74] , such as disease resistance genes or genes encoding specific organoleptic features .
Leaf material was retrieved from the collections of various institutes ( INRA Angers , France; USDA - ARS , Plant Genetic Resources Unit , Geneva , NY; ILVO Melle , Belgium ) and from a private apple germplasm repository in Brittany for M . domestica ( N = 368 , Figure S1 including only diploid cultivars N = 299 ) and from forests for the four wild species ( Figure 1; Table S1 ) . Malus sieversii ( N = 168 ) material was collected from 2007 to 2010 in the Chinese Xinjiang province ( N = 26 ) , Kyrgyzstan ( N = 5 ) , Uzbekistan ( N = 1 ) , Tajikistan ( N = 1 ) and Kazakhstan ( N = 114 ) . Malus orientalis ( N = 215 ) was sampled in 2009 in Armenia ( N = 203 ) , Turkey ( N = 5 ) and Russia ( N = 5 ) . Malus sylvestris ( N = 40 ) samples were obtained from 15 European countries . Malus baccata ( N = 48 ) was sampled in 2010 in Russia . The origins of M . domestica cultivars were: France ( N = 266 ) , Great Britain ( N = 12 ) , USA ( N = 12 ) , Russia ( N = 7 ) , the Netherlands ( N = 6 ) , Australia ( N = 4 ) , Belgium ( N = 4 ) , Germany ( N = 4 ) , Japan ( N = 3 ) , Ukraine ( N = 3 ) , Tunisia ( N = 2 ) , Switzerland ( N = 2 ) , Spain ( N = 2 ) , New Zealand ( N = 2 ) , Israel ( N = 1 ) , Ireland ( N = 1 ) , Canada ( N = 1 ) , Armenia ( N = 2 ) and unknown/debated ( N = 34 ) . Genomic DNA was extracted with the Nucleo Spin plant DNA extraction kit II ( Macherey & Nagel , Düren , Germany ) according to the manufacturer's instructions . Microsatellites were amplified by multiplex PCR , with the Multiplex PCR Kit ( QIAGEN , Inc . ) . We used 26 microsatellites spread across the 17 chromosomes ( one to three microsatellites per chromosome ) , in 10 different multiplexes previously optimised on a large set of genetically related progenies of M . domestica [75] . The four multiplexes ( MP01 , MP02 , MP03 , MP04; Table S9; Lasserre P . unpublished data ) were performed in a final reaction volume of 15 µl ( 7 . 5 µl of QIAGEN Multiplex Master Mix , 10–20 µM of each primer , with the forward primer labelled with a fluorescent dye and 10 ng of template DNA ) . We used a touch-down PCR program ( initial annealing temperature of 60°C , decreasing by 1°C per cycle down to 55°C ) . Six other multiplex reactions ( Hi6 , Hi4ab , Hi5-10 , Hi13a , Hi13b , Hi4b ) were performed using previously described protocols [75] . Genotyping was performed on an ABI PRISM X3730XL , with 2 µl of GS500LIZ size standard ( Applied Biosystems ) . Alleles were scored with GENEMAPPER 4 . 0 software ( Applied Biosystems ) . We retained only multilocus genotypes presenting less than 30% missing data . We checked the suitability of the markers for population genetic analyses . None of the 26 microsatellite markers deviated significantly from a neutral equilibrium model , as shown by the non significant P-values obtained in Ewen-Watterson tests [76] , and no pair of markers was found to be in significant linkage disequilibrium in any of the species [77] , [78] . The markers could therefore be considered unlinked and neutral . Apple cultivars may be polyploid [79] . We therefore first checked for the presence of polyploidy individuals of M . domestica within our dataset . Individuals presenting multiple peaks on electrophoregrams were first re-extracted to eliminate contamination as a possible source of apparent polyploidy . We then checked whether they had been reported to be polyploidy in previous studies [79] . After completion of this checking procedure , we removed 69 polyploids ( of the 368 samples ) from subsequent analyses . We tested for the occurrence of null alleles at each locus with MICROCHECKER 2 . 2 . 3 software [80] . Allelic richness and private allele frequencies were calculated with ADZE software [81] , for a sample size of 22 . Heterozygosity ( expected ( HE ) and observed ( HO ) ) , Weir & Cockerham F-statistics , deviation from Hardy-Weinberg equilibrium and genotypic linkage disequilibrium were estimated with GENEPOP 4 . 0 [77] , [78] . The significance of differences between FST values was assessed in exact tests carried out with GENEPOP 4 . 0 [77] , [78] . Individuals were assigned to clonal lineages with GENODIVE [53] . We estimated relatedness between pairs of cultivars and between pairs of individuals within each species , by calculating the rxy of Ritland and Lynch [82] with RE-RAT online software [83] . We tested whether the distributions of rxy deviated significantly from a Gaussian distribution with a mean of zero and a standard deviation equal to the observed standard deviation , by comparing observed and simulated distributions in Fisher's exact test ( R Development Core Team , URL http://www . R-project . org ) . We tested for the occurrence of a bottleneck during apple domestication with the method implemented in BOTTLENECK [58] , [84] . The tests were performed under the stepwise-mutation model ( SMM ) and under a two-phase model ( TPM ) allowing for 30% multistep changes . We used Wilcoxon signed rank tests to determine whether a population had a significant number of loci with excess genetic diversity . We used the individual-based Bayesian clustering method implemented in STRUCTURE 2 . 3 . 3 [55] , [85] , [86] to investigate species delimitation , intraspecific population structure and admixture . This method is based on Markov Chain Monte Carlo ( MCMC ) simulations and is used to infer the proportion of ancestry of genotypes in K distinct predefined clusters . The algorithm attempts to minimize deviations from Hardy–Weinberg and linkage equilibrium within clusters . Analyses were carried out without the use of prior information , except for analyses of population subdivision within the M . domestica genepool for which the “cider”/“dessert” classification of cultivars was used as prior information to assist clustering . K ranged from 1 to 8 for analyses of the five-species dataset and the M . domestica dataset , and was fixed at K = 2 for analyses of pairs of species including M . domestica and each of the wild species . Ten independent runs were carried out for each K and we used 500 , 000 MCMC iterations after a burn-in of 50 , 000 steps . We used CLUMPP v1 . 1 . 2 ( Greedy algorithm ) [87] to look for distinct modes among the 10 replicated runs of each K . STRUCTURE analyses were run for the full dataset ( N = 839 ) and for two pruned datasets excluding non-pure individuals ( i . e . , genotypes with <0 . 9 membership of their species' genepool ) and related individuals ( rxy≥0 . 5 ) . We used the DIYABC program [88] to compare different admixture models and infer historical parameters . We simulated microsatellite datasets for 14 loci ( Ch01h01 , Ch01h10 , Ch02c06 , Ch02d08 , Ch05f06 , Ch01f02 , Hi02c07 , Ch02c09 , Ch03d07 , Ch04c07 , Ch02b03b , MS06g03 , Ch04e03 , Ch02g01 ) previously reported to be of the perfect repeat type [89] , [90] , [91] . In total , we generated 5×105 simulated datasets for each model . A generalized stepwise model ( GSM ) was used as the mutational model . The model had two parameters: the mean mutation rate ( μ ) and the mean parameter ( P ) of the geometric distribution used to model the length of mutation events ( in numbers of repeats ) . As no experimental estimate of microsatellite mutation rate is available for Malus , the mean mutation rate was drawn from a uniform distribution by extreme values of 10−4 and 10−3 , and the mutation rate of each locus was drawn independently from a Gamma distribution ( mean = μ; shape = 2 ) . The parameter P ranged from 0 . 1 to 0 . 3 . Each locus L had a possible range of 40 contiguous allelic states ( 44 for CH02C06 , 42 for CH04E03 ) and was characterized by individual values for mutation rate ( μL ) and the parameter of the geometric distribution ( PL ) ; μL and PL were drawn from Gamma distributions with the following parameter sets: mean = μ , shape = 2 , range = 5×10−5–5×10−2 for μL , and mean = P , shape = 2 , range = 0 . 01–0 . 9 for PL . As not all allele lengths were multiples of motif length , we also included single-nucleotide insertion-deletion mutations in the model , with a mean mutation rate ( μSNI ) and locus-specific rates drawn from a Gamma distribution ( mean = μSNI; shape = 2 ) . The summary statistics used were: mean number of alleles per locus , mean genetic diversity [92] , genetic differentiation between pairwise groups ( FST; [93] ) , genetic distances ( δμ ) 2 [94] . We used a polychotomous logistic regression procedure [95] to estimate the relative posterior probability of each model , based on the 1% of simulated data sets closest to the observed data . Confidence intervals for the posterior probabilities were computed using the limiting distribution of the maximum likelihood estimators [64] . Once the most likely model was identified , we used a local linear regression to estimate the posterior distributions of parameters under this model [96] . The 1% simulated datasets most closely resembling the observed data were used for the regression , after the application of a logit transformation to parameter values . | The apple , one of the most ubiquitous and culturally important temperate fruit crops , provides us with a unique opportunity to study the process of domestication in trees . The number and identity of the progenitors of the domesticated apple and the erosion of genetic diversity associated with the domestication process remain debated . The Central Asian wild apple has been identified as the main progenitor , but other closely related species along the Silk Route running from Asia to Western Europe may have contributed to the genome of the domesticated crop . Using rapidly evolving genetic markers to make inferences about the recent evolutionary history of the domesticated apple , we found that the European crabapple has made an unexpectedly large contribution to the genome of the domesticated apple . Bidirectional gene flow between the domesticated apple and the European crabapple resulted in the domesticated apple being currently more similar genetically to this secondary genepool than to the ancestral progenitor , the Central Asian wild apple . We found that domesticated apples have evolved over long time scales , with contributions from at least two wild species in different geographic areas , with no significant erosion of genetic diversity . This process of domestication and diversification may be common to other fruit trees and contrasts with the models documented for annual crops . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"forestry",
"plant",
"science",
"plant",
"evolution",
"plant",
"biology",
"crops",
"genetics",
"plant",
"genetics",
"population",
"genetics",
"biology",
"evolutionary",
"biology",
"molecular",
"genetics",
"evolutionary",
"genetics",
"genetics",
"and",
"genomics",
"agriculture"
] | 2012 | New Insight into the History of Domesticated Apple: Secondary Contribution of the European Wild Apple to the Genome of Cultivated Varieties |
Misfolded proteins in transgenic models of conformational diseases interfere with proteostasis machinery and compromise the function of many structurally and functionally unrelated metastable proteins . This collateral damage to cellular proteins has been termed 'bystander' mechanism . How a single misfolded protein overwhelms the proteostasis , and how broadly-expressed mutant proteins cause cell type-selective phenotypes in disease are open questions . We tested the gain-of-function mechanism of a R37C folding mutation in an endogenous IGF-like C . elegans protein DAF-28 . DAF-28 ( R37C ) is broadly expressed , but only causes dysfunction in one specific neuron , ASI , leading to a distinct developmental phenotype . We find that this phenotype is caused by selective disruption of normal biogenesis of an unrelated endogenous protein , DAF-7/TGF-β . The combined deficiency of DAF-28 and DAF-7 biogenesis , but not of DAF-28 alone , explains the gain-of-function phenotype—deficient pro-growth signaling by the ASI neuron . Using functional , fluorescently-tagged protein , we find that , in animals with mutant DAF-28/IGF , the wild-type DAF-7/TGF-β is mislocalized to and accumulates in the proximal axon of the ASI neuron . Activation of two different branches of the unfolded protein response can modulate both the developmental phenotype and DAF-7 mislocalization in DAF-28 ( R37C ) animals , but appear to act through divergent mechanisms . Our finding that bystander targeting of TGF-β explains the phenotype caused by a folding mutation in an IGF-like protein suggests that , in conformational diseases , bystander misfolding may specify the distinct phenotypes caused by different folding mutations .
Cellular and organismal functions depend critically on the correct folding and intracellular targeting of proteins , and folding mutations are associated with many human pathologies , including neurodegenerative diseases and some forms of diabetes and cancer [1] . In addition to directly impairing the function of the affected protein , folding mutations can exhibit toxic-gain-of-function properties [2] . The mechanistic understanding of what links a specific toxic-gain-of-function mutation to the resulting disease phenotype is still very limited . One of the proposed mechanisms is global disruption of cellular folding environment , initiated by titration of chaperones , degradation machinery , and other proteostasis components by the disease-associated proteins [3–7] . We have previously shown that ectopic expression of disease proteins in C . elegans causes misfolding and loss of function of unrelated chaperone-dependent or metastable proteins in the same cell , which , in turn , drives the toxic phenotypes [5 , 8] . This collateral damage to normal cellular proteins by a gain-of-function mutant protein has been termed 'bystander' mechanism [9 , 10] and has also been shown in response to the high amyloid burden in Alzheimer's disease model as well as in other disease models [11–13] . How a single misfolded protein overwhelms the proteostasis and which cellular proteins are subject to this bystander effect are open questions . The latter question is particularly important for understanding how a broadly- or ubiquitously-expressed mutant protein can cause cell-specific dysfunction in disease . Finally , in many models of disease , mutant proteins are ectopically ( over ) expressed . Because such proteins may engage the proteostasis machinery differently than endogenous mutant proteins , it is important to ask whether the bystander effect can contribute to gain-of-function mechanisms exerted by endogenous mutant proteins expressed in their cognate cellular environment . These questions about the bystander effect are particularly relevant in the endoplasmic reticulum ( ER ) , where a single misfolded protein can cause folding stress and cellular dysfunction even though many other itinerant proteins in the ER are in the process of folding and assembly and , thus , are non-native [14–16] . Here , we ask how a folding mutation in the endogenous C . elegans insulin/IGF-like protein DAF-28 [17] affects folding or maturation of other unrelated proteins in the secretory pathway and probe the molecular events underlying the cell-selective phenotypic outcome of this mutation . We use the DAF-28 ( R37C ) mutation to test the bystander mechanism for three reasons . First , it causes folding stress in the ER , as seen by induction of the unfolded protein response ( UPR ) [18] . Second , IGF proteins , the mammalian counterparts of DAF-28 , are strictly dependent on the molecular chaperone GRP94 for their folding and secretion [19 , 20] , indicating a strong engagement of this family of proteins with the ER proteostasis machinery . Third , DAF-28 ( R37C ) mutant animals exhibit a specific and quantifiable developmental phenotype called dauer diapause resulting from dysfunction of a single chemosensory neuron ( ASI ) , despite expression of the mutant DAF-28 protein in nine different tissues [21] . DAF-28 ( R37C ) mutant protein is encoded by a semi-dominant sa191 allele and causes inappropriate dauer entry [22] . In C . elegans , exposure of first larval stage animals ( L1 ) to adverse conditions , such as crowding , limited food , and elevated temperature , triggers a switch from reproductive growth to an alternative stress-resistant developmental stage known as dauer diapause [23] . The decision to continue reproductive development or to enter dauer is specified by secretion of the insulin/IGF-like protein DAF-28 ( referred to here as IGF-like ) and the TGF-β protein DAF-7 from the ASI sensory neurons [17 , 24–26] ( Fig 1A ) . The ASI neuron is the main source of the DAF-7/TGF-β in dauer signaling [27] . Loss of daf-7 results in partial activation of the dauer state even under growth-promoting conditions , and in a complete dauer entry under sensitizing conditions , such as elevated temperature [28] . In contrast , deletion of daf-28 does not cause dauer signaling due to redundancy with other insulin/IGF-like proteins [29 , 30] , consistent with the gain-of-function for sa191 allele . Overexpression of the wild-type DAF-28 or other insulin/IGF-like proteins ( INS-4 or INS-6 ) can rescue dauer induction in sa191 animals [17] , suggesting that the mutant DAF-28 ( R37C ) protein may have a dominant-negative effect on the wild-type pro-growth IGF-like proteins . The R37C substitution is in a predicted RXXR proteolytic cleavage site of DAF-28 [17] . In mammalian insulins and IGFs , proteolytic processing in Golgi or secretory vesicles follows their normal folding and disulfide bond formation in the ER . Thus , the phenotype of R37C mutation may be due to misprocessing of DAF-28 . However , DAF-28 with a different mutation in the same residue , R37A , does not cause a gain-of-function and is partially active [17] . Similarly , arginine substitutions in the KR cleavage site in human insulin—R89L , R89P , or R89H—result in a protein that is misprocessed but non-toxic and efficiently secreted , causing hyperproinsulinemia; however , mutation to a cysteine at the same residue—R89C—results in a severely misfolded protein and causes permanent neonatal diabetes mellitus ( PNDM ) [31 , 32] . These observations argue against misprocessing as the cause for the gain-of-function toxicity of DAF-28 ( R37C ) . Interestingly , many of the insulin folding mutations that cause PNDM and the mutant INS-gene-induced diabetes of youth ( MIDY ) generate unpaired cysteines [32 , 33] . Similarly , a disulfide bond-disrupting C ( A7 ) Y mutation in the Ins2 gene of Akita mouse , a diabetes model , is a toxic-gain-of-function mutation and results in insulin misfolding , induction of UPR , and eventual death of insulin-producing beta cells [34 , 35] . Conversely , an engineered insulin mutant carrying the Akita mutation but lacking all cysteines does not interfere with the wild-type insulin , despite being severely misfolded [33] . Thus , in addition to the general danger of having unpaired cysteines in the oxidizing environment of the secretory pathway , the insulin/IGF fold may be particularly sensitive to these mutations due to the topologically complex arrangement of three ( four in DAF-28 ) disulfide bonds in a small protein . Abnormal UPR induction is thought to be one of the mechanisms by which misfolded secretory proteins cause cellular dysfunction in many proteinopathies [36 , 37] . In some cases , such as in PNDM/MIDY , affected cells produce large amounts of the mutant protein , which triggers the UPR [38] . However , when the mutant protein represents only a small fraction of the non-native species in the ER , the mechanism of UPR induction is less clear , as it is not well understood what effect misfolding of one non-abundant protein has on the biogenesis of other proteins in the same compartment . Here , we find that the R37C folding mutation in the broadly-expressed IGF-like protein DAF-28 induces defects in the protein biogenesis of the endogenous DAF-7/TGF-β expressed in the ASI neuron . The combined deficiency in DAF-28 and DAF-7 biogenesis , but not in DAF-28 alone , explains the gain-of-function phenotype of the DAF-28 ( R37C ) mutation—deficient pro-growth signaling by the ASI neuron . This toxic effect can be modulated by ER chaperones but is observed prior to the ASI-specific UPR induction , indicating that a targeted defect in secretory protein biogenesis , rather than global ER stress , is a triggering event . Using a functional , fluorescently-tagged reporter , we find that the wild-type DAF-7 is normally localized to the sensory dendrite . However , in animals with misfolded DAF-28/IGF , DAF-7/TGF-β becomes mislocalized and accumulates in the proximal axon of the ASI neuron . The finding that bystander targeting of TGF-β explains the phenotype of a folding mutation in an IGF-like protein suggests that cellular context ( i . e . the cell-specific composition of the proteome ) may determine the distinct phenotypic outcomes of the different folding mutations implicated in conformational diseases .
daf-28 ( sa191 ) mutants expressing DAF-28 ( R37C ) protein have fully penetrant dauer arrest at elevated temperature of 25°C [22] . We have previously shown that growth at 25°C induces misfolding of temperature-sensitive and chaperone-dependent ( metastable ) proteins in C . elegans [5] , reflecting increased burden on the proteostasis machinery . Growth at 25°C also leads to changes in stress and longevity pathways and to altered fecundity , which may , in turn , affect proteostasis [39–41] . Elevated temperature itself is highly sensitizing to dauer entry , and a further 2°C increase can cause dauer entry even in wild-type animals [42] . We tested whether sa191 gain-of-function phenotype is still present at permissive temperatures . A majority of sa191 animals raised under growth-promoting conditions—20°C , abundant food , and low population density—abnormally entered the pre-dauer developmental stage L2d ( Fig 1B and 1C ) , indicating deficient pro-growth signaling by the ASI neuron ( Fig 1A ) [22] . As previously reported , the entry to L2d was transient , with many animals resuming the normal development within hours and some progressing to dauer before resuming the normal development . To circumvent this variability , we scored development of sa191 animals at 20 or 15°C at 65–66 or 90–91 hours post-gastrula , respectively . At these time points , most of the wild-type animals become reproductive adults ( green , Fig 1B and 1C ) and any time spent in L2d and/or dauer stages is reflected as mild or severe developmental delay ( yellow/red , Fig 1B and 1C ) . Unlike wild-type , 69±16% of sa191 animals raised at normal growth temperature ( 20°C ) were mildly delayed , and 29±15% were severely delayed as either early L4 larvae , dauers or pre-dauers/L2ds ( Fig 1C ) . By contrast , only 6±1% animals with daf-28 loss-of-function allele tm2308 were mildly delayed and 3±4% severely delayed . At low growth temperature ( 15°C ) , 98±1% of daf-28 ( sa191 ) animals were still mildly delayed ( Fig 1C ) , showing that DAF-28 ( R37C ) mutation exerts its gain-of-function properties even at low growth temperature . Entry into the L2d stage results from activation of some but not all of the converging dauer signals , such as decreased signaling from DAF-7/TGF-β [27] . DAF-7 is secreted from the ASI neurons and functions in parallel to DAF-28 ( Fig 1A ) . Indeed , most animals carrying the e1372 loss-of-function allele of daf-7 entered L2d stage at 20°C , resulting in growth delay at 65–66 hours ( Fig 1C ) . In this respect , the R37C gain-of-function mutation in DAF-28/IGF protein behaves as a phenocopy of the loss-of-function mutation of DAF-7/TGF-β . Thus , we asked whether DAF-7 protein was functional in daf-28 ( sa191 ) mutants . We used an established DAF-7 activity reporter—a cuIs5 transgene expressing GFP in the pharynx from a SMAD-dependent promoter ( SMAD::GFP ) . Reporter fluorescence is bright only when DAF-7 is secreted and is attenuated when DAF-7 secretion is low . At the late L1 larval stage , when DAF-7 and DAF-28 secretion determines the development vs . dauer decision , wild-type animals exhibited bright reporter GFP fluorescence ( Fig 2A and 2B ) . In contrast , many daf-28 ( sa191 ) animals had decreased GFP fluorescence ( Fig 2A and 2B ) , indicating decreased DAF-7 activity . The decrease in fluorescence was variable: some sa191 animals were comparable to daf-7 loss-of-function animals , while others showed intermediate to wild-type levels . Decreased DAF-7 activity in daf-28 ( sa191 ) animals could due to be its transcriptional downregulation . However , daf-7 expression does not depend on insulin signaling in C . elegans , and we did not detect a decrease in expression of its transcriptional reporter in the ASI neuron of sa191 animals ( [17] and S1A Fig ) . We asked whether the variability in SMAD-dependent GFP fluorescence , the proxy for DAF-7 activity , was related to the variability in the sa191 dauer phenotype . We separated the daf-28 ( sa191 ) ;cuIs5 animals by eye into ‘bright’ and ‘dim’ populations at the end of the L1 larval stage and scored their development . Indeed , we found that daf-28 ( sa191 ) mutants with ‘dim’ SMAD-dependent GFP fluorescence had a higher percentage of severely delayed animals ( 24% v . 9% ) than those with ‘bright’ fluorescence ( Fig 2C ) . To test whether decreased DAF-7 function contributes directly to the dauer phenotype in daf-28 ( sa191 ) mutants , we asked if over-expression of DAF-7 could rescue this phenotype . We used two independently generated transgenes . The first , adEx2202 , expresses daf-7 cDNA in the ASI neurons under the control of the gpa-4 promoter ( ASI::DAF-7 ) [43] . When crossed into daf-28 ( sa191 ) mutants , the ASI::DAF-7 transgene completely rescued the severe developmental delay , but not the mild delay ( Fig 2D ) . Thus , the adEx2202 transgene prevented the persistence of the L2d partial dauer stage in sa191 animals , or their commitment to dauer , but did not completely rescue the deficient pro-growth signaling . Second , we constructed a mCherry::DAF-7 fusion protein expressed from its cognate daf-7 promoter , using a strategy previously used to generate functionally-tagged TGF-β proteins—Dpp in Drosophila and DBL-1 in C . elegans ( S1B Fig ) [44 , 45] . mCherry was chosen because it lacks cysteines and , thus , would not be expected to interfere with oxidative folding of DAF-7 , which contains a disulfide bond-rich cysteine knot domain . The mCherry::DAF-7 protein was functional , as it efficiently rescued both characteristic phenotypes of the daf-7 ( e1372 ) loss-of-function allele—developmental delay/L2d entry in early larvae ( Fig 2E ) and the egg retention/dark intestine phenotype in adults ( >99% ) . Surprisingly , the dauer phenotype was also rescued in non-transgenic e1372 progeny of transgenic parents ( Fig 2E , N-Tg sib . ) , but only in the first generation , suggesting it was due to a maternal contribution . When expressed in daf-28 ( sa191 ) animals , the functional mCherry::DAF-7 protein rescued their severe developmental delay from 67% to 5±3% and supported normal reproductive development in the majority of animals ( Fig 2D ) . Interestingly , the L2d/dauer phenotype was again rescued in the first-generation non-transgenic sa191 progeny , indicating that the gain-of-function phenotype of DAF-28 ( R37C ) mutation could be rescued through maternal contribution of DAF-7 . Since the ASI-restricted adEx2202 transgene did not rescue the dauer phenotype in the first-generation non-transgenic siblings ( N-Tg . Sib . v . ASI::DAF-7 , Fig 2D ) , the maternal rescue may depend on the non-ASI expression of DAF-7 , perhaps due to the promoter or intronic elements present in our mCherry::DAF-7 transgene . Alternatively , the timing and/or strength of the gpa-4 promoter in the adEx2202 transgene may not be sufficient to see this rescue . Overexpression of DAF-7 could be rescuing the dauer entry in sa191 animals non-specifically , by simply increasing pro-growth signaling over the dauer induction threshold . If so , it should also decrease the abnormal dauer entry caused by genetic loss of all pro-growth insulin/IGF signaling . Deletion of daf-28 alone is not sufficient to induce dauer ( [29] and Fig 1C ) . However , a ZM7963 strain with a combined deletion of daf-28 , ins-4 , ins-5 , and ins-6 ( designated as 4xDel ) has a constitutive dauer phenotype at 20°C . Importantly , the dauer phenotype of this strain can be rescued by overexpression of DAF-28 alone , or by INS-4 or INS-6 , due to the redundancy between deleted insulin/IGF proteins [30] . In contrast to its strong rescue of daf-28 ( sa191 ) , mCherry::DAF-7 had no effect on the dauer induction in the 4xDel strain ( Fig 2D ) , indicating that it only rescues the specific defect caused by the sa191 mutation . Taken together , our results suggest that decreased availability of secreted DAF-7 protein underlies the gain-of-function mechanism of dauer induction in animals carrying DAF-28 ( R37C ) mutation . How could a putative folding mutation in DAF-28/IGF protein disrupt DAF-7/TGF-β activity ? Since both proteins need to be secreted to signal reproductive development , and since UPR induction specifically in the ASI neuron has been implicated in the sa191 dauer phenotype [18] , we wanted to test two possible mechanisms: ( 1 ) the ASI-specific UPR , induced by the misfolded DAF-28 protein , leads to global ASI neuron dysfunction during growth vs . dauer decision and ( 2 ) the bystander effect , i . e . a targeted collateral damage to the endogenous cellular proteins , including DAF-7 protein , from the misfolded DAF-28 ( R37C ) . Since overactive UPR can cause generalized cellular dysfunction and degeneration and interfere with development [46 , 47] , we first asked if there is UPR induction in the ASI neurons of daf-28 ( sa191 ) animals at the time when the growth vs . dauer decision is made—late L1/early L2 larval stages . To visualize UPR in individual cells , we used a phsp-4::GFP transgene , which has been shown to be a specific and sensitive reporter of UPR in C . elegans [48] . In wild type animals , the UPR reporter is weakly induced in the intestine and seam cells , and is undetectable in neurons ( Fig 3A and 3B ) . Surprisingly , we did not observe a specific or strong induction of UPR in the ASI neurons of sa191 animals at L1/L2 stages . Instead , we observed similar induction of the UPR reporter in several different head neurons of L2 animals , and no reporter induction in most L1 animals ( Fig 3C , 3D and 3E ) . This was not due to lack of sensitivity , as we easily detected reporter expression in non-ASI cells of same animals , including seam cells and intestine ( Fig 3C and 3D ) . Induction of the UPR reporter became more pronounced in the ASI neuron in older animals , eventually becoming the predominant source of GFP fluorescence ( L3 , Fig 3F ) . In C . elegans , deletion of PERK increases hsp-4 expression [49] , and we observed a striking upregulation of the UPR reporter in the intestine of unstressed pek-1 ( - ) animals ( Fig 3G ) . Compared to its intensity in cells of pek-1 ( - ) animals , the UPR reporter induction in the head neurons of daf-28 ( sa191 ) L1/L2 animals ( Fig 3C–3F ) was very weak . We also noted that deletion of pek-1 , that is known to rescue dauer phenotype of sa191 animals [18] , did not eliminate ER stress in neurons of L2 sa191 animals ( Fig 3H and 3I ) . The observed UPR reporter induction in intestine and seam cells of daf-28 ( sa191 ) animals could reflect misfolding of the DAF-28 ( R37C ) mutant protein in these cells . In addition to the ASI and other head neurons , daf-28 is expressed in eight other tissues , including pharynx , hypodermis , ventral nerve cord , intestine and several reproductive tissues [21] . To examine possible dysfunction of these cells , we assayed adult body size as indicator of intestinal and pharyngeal function , brood size for dysfunction in daf-28-expressing reproductive cells ( vulva muscles , gonad sheath cells , or distal tip cell ) , and swimming as proxy for gross dysfunction of ventral nerve cord . We found no significant differences between wild-type animals and daf-28 ( sa191 ) mutants for these phenotypes ( Fig 3J–3L ) . Previous reports also found no other deficiencies specific to the sa191 allele beyond dauer induction [22] . Finally , as noted by Li et . al . [17] , the highly transient nature of the L2d/dauer entry in sa191 animals indicates normal function of the ASJ neuron that regulates exit from dauer , despite expression of DAF-28 ( R37C ) protein . Thus , expression of the mutant DAF-28 ( R37C ) protein and its activation of UPR in cells and tissues other than the ASI neuron are not sufficient to cause cellular dysfunction . Importantly , the DAF-7 secretion defect in sa191 animals was observed already in the late L1 stage ( Fig 2A and 2B ) , prior to the strong and cell-specific UPR induction in the ASI neuron of older animals . Together , our data argue against the UPR being the initiating factor for the global ASI dysfunction and for the molecular events leading to the gain-of-function toxicity of DAF-28 ( R37C ) protein . If the R37C mutation indeed causes misfolding , its phenotypic expression would be expected to depend on ER chaperones and folding environment . In C . elegans , XBP-1 is a UPR transcription factor that is activated through a conserved splicing mechanism in response to the folding stress in the ER and , thus , upregulates expression of the ER chaperones and proteostasis components [48] . Importantly , transgenic expression of the active , spliced protein ( XBP-1s ) upregulates ER chaperone levels without causing ER stress [50] . We found that pan-neuronal expression of XBP-1s strongly rescued the gain-of-function dauer phenotype of sa191 animals , decreasing the number of severely delayed animals from 29±18% to 3±3% ( Fig 4A ) . This rescue , however , became less robust over several generations . This could be due to a silencing of the xbp-1s-expressing transgene in this genetic background , or could suggest a complex genetic interaction between the chronic folding stress and the constitutive XBP-1s activity . To ask whether the observed dauer rescue was related to improved DAF-7 activity , we measured the SMAD-GFP reporter fluorescence . Spliced XBP-1 efficiently rescued the decrease in SMAD::GFP fluorescence in sa191 animals ( Fig 4B ) . Of note , the positive effect of XBP-1s on the SMAD::GFP fluorescence did not attenuate over generations . Conversely , deletion of ER chaperones would be expected to exacerbate the phenotypes caused by misfolding of DAF-28 ( R37C ) , as it decreases the cell's ability to deal with misfolded proteins . To test this , we targeted HSP-4 , a stress-inducible form of the major HSP-70 ER chaperone BiP in C . elegans [48 , 49] . hsp-4 is expressed in only few tissues at basal conditions , and animals with hsp-4 ( gk514 ) deletion appear wild-type in the absence of applied stress . Deletion of hsp-4 resulted in increase in severely delayed animals from 29±18% to 83±6% , with majority of these being in L2d/dauer stages ( Fig 4A ) . Together , these data show that the developmental phenotype of sa191 allele can be modulated by molecular chaperones , consistent with the idea that misfolding of DAF-28 ( R37C ) contributes to its gain-of-function . Under acute ER stress conditions , activation of the PERK/eIF2α branch of the UPR allows for cellular recovery via transient attenuation of translation [51 , 52] . However , if translational attenuation was present during early development , it could lead to insufficient production of DAF-28 and DAF-7 proteins and , thus , the dauer phenotype of animals with misfolded DAF-28 ( R37C ) . Indeed , at 25°C , activation of PERK/eIF2α branch of the UPR specifically in the ASI neurons contributes to the dauer phenotype of daf-28 ( sa191 ) mutants [18] . Although we did not detect a decrease in pdaf-7::GFP fluorescence in the ASI neurons of L1-L2 daf-28 ( sa191 ) larvae ( S1A Fig ) , the GFP reporter may not be sensitive enough to detect translational attenuation . Thus , we asked whether elimination of PERK signaling was able to rescue the sa191 phenotype under our growth conditions . Deletion of pek-1 indeed partially rescued the gain-of-function phenotype in daf-28 ( sa191 ) mutants at 20°C ( Fig 4C ) when the animals were grown at a low density . However , we noticed that many daf-28 ( sa191 ) ;pek-1 ( - ) animals entered the L2d stage even at permissive temperature and in the presence of abundant food when plates became crowded ( Fig 4D–4H and S2 Fig ) . Since increased population density , and subsequent increased pheromone signaling , is one of the environmental inputs into the growth vs . dauer decision , loss of PERK may be selectively affecting specific aspects of the dauer signaling in sa191 animals . Thus , we wanted to ask whether dauer rescue by deletion of pek-1 is mediated by the rescue of DAF-7 activity , similar to what we found with spliced XBP-1 . We were unable to directly assess SMAD::GFP activity due to difficulty with crosses . Instead , we asked whether rescue by DAF-7 overexpression is also sensitive to the population density and found that , unlike deletion of pek-1 , mCherry::DAF-7 rescues the dauer phenotype of sa191 animals even at high density ( Fig 4D ) . Thus , rescue of the sa191 dauer phenotype by pek-1 deletion may not depend on increase in DAF-7 activity . Overall , out data show that the phenotypic expression of DAF-28 ( R37C ) mutation depends on the ER folding environment , and that the DAF-7 activity defect in these animals may be differentially affected by the two branches of the UPR . Since we found that UPR does not trigger the dauer signaling in daf-28 ( sa191 ) animals , we tested the second proposed mechanism—bystander misfolding of endogenous cellular proteins . We considered two potential targets: other pro-growth insulin/IGF-like proteins and DAF-7 . To examine the effect of mutant DAF-28 ( R37C ) on wild-type insulin/IGF-like proteins , we generated a wild-type DAF-28::mCherry and followed its localization in wild-type and sa191 animals . The DAF-28::mCherry expression followed the reported expression pattern of pdaf-28::GFP transcriptional reporter , with fluorescent protein detected in head and tail neurons , pharynx , hypodermis , and other tissues ( Fig 5A and 5B ) . DAF-28::mCherry was efficiently secreted upon expression in L1 stage , as detected by its uptake by endocytic scavenger cells , coelomocytes [53] ( Fig 5C and 5D ) . Interestingly , compared to the DAF-28::mCherry fusion protein , the previously described DAF-28::GFP protein [17 , 54] was not efficiently secreted , as judged by coelomocyte fluorescence , and instead appeared to accumulate in neuronal cell bodies ( Fig 5C and 5D ) . As discussed below , this may be due to misfolding of the GFP moiety . To verify the functionality of the DAF-28::mCherry fusion protein , we crossed it into the 4xDel strain . Despite mosaic expression , DAF-28::mCherry protein efficiently rescued the dauer phenotype of the 4xDel strain , showing that this protein is functional ( Fig 5E ) . Confocal imaging showed that DAF-28::mCherry protein is predominantly found in a punctate pattern reminiscent of the secretory vesicles in mammalian cells expressing insulin or IGF , with some puncta found in a regularly spaced pattern adjacent to the ASI axons ( Fig 5F ) . Inactivation of unc-104 , the C . elegans homologue of the axonal kinesin KIF1A , eliminated these axon-adjacent puncta ( Fig 5G ) , suggesting that they represent either local accumulation of DAF-28::mCherry protein secreted from the ASI axon , or protein present in axons of other neurons expressing daf-28 . The orientation of the ASI cell body , axon and dendrite is shown in Fig 5H . Misfolded insulin with unpaired cysteines can exert a dominant-negative effect on the wild-type insulin [33 , 55] . To determine whether interference with biogenesis of wild-type insulin/IGF-like proteins contributes to the gain-of-function of sa191 allele , we crossed sa191 animals to those expressing the functional DAF-28::mCherry protein . DAF-28::mCherry was still secreted , and we did not detect major redistribution of the fluorescent signal to the cell bodies as would be expected if the protein was retained in the ER ( Fig 6A ) . We did detect minor alterations in the ASI axon-adjacent punctate pattern of wild-type DAF-28::mCherry in these animals ( Fig 6A and 6B ) , which became less regular . Next , we generated a DAF-28 ( R37C ) ::mCherry transgenic strain . We found that this transgene phenocopied the endogenous sa191 mutation , as it caused severe developmental delay in the daf-28 ( tm2308 ) deletion background ( Fig 6C ) . Imaging revealed dramatic differences in localization of DAF-28 ( R37C ) ::mCherry mutant protein as compared to wild-type . First , the protein appeared to strongly accumulate in the pharyngeal muscle and , to a lesser extent , in hypodermal tissue in the head , two tissues known to express daf-28 ( Fig 6D , 6E and 6F ) . DAF-28 ( R37C ) ::mCherry also appeared to accumulate in the ASI proximal axons , forming large aggregate-like puncta ( Fig 6E–6H , block arrows ) . Unlike the puncta seen with the wild-type DAF-28::mCherry protein , these puncta were contained within the neuronal processes ( Fig 6F and 6G ) . Strikingly , these puncta created voids in the fluorescence of soluble cytosolic GFP expressed in the ASI neurons , suggesting that the transgenic mutant protein disrupts axonal architecture of these cells ( Fig 6H ) . Surprisingly , at least some of the DAF-28 ( R37C ) ::mCherry mutant protein was secreted , as evidenced by its presence in coelomocytes as early as L1 stage ( Fig 6D , cc ) . Our data thus show that endogenous DAF-28 ( R37C ) protein causes mild axonal defects , while overexpression of the DAF-28 ( R37C ) ::mCherry transgene , in addition to its own mistrafficking and accumulation , causes significant disruption of the ASI axons . We took advantage of the mCherry-tagged DAF-28 ( R37C ) to ask if the R37C mutation was indeed causing oxidative misfolding of this IGF-like protein . Misfolded MIDY insulins are known to engage in abnormal disulfide-linked protein complexes , resulting in loss of their detection as discrete species under non-reducing conditions [33] . DAF-28 ( R37C ) ::mCherry protein similarly did not resolve into any predominant bands under non-reducing conditions , while treating the worm lysates with reducing reagents produced a single band of expected size , approximately 37 kDa ( Fig 6I , white arrow ) . As control , mCherry protein alone resolved into a single band under both reducing and non-reducing conditions ( Fig 6I , black arrows ) . Although this is consistent with DAF-28 ( R37C ) ::mCherry protein being abnormally engaged in intermolecular disulfide-linked protein complexes , we could not unambiguously conclude this , since our ability to detect the wild-type DAF-28::mCherry protein , which , unlike the mutant , does not accumulate in any tissues , was not reliable ( S3 Fig ) . Finally , we examined the functionality and localization of the DAF-28::GFP protein , since we have found that it is not efficiently secreted ( Fig 5C and 5D ) . DAF-28::GFP protein exhibited a strong toxic gain-of-function phenotype , causing severe developmental delay in the daf-28 ( tm2308 ) deletion background ( Fig 6C ) . Moreover , DAF-28::GFP accumulated in both the cell body and axons of the ASI and ASJ neurons , eventually filling the cell bodies ( Fig 6J–6L ) . This is consistent with misfolding and ER retention of a GFP-tagged secretory protein , since GFP is known to undergo oxidative misfolding in the ER of mammalian cells due to the presence of two buried cysteine residues [56 , 57] . Our data so far indicate that neither of the two most straightforward mechanisms—UPR induction or dominant-negative effect on wild-type insulin/IGF-like proteins—completely explain the gain-of-function phenotype caused by DAF-28 ( R37C ) protein . As we observed defects in daf-7 signaling in sa191 animals ( Fig 2 ) , we next asked whether localization or secretion of our functional mCherry::DAF-7 protein was affected by the presence of the endogenous DAF-28 ( R37C ) protein . In the wild-type background , mCherry::DAF-7 fluorescence was detected in neurons as well as in the pharyngeal muscles ( Fig 7A–7E ) . In neurons , we observed punctate fluorescence in the cell bodies and in the area posterior to the ASI cell body ( Fig 7A , 7D and 7E , stars and arrowheads , respectively ) . In contrast to DAF-28::mCherry , which was present in axons but excluded from dendrites , mCherry::DAF-7 protein was mainly detected along the dendrites of the ASI neurons ( Fig 7A–7E , block arrows ) . Only rare small puncta were noted in the proximal axons ( Fig 7E , arrow ) . Consistent with dendritic trafficking , we observed a large accumulation of the fluorescent signal surrounding the base of the ASI sensory cilia located at the distal end of the dendrite ( Fig 7A , 7C and 7D , square brackets ) . Although the maturation and trafficking of DAF-7/TGF-β have not been characterized in C . elegans , the signal outside the cilia could represent locally secreted mCherry::DAF-7 protein . To test this , we used a transgene that expresses active caspase specifically in the ASI neurons [58] . Activation of apoptosis in the ASI neurons collapsed both the ASI cell body- and posterior-localized fluorescent signal and strongly decreased the accumulation of mCherry fluorescence near cilia ( Fig 7F–7I ) , confirming that they all represent the mCherry::DAF-7 protein secreted from the ASI neuron . ASI apoptosis did not eliminate the pharyngeal mCherry fluorescence ( Fig 7H ) , suggesting that DAF-7 protein may also be expressed in the pharynx . When placed in the background of the daf-28 ( sa191 ) mutation , mCherry::DAF-7 protein did not accumulate in the ASI cell bodies ( Fig 8A–8F ) , and we did not detect any severe defects in the dendritic targeting ( Fig 8B ) . Strikingly , unlike in a wild-type background , mCherry::DAF-7 protein accumulated in the proximal regions of the ASI axons in sa191 mutant animals ( Fig 8A–8H ) . This mistargeting of the mCherry::DAF-7 protein was evident already in the L1 ( Fig 8A and 8C ) and early L2 ( Fig 8D and 8E ) larval stages , which is the time the DAF-7 function in sa191 animals is compromised ( Fig 2A and 2B ) . This was not due to a generic trafficking defect , as localization of an unrelated secretory protein ChannelRhodopsin-2::YFP [59] to dendrites and axons was not affected by sa191 ( Fig 8I and 8J ) , and the shape of ASI cilia , which depends on the cellular trafficking , was also normal ( Fig 8B and 8J , bottom panel ) . Thus , the mistargeting of DAF-7 to the axon of the ASI neuron is a specific molecular consequence of the expression of misfolded DAF-28 ( R37C ) in the same cell , and may reflect the molecular events underlying the gain-of-function mechanism in sa191 animals . To quantify the mistargeting , we scored L1 or L2 animals with one or both of the mCherry-positive ASI neurons visualized in their entirety . In daf-28 ( sa191 ) mutant animals , 5 out of 9 ASI neurons examined had accumulated mCherry::DAF-7 protein in their axons , while 0 out of 8 ASI neurons in animals with wild-type daf-28 had such axonal accumulation . This was not simply due to the daf-28 ( sa191 ) background being sensitizing to dauer entry , since we did not detect ( 0 out of 6 ) axonal mistargeting of mCherry::DAF-7 protein in the equally sensitized daf-7 ( e1372 ) animals . The accumulation of mCherry::DAF-7 in the ASI axon and in the pharynx was even more prominent in older daf-28 ( sa191 ) mutant animals ( Fig 8F–8H , L4 animal shown ) . Based on these data , the folding mutation in the endogenous DAF-28/IGF protein leads to aberrant localization and accumulation of the wild-type bystander DAF-7/TGF-β protein in the axons of affected neurons . If the ectopic mCherry::DAF-7 protein is mislocalized in the ASI neurons of sa191 animals , how does it rescue the dauer phenotype ? First , it is possible that overexpression of mCherry::DAF-7 protein results in the UPR induction and increased chaperone expression in the ASI neuron . We consider it unlikely , as we did not detect significant induction of the UPR reporter in the ASI neurons of L1-L2 animals expressing mCherry::DAF-7 ( Fig 9A and 9B ) . It is also possible that the protein fraction that is still correctly localized to the ASI dendrite or secreted from its cell body is sufficient to produce the necessary pro-growth signaling . However , since we obtained much stronger rescue with the protein expressed from pdaf-7 promoter than with ASI-specific expression ( Fig 2D ) , we asked whether DAF-7 protein may be expressed in cells other than the ASI neuron and other DAF-28-expressing cells , or at an earlier time than the mutant DAF-28 ( R37C ) protein . Indeed , the SMAD::GFP reporter activity is clearly detectable in the developing pharynx already in the early embryonic stages ( Fig 9C , comma-stage embryo ) . We detected expression of the established daf-7 promoter-GFP transcriptional reporter ksIs2 in multiple developing neurons in comma-stage embryos , as well as in several neurons in 3-fold embryos ( Fig 9D–9G ) . Importantly , we detected accumulation of secreted mCherry::DAF-7 protein in the extraembryonic fluid at the same embryonic stages as the SMAD::GFP fluorescence ( Fig 9E , comma stage shown ) , suggesting that DAF-7 activity may have a physiological role in the early embryos . We also detected pharyngeal expression and localization to coelomocytes in 3-fold embryos ( Fig 9F and 9G ) . However , the earliest we were able to detect DAF-28::mCherry protein was in the coelomocytes of 2 . 5-fold stage embryos ( Fig 9H and 9I ) . Thus , the rescue of daf-28 ( sa191 ) dauer phenotype by the overexpressed mCherry::DAF-7 protein could be due to its secretion from cells other than the ASI neurons , or due to its expression prior to the onset of DAF-28 ( R37C ) expression . Finally , we asked whether expression of spliced XBP-1 and deletion of pek-1 rescued the daf-28 ( sa191 ) gain-of-function dauer phenotype by relieving the mislocalization of DAF-7 protein in the ASI neurons . Introduction of spliced XBP-1 into mCherry::DAF-7;daf-28 ( sa191 ) animals significantly reduced the mislocalization of mCherry::DAF-7 protein ( Fig 10A and 10B ) . Of 14 axons examined , we found only two , in the same animal , with significant axonal localization , and additional two with intermediate phenotype . Together with the rescue of SMAD::GFP induction in sa191 animals ( Fig 4B ) , these data suggest that expression of spliced XBP-1 indeed rescues the bystander targeting of DAF-7 by the mutant DAF-28 . In contrast to the spliced XBP-1 , introduction of pek-1 deletion allele into mCherry::DAF-7;daf-28 ( sa191 ) animals not only did not rescue , but appeared to enhance the DAF-7 mislocalization ( Fig 10C and 10D ) . The ASI proximal axons , and even some of their synaptic regions , appeared filled with red fluorescence ( Fig 10D ) . Strikingly , mCherry::DAF-7 protein showed massive accumulation in the ASI neuronal cell bodies in these animals ( Fig 10D , star ) . These data agree with the previous conclusion from Fig 4D that pek-1 ( - ) may rescue the dauer phenotype of daf-28 ( sa191 ) animals by mechanism other than increasing DAF-7 activity . Therefore , although both UPR branches tested here are able to modulate the gain-of-function phenotype of the folding mutation in DAF-28 , they appear to lead to vastly different molecular outcomes on the bystander target protein , DAF-7 .
Our data support oxidative misfolding of the mutant DAF-28 protein as the most proximal cause . As expected for a folding mutation [60] , altering the overall folding capacity of the ER modulated the penetrance of the dauer phenotype caused by the endogenous DAF-28 ( R37C ) mutation , while the transgenic mutant protein accumulated in tissues and exhibited aggregation-like behavior . Interestingly , as judged by its uptake into coelomocytes , at least some of the DAF-28 ( R37C ) ::mCherry mutant protein was secreted . ER quality control mechanisms typically prevent secretion of misfolded or non-native proteins . However , classical amyloid diseases are associated with secretion of destabilized amyloidogenic proteins , and increasing the stringency of the ER quality control by activation of the UPR transcription factor ATF6 selectively decreases their secretion [61] . The mechanism by which some of the DAF-28 ( R37C ) ::mCherry protein evades ER quality control is unclear . One possibility is that incorrect disulfide bond pairing in a small insulin/IGF-like mutant protein may result in an alternative structure that , while non-native , may present as a globular compact protein . Indeed , mammalian IGF is known to form two alternative stable conformations in vitro , only one of which has the correct disulfide bond arrangement and is active [62] , and a non-native mammalian mini-proinsulin has been shown to bypass ER quality control and be secreted in yeast [63] . Misfolded insulin , encoded by PNDM/MIDY mutations , can affect secretion of the wild-type insulin from the same cell [32 , 33] . On the other hand , Rajan et . al . [55] found that some insulin mutants , including PNDM-associated R89C , do not interfere with secretion of the wild-type insulin . Both R89C human insulin mutation and the C . elegans R37C mutation in DAF-28 introduce an unpaired cysteine while also disrupting the proteolytic processing site . Interestingly , similar to our DAF-28 ( R37C ) ::mCherry , the R89C mutant insulin was partially targeted to secretory granules and secreted , even though it strongly activated ER stress response and expression of the pro-apoptotic protein CHOP [55] . We did not detect ER retention of the wild-type DAF-28::mCherry protein in animals with sa191 mutation and observed only mild alterations in its axonal localization . A detailed biochemical characterization will be necessary to fully understand the molecular consequences that expression of DAF-28 ( R37C ) has on either wild-type DAF-28 or other insulin/IGF-like proteins . Our second observation is that , while activation of UPR plays a complex role in the phenotypic outcome of the sa191 mutation , it is unlikely to be its triggering mechanism . At the time sa191 mutation exerts its phenotypic effect ( L1/early L2 stages ) , the UPR reporter activity in the ASI neurons was either not induced , or was similar to that in several other neurons and much weaker than its induction in cells of animals carrying pek-1 deletion . Overexpression of the spliced XBP-1 showed that forced activation of this adaptive UPR branch can attenuate dauer signaling in sa191 animals , by suppressing the bystander effects of the mutant DAF-28 on localization and/or activity of DAF-7 . Deletion of pek-1 , on the other hand , prevented commitment to dauer by sa191 animals under non-stressful growth conditions , as was reported at high temperature [18] . Thus , unlike activation of IRE1/XBP1 branch of UPR , activation of PERK actually enhances the dauer phenotype of sa191 animals . However , about 20% of pek-1-deficient sa191 animals still abnormally entered the pre-dauer L2d stage at higher population densities ( Fig 4D ) , indicating that translational silencing or any transcriptional outcomes of the PERK activation did not , by themselves , initiate the molecular events leading to sa191 gain-of-function phenotype . These observations are inconsistent with the UPR being the trigger for ASI-specific dysfunction . The UPR appears to modulate the strength of signaling of TGF-β and insulin/IGF-like pathways , by affecting the balance of the folding environment in the ER and possibly impacting on translation . We suggest that , in context of the endogenous sa191 mutation , activation of the PERK/eIF2α pathway could amplify the initial trigger , which we infer to be the interference with the normal biogenesis of DAF-7/TGF-β by the folding mutant of DAF-28/IGF . However , PERK could also exert its effects through a yet-unknown , ASI- or DAF-7-independent mechanism , since we found that , despite its ability to decrease dauer phenotype , deletion of pek-1 enhanced the mislocalization of the transgenic mCherry::DAF-7 protein in animals with DAF-28 ( R37C ) mutation , and caused its massive intracellular accumulation in the ASI neurons . This was reminiscent of the findings that the PERK-deficient pancreatic β-cells exhibit severe disruption of ER-Golgi anterograde trafficking and disruption of the Golgi complex , while , paradoxically , decrease of PERK gene dosage ameliorated the progression of the Akita mouse expressing the gain-of-function insulin mutant to overt diabetes; the latter was proposed to be due to decrease degradation of the wild type insulin [64] . Our findings highlights the complexity of the UPR effects and underscore the need to further understand the role of UPR signaling under chronic , physiological , stress conditions . The selective targeting of DAF-7/TGF-β protein biogenesis , in particular its mislocalization to the proximal axon , is an intriguing finding . As previously noted , sa191 animals phenotypically resemble animals with deficient daf-7 signaling [17] , and we found that decreased DAF-7 function explains the sa191 gain-of-function dauer phenotype . However , because transcription of daf-7 does not depend on insulin signaling , the mechanism by which DAF-7 activity might be affected in these animals was unclear . Our data suggest the following model for the bystander targeting of DAF-7 protein and for its phenotypic outcome in sa191 animals ( Fig 11 ) . First , misfolded DAF-28 ( R37C ) protein in sa191 animals may titrate a component of the ER/Golgi proteostasis machinery that is required for efficient folding or maturation of the endogenous DAF-7 protein . This could be a chaperone required for productive folding by both DAF-7 and wild-type DAF-28 . Alternatively , DAF-7 may only share such a chaperone with the misfolded and not with wild-type DAF-28 . The latter is more likely , as we did not detect a strong effect of sa191 mutation on the wild-type DAF-28 . Because of the role of the unpaired cysteine , and the knotted arrangement of the disulfide bonds in DAF-7 , a potential candidate for this competition may be an oxidoreductase [65 , 66] . Another example of a potential candidate could be a folding chaperone with restricted client repertoire . For example , mammalian IGF proteins are completely dependent on the selective ER chaperone GRP94 for folding and secretion [16 , 19 , 20] . The resulting defects in the folding or maturation of the DAF-7 protein lead to its striking mislocalization and accumulation in the proximal axon of the ASI neuron . Axonal trafficking defects are common in neurodegeneration , yet , how soluble cargo is selectively sorted to the vesicles destined to axons vs . dendrites is not understood . It is possible that non-native species of DAF-7 , generated in the presence of misfolded DAF-28 ( R37C ) , interact abnormally with the sorting machinery , resulting in mistrafficking . The combined outcome of these defects in DAF-7 folding , processing , and/or trafficking , together with decreased function of the mutant DAF-28 ( R37C ) protein , result in decreased pro-growth signaling , despite the normal perception of the food signal ( Fig 11B ) . Decreased daf-7 activity , in turn , affects transcription of pro-growth insulin/IGF genes [17 , 67] , further reducing the pro-growth signaling . This balance of growth vs . dauer signals may result in activation of the dauer program , but may not be sufficient for the full commitment to dauer , as many sa191 animals that enter the L2d stage quickly resume reproductive growth . We suggest that PERK signaling is necessary for the dauer commitment of sa191 animals because it provides amplification of the signal , perhaps through translational attenuation ( Fig 11B ) . If the defect in DAF-7 protein biogenesis in sa191 animals is mediated by titration of a necessary chaperone by misfolded DAF-28 ( R37C ) , how can overexpression of DAF-7 rescue this defect ? Overexpression of a protein under conditions of chaperone insufficiency would further increase the folding stress in the ER; yet , we observe a strong rescue of the sa191 dauer phenotype by overexpressed DAF-7 ( Fig 2D ) . Our data suggest several explanations . First , daf-7 is thought to be expressed predominantly in the ASI and , in certain conditions , the ASJ and a subset of mechanosensory neurons [68] . However , we find that in embryos and just-hatched L1 larvae , daf-7 is expressed in multiple neurons . If these neurons do not express daf-28 ( sa191 ) , they may be a source of native , functional secreted DAF-7 transgenic protein . Second , we observe expression and secretion of mCherry::DAF-7 protein already in the early stage embryos and throughout subsequent embryonic development , while DAF-28::mCherry is expressed later . Similarly , the ASI-specific gpa-4 promoter in ASI::DAF-7 transgene is known to be active in early embryos [69] . Thus , transgenic mCherry::DAF-7 may be secreted prior to the disruptive effects of the endogenous DAF-28 ( R37C ) on its biogenesis . This interpretation is supported by the maternal rescue of dauer signaling by the transgenic DAF-7 , which suggests that the presence of this protein only in the embryo , without further expression in larval stages , is sufficient to prevent dauer induction . What accounts for the specificity of the toxic effect of the misfolded DAF-28 ( R37C ) protein , which appears to only target a dauer-promoting function in the ASI neuron despite being expressed in many other cells ? The most trivial possibility is that the mutant DAF-28 protein may be expressed at higher levels in the ASI neuron than in other cells in the early larval stages , although this is not supported by transcriptional reporters . Second , the proteostasis component ( s ) required for both DAF-28 ( R37C ) and DAF-7 protein biogenesis may only be limiting in the ASI neuron but present in a sufficient amount to buffer the misfolded DAF-28 mutant in other cells . In this case , the observed lack of generic protein mislocalization and of dysfunction or degeneration of the ASI neuron would also imply that the requirement for this chaperone is not as strong for other proteins expressed in this cell as it is for DAF-7 . An intriguing possibility is that the ASI neuron could , due to a yet unknown intrinsic property , be selectively sensitive to misfolding in the ER . Mammalian Purkinje cells , for example , are selectively sensitive to mutations in several broadly or ubiquitously expressed proteins , which cause ER stress and toxicity in Purkinje cells but not in other neuronal cell types [70–72] . Alternatively , sensitivity of dauer activation to ER stress , rather than selective sensitivity of the ASI neuron , could be responsible for the ASI-specific dauer phenotype , for example due to being tuned to small changes in the rate of secretion of DAF-28 and DAF-7 . Indeed , DAF-28 and DAF-7 cooperate to activate a growth-promoting feed-forward loop , while a decrease in DAF-7 feeds back to attenuate daf-28 transcription ( Fig 11 ) . Several observations , including some that were previously unexplained , support these two possibilities . First , a highly-expressed pdaf-7::GFP transcriptional reporter saIs8 was previously observed to abnormally induce dauer entry for unknown reasons [27] . Examination of its sequence suggests that it may be prone to misfolding since , in addition to the DAF-7 promoter , this reporter contains an N-terminal fragment of the DAF-7 protein , including ER signal sequence and most of its LAP , fused to a GFP moiety . In mammalian TGF-β , the LAP forms extensive hydrophobic contacts with the C-terminal cysteine-knot hormone domain [73] , which is deleted in this reporter . Misfolding of this reporter in the ER of the ASI neuron may explain the observed dauer induction . Second , we find that a DAF-28::GFP fusion protein induces a strong gain-of-function dauer phenotype in daf-28 deficient background , likely due to the oxidative misfolding of the GFP and the accumulation of the misfolded fusion protein in the ER of the ASI ( and ASJ ) neurons . Finally , it was previously reported that crossing DAF-28::GFP transgene into a daf-8 ( m85 ) mutant background unexpectedly resulted in irrecoverable dauer-constitutive phenotype [74] . DAF-8 is a pro-growth R-Smad transcription factor in the DAF-7/TGF-β signaling pathway . The sensitivity of the ASI neuron , and/or of the dauer signaling pathway , to protein misfolding in the ER may explain the dauer phenotype in all three of these examples . Our data suggest that DAF-7/TGF-β protein is a sensitive and selective target of the bystander effect caused by misfolded DAF-28/IGF . It will be very important to understand what makes a particular protein a target for bystander misfolding [75] , and whether different proteins are targeted by distinct misfolded species . Although global disruption of proteostasis and induction of stress responses are common to the toxic effects of misfolded proteins , there are examples of specific bystander targeting . In Drosophila , expression of human insulin bearing the Akita C96Y mutation results in ER stress and cellular dysfunction . While this mutant protein caused a general degeneration phenotype in the eye , its expression in the wing imaginal discs resulted in phenocopies of Notch and crossveinless mutations [76 , 77] . Interestingly , a targeted deletion of the ER thiol oxidase Ero1L specifically in the wing also caused a phenocopy of Notch , by inducing misfolding of Notch protein while not affecting other secretory proteins [78] . Our data suggest that a global vs . targeted bystander mechanism of a given folding mutation may depend on ( 1 ) the nature of the misfolded species it produces , ( 2 ) the identity , client repertoire , and availability of chaperones that bind these misfolded species , and , importantly , ( 3 ) the presence in the same cell of susceptible bystander targets controlling sensitive cellular or organismal processes . Identifying these potential bystander targets and the restricted chaperones could be instrumental in understanding—and protecting against—the specific toxic phenotypes in protein misfolding diseases .
Standard methods were used for worm culture and genetics [79] . Animals were synchronized by picking gastrula-stage embryos , or by hypochlorite treatment for FACS analysis . The following strains were obtained from the Caenorhabditis Genetics Center ( CGC ) : JT191 ( daf-28 ( sa191 ) V ) , TY3862 ( daf-7 ( e1372 ) III;cuIs5[pmyo-2C::GFP + pRF4 ( rol-6 ( su1006 ) ) ] ) , VC1099 ( hsp-4 ( gk514 ) II ) , RB545 ( pek-1 ( ok275 ) X ) , SJ4005 ( zcIs4[phsp-4::GFP]V ) , PD4792 ( mIs11[pmyo-2::GFP + ppes-10::GFP + gut-promoter::GFP]IV ) , HT2099 ( unc-119 ( ed3 ) III;wwEx85[pdaf-28::GFP] ) , FK181 ( ksIs2[pdaf-7::GFP + rol-6 ( su1006 ) ] ) , NM440 ( unc-104 ( e1265 ) II;jsIs1[pSB120 ( snb-1::GFP ) ;pRF4 ( rol-6 ( su1006 ) ) ] ) . N2AM ( wild-type ) is a subclone of N2Bristol from Morimoto Lab . DAF-28::GFP strain ( svIs69 array ) was from Naredi Lab ( U . of Gothenburg ) . Strain 2308 ( daf-28 ( tm2308 ) V ) was from the National BioResource Project ( Japan ) . ZM7963 ( hpDf761II;daf-28 ( tm2308 ) V ) and XL153 ( ntIs27[psra-6::ChR2::YFP , punc-122::dsRed] ) were a gift from Fang-Yen Lab ( UPenn ) . Crosses were conducted using phenotypic or fluorescent chromosomal markers ( http://www . wormbuilder . org/ ) . Strains were confirmed by PCR and restriction digest or sequencing . Transgenes were injected by Knudra Transgenics ( USA ) as 20ng/μL plasmid DNA and 80ng/μL sonicated salmon sperm DNA . All PCR products were verified by sequencing . Restriction sites were introduced into PCR primers . A 2 . 6 kb NaeI/XbaI fragment containing the daf-7 promoter region and first 23 residues of the DAF-7 protein , and a 1278bp SacI/EagI fragment from amino acid 23 to stop codon were amplified from N2 genomic DNA . XbaI/SacI worm mCherry minus the stop codon was amplified from pCFJ104 ( Addgene #19328 ) . The three fragments were assembled to exchange the pmyo-3::mCherry in pCFJ104 . The 703bp PstI/XbaI fragment containing coding region of daf-28 , and the 2 . 0 kb SphI/PstI daf-28 promoter region [21] were amplified from N2 genomic DNA . The 934bp XbaI/SacI worm mCherry and the 873bp SacI/PvuII unc-54 3’UTR fragments were amplified from pCFJ104 . Fragments were assembled in pMCS5 plasmid . The coding region of daf-28 was amplified from daf-28 ( sa191 ) genomic DNA as PstI/XbaI fragment and exchanged with the wild-type coding region of drxEx21 . Animals were grown on fresh plates seeded with OP50 E . coli at 20°C under non-crowded/non-contaminated conditions for at least 2 generations prior to embryo picking , to avoid effects on dauer entry [80] . 20–40 YA animals were allowed to lay embryos for 24 hours at 20°C . For transgenic rescue assays , only transgenic ( parent ) animals were picked , so that the non-transgenic animals among their progeny were siblings to the transgenic ones . From these , 100–200 gastrula-stage embryos were picked onto new plates and allowed to develop for 65–66 hours at 20°C . Animals with embryos present in uteri were scored as reproductive adults; YA or late L4 stages ( based on gonad development ) were scored as mildly delayed; and early L4 or earlier stages ( mainly L2d and/or dauers ) as severely delayed . Dauer larvae were radially constricted , lacked pharyngeal pumping , and had visibly constricted pharynxes [80] . L2d larvae were radially constricted to a lesser extent than dauers , had a uniformly dark intestine ( Fig 4F and 4H ) [80] and exhibited slow pharyngeal pumping . All developmental assays were repeated at least three times , raw data are in the Supplemental Data Table . For SMAD-reporter/development correlation , larvae were separated at ~35 hours post-gastrula into ‘bright’ and ‘dim’ populations based on GFP fluorescence viewed through stereo microscope , and allowed to develop for an additional 30 hours . Because of the separation , animals experienced drop in population density resulting in slight increase in reproductive development in sa191 . GFP intensity was measured in L1/early L2 animals using BioSorter ( Union Biometrica , USA ) for three strains carrying the cuIs5 transgene , and non-transgenic N2AM . Data analysis was performed by FlowJo software . An initial gate was set as the measurement of length ( time of flight ) versus absorbance ( extinction ) to distinguish larvae from debris . All animals with GFP intensity values higher than maximum detected in the non-transgenic N2AM strain were included in analysis . 50 or 100 L2 larvae in 30μL of water were flash-frozen in liquid nitrogen . 15μL of reducing or non-reducing sample buffer was added and samples incubated at 85–95°C for 10 minutes . Protein amounts were verified by Ponceau stain of membranes . Anti-RFP ( 5F8 , ChromoTek , Germany ) was used to detect DAF-28 ( R37C ) ::mCherry . JT191 and TU3401 strains were used as negative and positive controls . For the blot in S3 Fig , full plates were collected and frozen in aliquots . Worms were lysed by mechanical disruption , as described in [8] , treated with reducing/non-reducing sample buffer , and further processed as above . 10 L4 larvae ( total 150 animals/strain ) were picked into 25μL of M9 solution on a glass slide and acclimated for 1 minute . One minute movies were taken and analyzed using wrMTrck ImageJ plug-in ( Dr . Jesper Pedersen , http://www . phage . dk/plugins/wrmtrck . html ) . Pharyngeal GFP marker was introduced into daf-28 ( sa191 ) animals from PD4792 strain by crossing . PD4792 was used as control . 5 L4 larvae per plate ( total 30 animals/strain ) were plated and allowed to lay progeny at 20°C . Animals were transferred to new plates every 24-hours until egg-laying ceased . Number of progeny was quantified by counting fluorescent pharynxes using ImageJ . 50 L4 larvae of each strain were plated on seeded plates and incubated for 24 hours at 20°C . 30 adult animals were immobilized on glass slides using 20mM sodium azide , and imaged at 50X magnification on Leica M205FA . Microscale was included in the images as a ruler . Body length was measured from the tip of the nose to the tip of the tail with ImageJ . Confocal: animals were mounted on 2% agar pads with azide and imaged with Zeiss LSM700 microscope at Cell Imaging Center , Drexel University , using 1 . 4NA 63x oil objective . 12 bit confocal stacks were reconstructed in ImageJ as 3D projections , and where indicated overlaid on single plane DIC images . Stereo: animals were mounted as above , or immobilized by chilling on plates . Imaging was performed with Leica M205FA microscope and Hamamatsu Orca R2 camera , keeping magnification and intensity of fluorescent sources ( Chroma PhotoFluor 2 ) constant within experiment . All Chi-square , ANOVA , and t-test analyses were performed using Prism software ( GraphPad , USA ) . ANOVA was followed by multiple comparisons post-test , as indicated in Figure legend . α and significance levels are also indicated . For developmental assays , at least three replicates were used , the raw data including the number of animals per replicate are in Supplemental Data Table . | Correct protein folding and localization ensures cellular health . Dedicated proteostasis machinery assists in protein folding and protects against misfolding . Yet , folding mutations cause many conformational diseases , including neurodegenerative diseases and certain types of diabetes and cancer . Misfolded disease-related proteins interfere with proteostasis machinery , causing global misfolding in the cell . How this global mechanism leads to the specific phenotypes in different conformational diseases is unknown . Moreover , mutant misfolded proteins that only damage specific cell-types in disease often lose this cell-selectivity when overexpressed in genetic models . Here we use an endogenous folding mutation in a C . elegans secreted IGF-like protein , DAF-28 , that causes dysfunction in one neuron and a specific developmental phenotype , despite expression in many cells . We find that misfolding of mutant DAF-28 causes mislocalization and defective function of another , wild-type growth factor that is expressed in the affected neuron , the TGF-β protein DAF-7 . Decrease in DAF-7 function explains the observed developmental phenotype . This targeting of the bystander protein DAF-7 by the misfolded mutant DAF-28 is specific and is not caused by the global stress . Our data suggest that rather than global effects , it is the selective targeting of specific susceptible bystander proteins that defines the specific phenotypes in conformational diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"protein",
"transport",
"cell",
"processes",
"neuroscience",
"developmental",
"biology",
"protein",
"expression",
"chaperone",
"proteins",
"nerve",
"fibers",
"molecular",
"biology",
"techniques",
"embryos",
"neuronal",
"dendrites",
"research",
"and",
"analysis",
"methods",
"embryology",
"animal",
"cells",
"axons",
"proteins",
"molecular",
"biology",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"gene",
"expression",
"and",
"vector",
"techniques",
"biochemistry",
"protein",
"secretion",
"cellular",
"neuroscience",
"cell",
"biology",
"phenotypes",
"neurons",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types"
] | 2016 | A Bystander Mechanism Explains the Specific Phenotype of a Broadly Expressed Misfolded Protein |
Spatial attention is most often investigated in the visual modality through measurement of eye movements , with primates , including humans , a widely-studied model . Its study in laboratory rodents , such as mice and rats , requires different techniques , owing to the lack of a visual fovea and the particular ethological relevance of orienting movements of the snout and the whiskers in these animals . In recent years , several reliable relationships have been observed between environmental and behavioural variables and movements of the whiskers , but the function of these responses , as well as how they integrate , remains unclear . Here , we propose a unifying abstract model of whisker movement control that has as its key variable the region of space that is the animal's current focus of attention , and demonstrate , using computer-simulated behavioral experiments , that the model is consistent with a broad range of experimental observations . A core hypothesis is that the rat explicitly decodes the location in space of whisker contacts and that this representation is used to regulate whisker drive signals . This proposition stands in contrast to earlier proposals that the modulation of whisker movement during exploration is mediated primarily by reflex loops . We go on to argue that the superior colliculus is a candidate neural substrate for the siting of a head-centred map guiding whisker movement , in analogy to current models of visual attention . The proposed model has the potential to offer a more complete understanding of whisker control as well as to highlight the potential of the rodent and its whiskers as a tool for the study of mammalian attention .
A succinct summary of contemporary models of primate visual spatial attention is that exogenous signals ( those arising from external stimuli ) from multiple sensory modalities and endogenous signals ( those arising from internal processes ) compete and combine to produce a spatial map of salience from which a single region of immediate spatial attention is chosen [1]–[3] . In the case of overt attention , this location is ‘foveated’ by the rapid re-positioning of the eyes with movements of the head and body following as necessary [4] . If multiple salient locations are present , they are visited sequentially . The degree and nature of integration between overt and covert attention ( that expressed only internally ) , exogenous and endogenous influences , and inputs from different modalities are all matters of debate , as is a definition of attention itself [3]–[8] . One aspect , however , is uncontroversial: that overt attention is expressed by rapid orienting movements that centre the foveal region of the eye on the attentional target . Many small mammals , including laboratory rats and mice , possess in addition to vision a complementary and well-characterised sensory system driven by tactile stimulation of prominent arrays of sensitive whiskers , particularly those located around the snout [9] . Here , we will consider whether the movements of these whiskers might also represent an expression of overt attention , revealing areas of proximal space that are of high salience to the animal . Potentially , such a model would be useful to experimentalists interested in mammalian attentional processes and their neural substrates , not least owing to the growing ease with which observations of whisker movement and position can now be made and analysed in these animals even when they are freely behaving . Whisker movements have been most studied in animals that express ‘whisking’ , a periodic protraction and retraction of the whiskers , typically occurring at several cycles per second ( each cycle being termed a ‘whisk’ ) and in bouts lasting several seconds , with a close coupling of the oscillatory motions of the left and right whisker fields . Most data have been gathered using rats [10]–[12] , though analyses are also available for mice , shrews , opossums and hamsters [13]–[16] . Many studies have now described significant departures from spectrally pure , bilaterally symmetric and synchronous whisking , revealing that both spatial and temporal parameters of whisker movements are under active control and can change rapidly in response to environmental conditions as well as to the motivations of the animal [12] , [17]–[24] . Furthermore , small changes in whisker position can lead to large changes in sensory signals [25]–[27] . Thus , the proposition that an understanding of whisker movement is a pre-requisite to an understanding of whisker sensory signals has become a key focus of research [28]–[33] . This shift has been facilitated by the increasing availability of experimental tools for measurement of whisker movements [18]–[20] , [34] as well as for automated analysis of large high-speed video datasets [34]–[37] . Not only is whisker movement of interest to the researcher who wishes to understand whisker sensory processing ( and sensory systems in general ) , but these movements may also provide data about the internal state of the animal [32] , [38] . Since whisker motion can be modulated when the head is stationary some useful measures are available also in the head-restrained condition [17] , [39] . The modulation of whisker motion parameters under different conditions has been previously explained as arising from reflex responses ( e . g . [19] , [20] , [40] ) or from task-specific sensing strategies ( e . g . [12] , [41] ) . Furthermore , computational models developed by the current authors and evaluated in biomimetic whiskered robots [42]–[45] have demonstrated that a mix of positive and negative feedbacks , such as could plausibly be mediated by brainstem loops [46] , can produce some of the observed whisker modulations . However , a simple reflex model cannot explain all modulations—for instance , those driven by conditioning [47] , [48] or anticipation [17] , [18] , [20] , [23] , suggesting the involvement of higher centres in motion modulation [49] . Below , therefore , we motivate and develop a new model of whisker movement control that has as its key variable the region of spatial attention . The explicit representation of this region , as a tactile ‘salience map’ , represents a significant departure from current theories and our own earlier models of whisker control , and provides a theoretical bridge to the current paradigm for understanding visual attention in primates , in which salience maps are a core concept [6] . We go on to reprise three behavioural experiments in simulation using the new model and report comparable results to those obtained using animals [19] , [20] , [23] using analyses closely replicating those employed in the original studies . In our discussion , we summarize the key features of the model and of our results , compare it with competing models and discuss its limitations , suggest experiments that might invalidate it , and discuss its likely neural substrate . In addition , we highlight two architectural features common to any model of this form . Thus , this report both represents a step forward in our understanding of active sensing in rodents and highlights the potential of the rodent and its whiskers as a tool for the study of mammalian attention .
We have previously shown that whisker motion in the horizontal plane can be well summarized by just two variables for each side of the snout [23]: mean ( across whiskers ) angular position ( henceforth , ‘mean protraction angle’ ) and the angular position difference between caudal and rostral whiskers ( henceforth , ‘angular spread’ ) . Several distinct observations of correlations between these and other behavioural variables have been reported . An early result in rat , that whisker protraction angles increase as the animal approaches the location of an anticipated contact [11] , [17] , [18] , [20] , has been recently matched and quantified in mouse [24] . Two further observations first made in rat have also been extended to mouse and opossum [15] . The first , which we term Head-Turning Asymmetry ( HTA ) , is that mean protraction angles are adjusted to be more caudal/rostral on the side of the animal into/away from a future turn of the head [15] , [19] . The second , Contact-Induced Asymmetry ( CIA ) , is the observation that mean protraction angles are adjusted to be more caudal/rostral on the side of the animal near/away from a nearby object [15] , [20] ( see also Figure 1 ) . A further observation is the Rapid Cessation of Protraction ( RCP ) that interrupts the protraction phase of a whisk movement when whiskers on one side of the animal make contact with an obstruction [20] , [23] . We use the term Spread Reduction ( SR ) for the observation that the angular spread on each side of the snout is reduced during contact with objects in the vertical plane versus non-contacting whisks [23] . Finally , recent work in our lab has shown that animals engaged in rapid ( m/s ) goal-directed locomotion employ tonic protraction ( increased mean protraction angles and a reduced amplitude of periodic whisker movement , [53] ) . To account for the observation of HTA , Towal and colleagues proposed that the whiskers search in the space into which the head will shortly be moved , perhaps partly to avoid collisions [19] . To account for contact-driven observations ( RCP , CIA , SR ) we proposed the general control strategy of ‘Minimal Impingement , Maximal Contact’ ( MIMC , [20] , [23] , [42] ) whereby whiskers are controlled so as to maximize the number of contacts but avoid excessive whisker bending within each contact ( minimizing impingement ) . In addition , we recently hypothesized that tonic protraction during rapid forward locomotion reflects a strategy for collision avoidance whereby the ‘look-ahead’ distance of the animal is maximized [53] . Here , we propose that a single mechanism may be sufficient to explain all of these observations , including responses to anticipated contact . One clue to the nature of this mechanism is the observation that unilateral contact often elicits head-turning towards the contact point suggesting that CIA ( Contact-Induced Asymmetry ) and HTA ( Head-Turning Assymetry ) , at least , may be related . The simplest possibility is that they are examples of the same response , to head movement or whisker-contact , expressed under different circumstances , but this is excluded by the following two cases . First , CIA is expressed regularly even where head-turning is precluded or absent , such as when the animal is following a wall ( [20]; Video S1 , S2 , S3 all show examples of CIA in the absence of head-turning ) . Second , and conversely , HTA is expressed in the absence of any contact [19] . Nonetheless , these observations may be related through a hidden variable . In the case of HTA , whisker asymmetry precedes head-turning; therefore , unless whisker asymmetry drives head-turning directly—which seems unlikely—a hidden variable is implied . Seeking this hidden variable , we ask: Why does unilateral contact often elicit head-turning ? The intuitive answer is that contact will often elicit attention , and attention will typically elicit orienting . We hypothesize , accordingly , that the hidden variable relating these observations is the ‘attended region’—that region of the external world which is currently the subject of the animal's attention—which can be affected by both tactile signals and other influences . According to this hypothesis , then , the mechanism underlying CIA is that laterally-biased contact generates laterally-biased attention which , in turn , drives asymmetric whisking , whilst that underlying HTA is that laterally-biased attention ( however generated ) drives asymmetric whisking and also head-turning . This model , summarised in Figure 2 , is also consistent with observations of increased whisker protraction when contact ahead of the animal is anticipated and during goal-directed locomotion , both of which are conditions in which we might expect the attention of the animal to be focussed to the fore . Furthermore , the model explains why CIA is not observed in response to contacts in cases where the animal does not subsequently indicate attentiveness by orienting towards the contacted object [20] . Thus , our central hypothesis is that a transformation from the attended region to whisker protraction angles is the primary driver of long-term modulations of whisker movement ( that is , on timescales longer than that of a single whisk cycle ) . The second behavioural response seen in Figure 1 , the orienting of the snout tip , also intuitively appears to be an expression of overt attention since this movement serves to reposition a generalised sensory ‘fovea’—a body region in which are located the microvibrissae , lips , teeth , tongue , and nose , [9] , [54] , [55]—as well as an important actuator for small mammals: the jaws . We have , therefore , previously argued that movement of the head driven by switches in spatial attention represents a very significant component of the exploratory behaviour of small mammals ( [42] , [44] , [56] , [57]; see also [55] ) . Therefore , in analogy with the literature on the behaviour of visual animals , we refer to discrete head movements delineated by attention switches as ‘foveations’ . The current model ties together these two modes of expression of attention , using a single representation of the attended region—in the form of a ‘salience map’—to drive movements of both the whiskers and the head ( and , consequently , of the body ) . The remainder of this section details our implementation of this model , starting with an overview , and continuing with sub-sections detailing each computation , the headings of which correspond to the labels on the boxes in Figure 3 . Below , we use computer simulation of our attentional model to reprise three earlier behavioural experiments . In each case , we position obstacles in the simulated environment , allow the model to control the whiskers and head for some period , and make the following measurements . First , we measure the location of the tip of the snout over time , , and the head bearing ( that is , the angle of the head midline that runs from the neck joint to ) . Second , we record the measured protraction angle of the th whisker , , according to the methodology we have used previously in the behavioural laboratory [23] . That is , we locate the base of the whisker , and a point two thirds of the way along its shaft , and derive the angle between the vector connecting these points and the head midline . Similar strategies were used in most of the other behavioural work with which we make comparison [15] , [19] . We go on to obtain the instantaneous mean protraction angle of all the whiskers on each side of the snout , and , by simple arithmetic mean across the whiskers , again following precedent from analyses of behavioural data [15] , [19] , [23] . As a measure of whisker protraction angle that is unaffected by bending of the whiskers against obstacles , we also record the protraction angle at the base of the th whisker , , and compute the corresponding bilateral mean protraction angles , and . Presented examples of animal behaviour ( stills and videos ) were drawn from our archive of behavioural data to illustrate the text; recording methodology was described previously [20] , [23] . Bilateral mean protraction angle presented in Figure 1 was recovered from the video data using the BIOTACT Whisker Tracking Tool ( bwtt . sourceforge . net ) and the ViSA tracking algorithm suite [37] .
The results above can be summarised as follows . During exploration in free space , the simulation expresses HTA with a coefficient of linearity between those reported in two behavioural studies . During exploration near walls , the model expresses CIA with a strength comparable to that reported in two behavioural studies . During approach to a wall , the model expresses SR ( some reduction in first contacting whisk , substantially more in second ) with comparable strength to that reported in a behavioural study . To assess the sensitivity of these results to the ‘Reference’ parameter choices listed in Table 1 , we realised the three experiments multiple additional times , making adjustments to one or a few parameters in each case , and assessing the results for the qualitative findings given above . We did not test adjustments to the parameters marked in Table 1 since these are fairly well-defined by previous reports ( is a temporal scale parameter which defines only the overall rate of behaviour; the other three are anatomical parameters ) . The effect of adjustment of the remaining parameters is reported below . To begin with , we tried flipping the array along the left/right dimension after it had been built . The asymmetries of HTA and CIA had their senses reversed , as expected , whilst the SR result was somewhat weakened , also as expected . Next , we checked that integration error was not affecting our results by using higher spatial ( mm ) and temporal ( s ) resolution; the CIA result appeared a little strengthened , but otherwise there was no effect . Similarly , most other adjustments to the parameters ( listed in Table 1 , column ‘Adjusted’ ) had only minor effects and did not change the qualitative results; those that did impact the results are now listed . Increasing all three width parameters ( , , ) had little impact; decreasing them somewhat weakened the CIA result ( though the main lateral bias remained robust ) . Raising had little effect , but reducing it eliminated plausible gross behaviour in the CIA experiment so that the result could not be measured . Decreasing/increasing the excitation noise gain ( ) strengthened/weakened the results , as expected ( at the high noise level , the SR result was qualitatively degraded ) . Decreasing had little effect; increasing it had little effect on HTA or SR , and only slightly weakened the CIA result , apparently owing to changes in gross behaviour rather than any effect on whisker movement per se . Adjusting the nominal protraction angles up or down affected the scaling just of the SR result , but did not change it qualitatively . Increasing the protraction duty cycle , , to 80% had little effect; reducing it to 50% introduced some noise into the CIA result ( though the main lateral bias remained robust ) . Adjusting the overall modulation strength , , had the strongest effect of any of the tested adjustments , unsurprisingly—however , whilst the strength of all three results was very directly affected , all the results were qualitatively unchanged for all non-zero tested values . As expected , with a modulation strength of zero , both HTA and CIA plots are flat , whilst the SR plot shows a small reduction in spread owing to the measurement of physical whisker deformation .
Attention is a prototypical example of what is generally considered to be a cognitive process . That is , compared to the simpler notion of a reflex arc , attention requires mechanisms that can implement bottom-up filtering of stimuli , working memory for recent events , competitive selection , and top-down modulation ( e . g . by motivational systems ) ( see , e . g . [66] for a review of the nature of attentional processing ) . Components that implement each of these computations are required to create even a relatively simple model of spatial attention as demonstrated by the model system we describe above . Whilst it is reasonable to seek simpler mechanistic explanations of a phenomenon such as the sensory modulation of whisker movement , there is evidence in a wide-range of domains—time [67]–[69] , number [67] , [70] , reward [71] , [72] , decision-making [73] , [74] , space [75]–[78] , and working and long-term memory [79] , [80]—that rodents process information in a manner that reflects the operation of cognitive mechanisms sometimes approaching , in terms of their sophistication , those identified in primates . We propose that in the case of spatial attention , rat cognition again shares interesting similarities to primate cognition that have been largely overlooked ( though , see [81] , [82] ) . Specifically , that models of visual attention using salience maps , that have proved effective in explaining primate eye movement data , could have a useful analogue in the attentional mechanisms underlying rat vibrissal touch . Whilst not a minimal model in terms of the computations involved , we propose that our attentional hypothesis for rodent whisking modulation is parsimonious in the sense of being explanatorily powerful . That is , the model accounts for multiple observed phenomena ( HTA , CIA , SR ) , and , moreover , does so in a way that is robust to parameter change ( see Sensitivity Analysis , above ) . The model should also naturally reproduce phenomena described in the literature that cannot , even in principle , be explained by reflex mechanisms . Specifically , anticipatory ‘reaching’ , in the form of increased whisker protraction , has now been reported in a range of experimental paradigms: Sachdev et al . ( 2003 ) [17] reported unilateral reaching in anticipation of contact with a sensor that triggered a reward; Berg & Kleinfeld ( 2003 ) [18] reported reaching ( alongside changes in temporal parameters ) when animals were challenged to contact a discriminandum on the other side of a gap; our own observations of a freely-exploring condition also suggest reaching [20] ( see Figure 7B ) as does evidence of rats increasing whisker protraction during running [53]; finally , SR also appears to be anticipatory at least in part [23] . All of these experiments used rats , but reaching has also recently been observed in mouse by Voigts et al . ( 2013 ) [24] , who highlighted that “The precision in amplitude modulation is not due to current sensory input” but rather relies on historical sensory information ( i . e . on working memory ) . The validity of the attentional explanation of whisking modulation can be further tested in the behavioural laboratory . One key prediction is that non-attended objects will not elicit whisker modulation , as we have previously observed informally in a handful of trials but have not yet quantified [20] . A possible preparation to test this prediction might be , for instance , a motivated animal seeking particular objects preferentially over others positioned nearby . A second key prediction is that whisker movement is modulated by spatial attention , however generated . A preparation for testing this might be an examination of the whisker movements of a head-fixed animal , with spatial attention manipulated by olfactory , auditory , or visual cues rather than by tactile stimuli . If , for instance , a whiff of an attractive odor from a specific direction elicited whisker movement toward that direction this would be strong evidence in favour of an attentional model of whisking modulation , in this case showing cross-modal transfer of target salience . Our model does not include modulation of whisk frequency , nor changes in whisker movement at very short time-scales . As a result , two notable observations not accounted for by the model are Rapid Cessation of Protraction ( RCP ) [20] , [23] and the ‘touch-induced pump’ ( TIP ) [40] both of which occur within the time-scale of a single whisk . As previously discussed [20] , [83] , these observations may reflect the operation of a rapid negative feedback loop , though alternative plausible models for RCP and TIP include ( i ) that they represent contact-driven changes in the timing of an underlying motor pattern and ( ii ) that they follow from rapid switches in spatial attention through the attentional mechanism proposed here ( given the rapidity of responses in midbrain to whisker contact , [84] ) . Further experiments will be required to establish whether brainstem mechanisms alone are sufficient to elicit these phenomena . The model presented is abstract in form and also in substrate , however , neuroscientific evidence does point towards some likely substrates for different aspects of these attentional computations in the rat brain . Most clearly , the superior colliculus ( SC ) would be a very plausible location for a spatial attention map to be sited . SC has the right inputs from somatosensory centres—rapid bottom-up inputs arrive directly from trigeminal sensory complex , whilst top-down inputs from somatosensory cortex are also present [84]–[86]—and the sensory organization is topographic [87]–[89] . More broadly , rodent SC receives inputs also from visual and auditory centres [90] , reflecting that SC is an important centre for the integration of multi-sensory—specifically , spatial—information [91] . It also has the right outputs: it contains topographic motor maps for both orienting-like head movements [92] and apparently modulatory ( non-periodic ) whisker movements [93] , [94] and has direct efferents to facial nucleus , the motor nucleus associated with the whisker musculature [85] , [95] . Salience maps have been identified in SC [1] and it has been strongly implicated in the mediation of visual attention processing [96]–[99] . The proposal that SC plays a key role in rat orienting to whisker stimuli is consistent with its importance for rat prey capture [100] . Interestingly , adult-like HTA , CIA and SR emerge in the post-natal animal during overlapping periods in P12–16 [65] , corresponding approximately to the time when SC is reported to be maturing anatomically ( around the beginning of the third post-natal week , [89] , [101] ) . Aside from colliculus , other centres likely to be involved in attention management and/or whisker movement include motor cortex and the basal ganglia . Stimulation of vibrissal motor cortex ( vMCx ) can evoke whisking-like movements of the whiskers , and the parameters of stimulation affect the parameters of whisking [102]–[104] . In addition , motor cortex ablation significantly disrupts whisking parameters , particularly contralaterally [105] . These data suggest that vMCx is involved in initiating and modulating whisking even though whisking itself appears to rely on a CPG [32] , [106] , [107] . Activity recorded in vMCx during natural whisking reflects whisking onset as well as variations in amplitude and set-point , consistent with this hypothesis [108]–[110] . Interestingly , motor area M2 in rat has been analogised to the primate Frontal Eye Fields ( FEF ) [111] , a key structure involved in primate oculomotor control and critical in relaying signals from frontal cortex related to voluntary control of visual attention [112] . In addition to projecting to the SC , the FEF , in primates , also project directly to the brainstem saccadic generator so that a primate with a SC lesion is still able to generate saccades . The M2 area in rat likewise has strong reciprocal connections with prefrontal cortex [113] , projections to SC [114] , and direct brainstem projections to areas involved in orienting [115] . Unilateral lesions in this area have been found to produce contralateral neglect in both primates and rats [111] . The basal ganglia ( BG ) , in both rats and primates , are well-situated to gate switches of attention . SC , whisker somatosensory cortex S1 , and whisker motor cortex , all project to similar regions of the dorsolateral striatum ( DLS ) , the input region of the BG [116] . In the case of SC , the projection is via the thalamic intralaminar nuclei [117] . DLS then has an inhibitory projection to BG output structures including the substantia nigra pars reticulata which , in turn , tonically inhibits SC and , via the thalamus , areas of sensory and motor cortex related to the vibrissae , thus completing a double-disinhibitory loop that seems configured to select target representations that are of high salience to the animal [118] , [119] . In primates , the role of BG in gating saccadic eye-movements to salient targets has been described in detail by Hikosaka et al . ( 2000 ) [120] , and it seems plausible that the BG will play a similar role for whisker-guided orienting movement in rats . The model has two interesting architectural features distinct to this system . First , whisker-centric data are mapped into a head-centric representation of space , implying dynamic routing of sensory data , in analogy to remappings of auditory and somatosensory data in other animals [91] . However , owing to the rhythmic exploration of space by the whiskers ( along with inertial or contact-driven bending ) , the central representation of the periphery is constantly and rapidly on the move in such a model . In SC , rats have an approximately retino-centric topography in the superficial layers , whilst vibrissal data is represented in the deeper layers in spatial register with the overlying visual maps [87] , [93] . At the same time , regions sensitive to stimulation of individual whiskers are large and overlapping under anaesthesia [86] , [87] , particularly in the rostral-caudal dimension , consistent with the large area of the visual field swept by individual whiskers as they move back and forth [50] . Whisker-sensitive cells in primary somatosensory cortex have been reported both to respond most strongly at particular whisker movement phases [121] and to encode whisker bending direction [122] , and primary afferent cells that encode whisker phase have also been identified [27] . Thus , this highly dynamic model is consistent with existing data , whilst cells such as those identified could constitute part of a substrate for remapping , as has been previously discussed [27] , [121] , [122] . Second , whilst visual overt attention is primarily expressed through the azimuth and elevation angles of the eye [96] , our model of tactile overt attention hinges upon the radial dimension since the generalised sensory fovea must be brought to an object rather than just pointed at it [9] . Accordingly , the current study could not have been performed without a representation of the radial dimension . In the current study , we did not represent the vertical dimension ( primarily because behavioural data are lacking ) but we routinely find it necessary to use three-dimensional representations of space as the substrate for spatial orienting in our work with robots ( reviewed in [44] ) . The current proposal can be extended to three dimensions if a three-dimensional representation of the attended region is assumed , but whether extension in this way would respect the biological organisation remains an open and important question . In summary , then , our findings support the general hypothesis that there exists in the rat a system somewhat homologous to the visual orienting system known from primate studies [96] , with the primary outputs being re-location of a generalised sensory fovea around the mouth , supported by body movements as required [92] , and adjustment of the protraction angles of the whiskers , perhaps to favour a ‘Minimal Impingement , Maximal Contact’-like control aim . Within this system , superior colliculus may well play a key role , along with areas of cortex and the basal ganglia [111] , [123] . This system probably forms only part of a larger system that generates whisker movements but most or all non-periodic components of motion may be mediated therein . Thus , this sensorimotor model has the potential to substantially improve our understanding of the modulations of periodic whisker movements that are observed in behaving animals . As highlighted recently by Schwarz et al . ( 2010 ) [124] , a particular disadvantage of the head-fixed rat preparation is that the behavioural repertoire of rodents includes many whole-body movements , whisker movements being an exception . In contrast to widely-studied rodent attentional measurement paradigms ( such as the 5-choice serial reaction time task , [125] ) , whisker movements could reveal attention on relatively short timescales , in considerable spatial detail , optionally in head-fixed preparations , with measurement remaining highly automatable . Thus , if whisker movements can be confirmed to reveal the region of spatial attention , their observation might provide a novel and practical tool for its investigation in small mammals . | The management of attention is central to animal behaviour and a central theme of study in both neuroscience and psychology . Attention is usually studied in the visual system ( most often using cats or primates ) owing to the ease of generating controlled visual stimuli and of measuring its expression through eye movement . In this study , we develop a model of the expression of attention in another sensory modality , that served by the tactile whiskers of small mammals ( such as rats and mice ) . This sensory system has long been a popular model in neuroscience and is well characterised . It has become recognised in recent years that the modulations of whisker movements prevalent in the behaving animal represent “active sensing” ( in the sense of moving the sensors to optimise sensing performance ) , yet a unified understanding of these modulations is still lacking . Our model proposes just such a unified understanding , suggesting that whisker movement modulations can be understood as an overt expression of the animal's changing focus of attention . This proposal , therefore , offers to provide both an enhanced understanding of the whisker sensory system and an insight into the management of attention in these animals . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Whisker Movements Reveal Spatial Attention: A Unified Computational Model of Active Sensing Control in the Rat |
Neisseria adhesin A ( NadA ) is present on the meningococcal surface and contributes to adhesion to and invasion of human cells . NadA is also one of three recombinant antigens in the recently-approved Bexsero vaccine , which protects against serogroup B meningococcus . The amount of NadA on the bacterial surface is of direct relevance in the constant battle of host-pathogen interactions: it influences the ability of the pathogen to engage human cell surface-exposed receptors and , conversely , the bacterial susceptibility to the antibody-mediated immune response . It is therefore important to understand the mechanisms which regulate nadA expression levels , which are predominantly controlled by the transcriptional regulator NadR ( Neisseria adhesin A Regulator ) both in vitro and in vivo . NadR binds the nadA promoter and represses gene transcription . In the presence of 4-hydroxyphenylacetate ( 4-HPA ) , a catabolite present in human saliva both under physiological conditions and during bacterial infection , the binding of NadR to the nadA promoter is attenuated and nadA expression is induced . NadR also mediates ligand-dependent regulation of many other meningococcal genes , for example the highly-conserved multiple adhesin family ( maf ) genes , which encode proteins emerging with important roles in host-pathogen interactions , immune evasion and niche adaptation . To gain insights into the regulation of NadR mediated by 4-HPA , we combined structural , biochemical , and mutagenesis studies . In particular , two new crystal structures of ligand-free and ligand-bound NadR revealed ( i ) the molecular basis of ‘conformational selection’ by which a single molecule of 4-HPA binds and stabilizes dimeric NadR in a conformation unsuitable for DNA-binding , ( ii ) molecular explanations for the binding specificities of different hydroxyphenylacetate ligands , including 3Cl , 4-HPA which is produced during inflammation , ( iii ) the presence of a leucine residue essential for dimerization and conserved in many MarR family proteins , and ( iv ) four residues ( His7 , Ser9 , Asn11 and Phe25 ) , which are involved in binding 4-HPA , and were confirmed in vitro to have key roles in the regulatory mechanism in bacteria . Overall , this study deepens our molecular understanding of the sophisticated regulatory mechanisms of the expression of nadA and other genes governed by NadR , dependent on interactions with niche-specific signal molecules that may play important roles during meningococcal pathogenesis .
The ‘Reverse Vaccinology’ approach was pioneered to identify antigens for a protein-based vaccine against serogroup B Neisseria meningitidis ( MenB ) , a human pathogen causing potentially-fatal sepsis and invasive meningococcal disease [1] . Indeed , Reverse Vaccinology identified Neisseria adhesin A ( NadA ) , a surface-exposed protein involved in epithelial cell invasion and found in ~30% of clinical isolates [2–4] . Recently , we reported the crystal structure of NadA , providing insights into its biological and immunological functions [5] . Recombinant NadA elicits a strong bactericidal immune response and is therefore included in the Bexsero vaccine that protects against MenB and which was recently approved in over 35 countries worldwide [6] . Previous studies revealed that nadA expression levels are mainly regulated by the Neisseria adhesin A Regulator ( NadR ) [7] . Although additional factors influence nadA expression , we focused on its regulation by NadR , the major mediator of NadA phase variable expression [8 , 9] . Studies of NadR also have broader implications , since a genome-wide analysis of MenB wild-type and nadR knock-out strains revealed that NadR influences the regulation of > 30 genes , including maf genes , from the multiple adhesin family [10] . These genes encode a wide variety of proteins connected to many biological processes contributing to bacterial survival , adaptation in the host niche , colonization and invasion [11 , 12] . NadR belongs to the MarR ( Multiple Antibiotic Resistance Regulator ) family , a group of ligand-responsive transcriptional regulators ubiquitous in bacteria and archaea . MarR family proteins can promote bacterial survival in the presence of antibiotics , toxic chemicals , organic solvents or reactive oxygen species [13 , 14] and can regulate virulence factor expression [15] . MarR homologues can act either as transcriptional repressors or as activators [16] . Although > 50 MarR family structures are known , a molecular understanding of their ligand-dependent regulatory mechanisms is still limited , often hampered by lack of identification of their ligands and/or DNA targets . A potentially interesting exception comes from the ligand-free and salicylate-bound forms of the Methanobacterium thermoautotrophicum protein MTH313 which revealed that two salicylate molecules bind to one MTH313 dimer and induce large conformational changes , apparently sufficient to prevent DNA binding [17] . However , the homologous archeal Sulfolobus tokodaii protein ST1710 presented essentially the same structure in ligand-free and salicylate-bound forms , apparently contrasting the mechanism proposed for MTH313 [18] . Despite these apparent differences , MTH313 and ST1710 bind salicylate in approximately the same site , between their dimerization and DNA-binding domains . However , it is unknown whether salicylate is a relevant in vivo ligand of either of these two proteins , which share ~20% sequence identity with NadR , rendering unclear the interpretation of these findings in relation to the regulatory mechanisms of NadR or other MarR family proteins [16] . NadR binds the nadA promoter and represses gene transcription [9] . NadR binds nadA on three different operators ( OpI , OpII and OpIII ) [10] . The DNA-binding activity of NadR is attenuated in vitro upon addition of various hydroxyphenylacetate ( HPA ) derivatives , including 4-HPA . 4-HPA is a small molecule derived from mammalian aromatic amino acid catabolism and is released in human saliva , where it has been detected at micromolar concentration [19] . In the presence of 4-HPA , NadR is unable to bind the nadA promoter and nadA gene expression is induced [9 , 10] . In vivo , the presence of 4-HPA in the host niche of N . meningitidis serves as an inducer of NadA production , thereby promoting bacterial adhesion to host cells [10] . Further , we recently reported that 3Cl , 4-HPA , produced during inflammation , is another inducer of nadA expression [20] . Extending our previous studies based on hydrogen-deuterium exchange mass spectrometry ( HDX-MS ) [21] , here we sought to reveal the molecular mechanisms and effects of NadR/HPA interactions via X-ray crystallography , NMR spectroscopy and complementary biochemical and in vivo mutagenesis studies . We obtained detailed new insights into ligand specificity , how the ligand allosterically influences the DNA-binding ability of NadR , and the regulation of nadA expression , thus also providing a deeper structural understanding of the ligand-responsive MarR super-family . Moreover , these findings are important because the activity of NadR impacts the potential coverage provided by anti-NadA antibodies elicited by the Bexsero vaccine and influences host-bacteria interactions that contribute to meningococcal pathogenesis [20] .
Recombinant NadR was produced in E . coli using an expression construct prepared from N . meningitidis serogroup B strain MC58 . Standard chromatographic techniques were used to obtain a highly purified sample of NadR ( see Materials and Methods ) . In analytical size-exclusion high-performance liquid chromatography ( SE-HPLC ) experiments coupled with multi-angle laser light scattering ( MALLS ) , NadR presented a single species with an absolute molecular mass of 35 kDa ( S1 Fig ) . These data showed that NadR was dimeric in solution , since the theoretical molecular mass of the NadR dimer is 33 . 73 kDa; and , there was no change in oligomeric state on addition of 4-HPA . The thermal stability of NadR was examined using differential scanning calorimetry ( DSC ) . Since ligand-binding often increases protein stability , we also investigated the effect of various HPAs ( Fig 1A ) on the melting temperature ( Tm ) of NadR . As a control of specificity , we also tested salicylate , a known ligand of some MarR proteins previously reported to increase the Tm of ST1710 and MTH313 [17] . The Tm of NadR was 67 . 4 ± 0 . 1°C in the absence of ligand , and was unaffected by salicylate . However , an increased thermal stability was induced by 4-HPA and , to a lesser extent , by 3-HPA . Interestingly , NadR displayed the greatest Tm increase upon addition of 3Cl , 4-HPA ( Table 1 and Fig 1B ) . To further investigate the binding of HPAs to NadR , we used surface plasmon resonance ( SPR ) . The SPR sensorgrams revealed very fast association and dissociation events , typical of small molecule ligands , thus prohibiting a detailed study of binding kinetics . However , steady-state SPR analyses of the NadR-HPA interactions allowed determination of the equilibrium dissociation constants ( KD ) ( Table 1 and S2 Fig ) . The interactions of 4-HPA and 3Cl , 4-HPA with NadR exhibited KD values of 1 . 5 mM and 1 . 1 mM , respectively . 3-HPA showed a weaker interaction , with a KD of 2 . 7 mM , while salicylate showed only a very weak response that did not reach saturation , indicating a non-specific interaction with NadR . A ranking of these KD values showed that 3Cl , 4-HPA was the tightest binder , and thus matched the ranking of ligand-induced Tm increases observed in the DSC experiments . Although these KD values indicate rather weak interactions , they are similar to the values reported previously for the MarR/salicylate interaction ( KD ~1 mM ) [22] and the MTH313/salicylate interaction ( KD 2–3 mM ) [17] , and approximately 20-fold tighter than the ST1710/salicylate interaction ( KD ~20 mM ) [18] . To fully characterize the NadR/HPA interactions , we sought to determine crystal structures of NadR in ligand-bound ( holo ) and ligand-free ( apo ) forms . First , we crystallized NadR ( a selenomethionine-labelled derivative ) in the presence of a 200-fold molar excess of 4-HPA . The structure of the NadR/4-HPA complex was determined at 2 . 3 Å resolution using a combination of the single-wavelength anomalous dispersion ( SAD ) and molecular replacement ( MR ) methods , and was refined to Rwork/Rfree values of 20 . 9/26 . 0% ( Table 2 ) . Despite numerous attempts , we were unable to obtain high-quality crystals of NadR complexed with 3Cl , 4-HPA , 3 , 4-HPA , 3-HPA or DNA targets . However , it was eventually possible to crystallize apo-NadR , and the structure was determined at 2 . 7 Å resolution by MR methods using the NadR/4-HPA complex as the search model . The apo-NadR structure was refined to Rwork/Rfree values of 19 . 1/26 . 8% ( Table 2 ) . The asymmetric unit of the NadR/4-HPA crystals ( holo-NadR ) contained one NadR homodimer , while the apo-NadR crystals contained two homodimers . In the apo-NadR crystals , the two homodimers were related by a rotation of ~90°; the observed association of the two dimers was presumably merely an effect of crystal packing , since the interface between the two homodimers is small ( < 550 Å2 of buried surface area ) , and is not predicted to be physiologically relevant by the PISA software [23] . Moreover , our SE-HPLC/MALLS analyses ( see above ) revealed that in solution NadR is dimeric , and previous studies using native mass spectrometry ( MS ) revealed dimers , not tetramers [21] . The NadR homodimer bound to 4-HPA has a dimerization interface mostly involving the top of its ‘triangular’ form , while the two DNA-binding domains are located at the base ( Fig 2A ) . High-quality electron density maps allowed clear identification of the bound ligand , 4-HPA ( Fig 2B ) . The overall structure of NadR shows dimensions of ~50 × 65 × 50 Å and a large homodimer interface that buries a total surface area of ~ 4800 Å2 . Each NadR monomer consists of six α-helices and two short β-strands , with helices α1 , α5 , and α6 forming the dimer interface . Helices α3 and α4 form a helix-turn-helix motif , followed by the “wing motif” comprised of two short antiparallel β-strands ( β1-β2 ) linked by a relatively long and flexible loop . Interestingly , in the α4-β2 region , the stretch of residues from R64-R91 presents seven positively-charged side chains , all available for potential interactions with DNA . Together , these structural elements constitute the winged helix-turn-helix ( wHTH ) DNA-binding domain and , together with the dimeric organization , are the hallmarks of MarR family structures [16] . The NadR dimer interface is formed by at least 32 residues , which establish numerous inter-chain salt bridges or hydrogen bonds , and many hydrophobic packing interactions ( Fig 3A and 3B ) . To determine which residues were most important for dimerization , we studied the interface in silico and identified several residues as potential mediators of key stabilizing interactions . Using site-directed mutagenesis , a panel of eight mutant NadR proteins was prepared ( including mutations H7A , S9A , N11A , D112A , R114A , Y115A , K126A , L130K and L133K ) , sufficient to explore the entire dimer interface . Each mutant NadR protein was purified , and then its oligomeric state was examined by analytical SE-HPLC . Almost all the mutants showed the same elution profile as the wild-type ( WT ) NadR protein . Only the L130K mutation induced a notable change in the oligomeric state of NadR ( Fig 3C ) . Further , in SE-MALLS analyses , the L130K mutant displayed two distinct species in solution , approximately 80% being monomeric ( a 19 kDa species ) , and only 20% retaining the typical native dimeric state ( a 35 kDa species ) ( Fig 3D ) , demonstrating that Leu130 is crucial for stable dimerization . It is notable that L130 is usually present as Leu , or an alternative bulky hydrophobic amino acid ( e . g . Phe , Val ) , in many MarR family proteins , suggesting a conserved role in stabilizing the dimer interface . In contrast , most of the other residues identified in the NadR dimer interface were poorly conserved in the MarR family . The NadR/4-HPA structure revealed the ligand-binding site nestled between the dimerization and DNA-binding domains ( Fig 2 ) . The ligand showed a different position and orientation compared to salicylate complexed with MTH313 and ST1710 [17 , 18] ( see Discussion ) . The binding pocket was almost entirely filled by 4-HPA and one water molecule , although there also remained a small tunnel 2-4Å in diameter and 5-6Å long leading from the pocket ( proximal to the 4-hydroxyl position ) to the protein surface . The tunnel was lined with rather hydrophobic amino acids , and did not contain water molecules . Unexpectedly , only one monomer of the holo-NadR homodimer contained 4-HPA in the binding pocket , whereas the corresponding pocket of the other monomer was unoccupied by ligand , despite the large excess of 4-HPA used in the crystallization conditions . Inspection of the protein-ligand interaction network revealed no bonds from NadR backbone groups to the ligand , but several key side chain mediated hydrogen ( H ) -bonds and ionic interactions , most notably between the carboxylate group of 4-HPA and Ser9 of chain A ( SerA9 ) , and chain B residues TrpB39 , ArgB43 and TyrB115 ( Fig 4A ) . At the other ‘end’ of the ligand , the 4-hydroxyl group was proximal to AspB36 , with which it may establish an H-bond ( see bond distances in Table 3 ) . The water molecule observed in the pocket was bound by the carboxylate group and the side chains of SerA9 and AsnA11 . In addition to the H-bonds involving the carboxylate and hydroxyl groups of 4-HPA , binding of the phenyl moiety appeared to be stabilized by several van der Waals’ contacts , particularly those involving the hydrophobic side chain atoms of LeuB21 , MetB22 , PheB25 , LeuB29 and ValB111 ( Fig 4A ) . Notably , the phenyl ring of PheB25 was positioned parallel to the phenyl ring of 4-HPA , potentially forming π-π parallel-displaced stacking interactions . Consequently , residues in the 4-HPA binding pocket are mostly contributed by NadR chain B , and effectively created a polar ‘floor’ and a hydrophobic ‘ceiling’ , which house the ligand . Collectively , this mixed network of polar and hydrophobic interactions endows NadR with a strong recognition pattern for HPAs , with additional medium-range interactions potentially established with the hydroxyl group at the 4-position . We modelled the binding of other HPAs by in silico superposition onto 4-HPA in the holo-NadR structure , and thereby obtained molecular explanations for the binding specificities of diverse ligands . For example , similar to 4-HPA , the binding of 3Cl , 4-HPA could involve multiple bonds towards the carboxylate group of the ligand and some to the 4-hydroxyl group . Additionally , the side chains of LeuB29 and AspB36 would be only 2 . 6–3 . 5 Å from the chlorine atom , thus providing van der Waals’ interactions or H-bonds to generate the additional binding affinity observed for 3Cl , 4-HPA ( Fig 4B ) . The presence of a single hydroxyl group at position 2 , as in 2-HPA , rather than at position 4 , would eliminate the possibility of favorable interactions with AspB36 , resulting in the lack of NadR regulation by 2-HPA described previously [20] . Finally , salicylate is presumably unable to specifically bind NadR due to the 2-hydroxyl substitution and the shorter aliphatic chain connecting its carboxylate group ( Fig 1A ) : the compound simply seems too small to simultaneously establish the network of beneficial bonds observed in the NadR/HPA interactions . We attempted to investigate further the binding stoichiometry using solution-based techniques . However , studies based on tryptophan fluorescence were confounded by the fluorescence of the HPA ligands , and isothermal titration calorimetry ( ITC ) was unfeasible due to the need for very high concentrations of NadR in the ITC chamber ( due to the relatively low affinity ) , which exceeded the solubility limits of the protein . However , it was possible to calculate the binding stoichiometry of the NadR-HPA interactions using an SPR-based approach . In SPR , the signal measured is proportional to the total molecular mass proximal to the sensor surface; consequently , if the molecular weights of the interactors are known , then the stoichiometry of the resulting complex can be determined [24] . This approach relies on the assumption that the captured protein ( ‘the ligand’ , according to SPR conventions ) is 100% active and freely-accessible to potential interactors ( ‘the analytes’ ) . This assumption is likely valid for this pair of interactors , for two main reasons . Firstly , NadR is expected to be covalently immobilized on the sensor chip as a dimer in random orientations , since it is a stable dimer in solution and has sixteen lysines well-distributed around its surface , all able to act as potential sites for amine coupling to the chip , and none of which are close to the ligand-binding pocket . Secondly , the HPA analytes are all very small ( MW 150–170 , Fig 1A ) and therefore are expected to be able to diffuse readily into all potential binding sites , irrespective of the random orientations of the immobilized NadR dimers on the chip . The stoichiometry of the NadR-HPA interactions was determined using Eq 1 ( see Materials and Methods ) , and revealed stoichiometries of 1 . 13 for 4-HPA , 1 . 02 for 3-HPA , and 1 . 21 for 3Cl , 4-HPA , strongly suggesting that one NadR dimer bound to 1 HPA analyte molecule . The crystallographic data , supported by the SPR studies of binding stoichiometry , revealed the lack of a second 4-HPA molecule in the homodimer , suggesting negative co-operativity , a phenomenon previously described for the MTH313/salicylate interaction [17] and for other MarR family proteins [16] . To explore the molecular basis of asymmetry in holo-NadR , we superposed its ligand-free monomer ( chain A ) onto the ligand-occupied monomer ( chain B ) . Overall , the superposition revealed a high degree of structural similarity ( Cα root mean square deviation ( rmsd ) of 1 . 5Å ) , though on closer inspection a rotational difference of ~9 degrees along the long axis of helix α6 was observed , suggesting that 4-HPA induced a slight conformational change ( Fig 5A ) . However , since residues of helix α6 were not directly involved in ligand binding , an explanation for the lack of 4-HPA in monomer A did not emerge by analyzing only these backbone atom positions , suggesting that a more complex series of allosteric events may occur . Indeed , we noted interesting differences in the side chains of Met22 , Phe25 and Arg43 , which in monomer B are used to contact the ligand while in monomer A they partially occupied the pocket and collectively reduced its volume significantly . Specifically , upon analysis with the CASTp software [25] , the pocket in chain B containing the 4-HPA exhibited a total volume of approximately 370 Å3 , while the pocket in chain A was occupied by these three side chains that adopted ‘inward’ positions and thereby divided the space into a few much smaller pockets , each with volume < 50 Å3 , evidently rendering chain A unfavorable for ligand binding . Most notably , atomic clashes between the ligand and the side chains of MetA22 , PheA25 and ArgA43 would occur if 4-HPA were present in the monomer A pocket ( Fig 5B ) . Subsequently , analyses of the pockets in apo-NadR revealed that in the absence of ligand the long Arg43 side chain was always in the open ‘outward’ position compatible with binding to the 4-HPA carboxylate group . In contrast , the apo-form Met22 and Phe25 residues were still encroaching the spaces of the 4-hydroxyl group and the phenyl ring of the ligand , respectively ( Fig 5C ) . The ‘outward’ position of Arg43 generated an open apo-form pocket with volume approximately 380Å3 . Taken together , these observations suggest that Arg43 is a major determinant of ligand binding , and that its ‘inward’ position inhibits the binding of 4-HPA to the empty pocket of holo-NadR . Finally , we applied 15N heteronuclear solution NMR spectroscopy to examine the interaction of 4-HPA with apo NadR . We collected NMR spectra on NadR in the presence and absence of 4-HPA ( see Materials and Methods ) . The 1H-15N TROSY-HSQC spectrum of apo-NadR , acquired at 25°C , displayed approximately 140 distinct peaks ( Fig 6A ) , most of which correspond to backbone amide N-H groups . The broad spectral dispersion and the number of peaks observed , which is close to the number of expected backbone amide N-H groups for this polypeptide , confirmed that apo-NadR is well-folded under these conditions and exhibits one conformation appreciable on the NMR timescale , i . e . in the NMR experiments at 25°C , two or more distinct conformations of apo-NadR monomers were not readily apparent . Upon the addition of 4-HPA , over 45 peaks showed chemical shift perturbations , i . e . changed position in the spectrum or disappeared , while the remaining peaks remained unchanged . This observation showed that 4-HPA was able to bind NadR and induce notable changes in specific regions of the protein . However , in the presence of 4-HPA , the 1H-15N TROSY-HSQC spectrum of NadR displayed approximately 140 peaks , as for apo-NadR , i . e . two distinct stable conformations ( that might have potentially revealed the molecular asymmetry observed crystallographically ) were not notable . Considering the small size , fast diffusion and relatively low binding affinity of 4-HPA , it would not be surprising if the ligand associates and dissociates rapidly on the NMR time scale , resulting in only one set of peaks whose chemical shifts represent the average environment of the bound and unbound states . Interestingly , by cooling the samples to 10°C , we observed that a number of those peaks strongly affected by 4-HPA ( and therefore likely to be in the ligand-binding site ) demonstrated evidence of peak splitting , i . e . a tendency to become two distinct peaks rather than one single peak ( Fig 6B and 6C ) . These doubled peaks may therefore reveal that the cooler temperature partially trapped the existence in solution of two distinct states , in presence or absence of 4-HPA , with minor conformational differences occurring at least in proximity to the binding pocket . Although more comprehensive NMR experiments and full chemical shift assignment of the spectra would be required to precisely define this multi-state behavior , the NMR data clearly demonstrate that NadR exhibits conformational flexibility which is modulated by 4-HPA in solution . The apo-NadR crystal structure contained two homodimers in the asymmetric unit ( chains A+B and chains C+D ) . Upon overall structural superposition , these dimers revealed a few minor differences in the α6 helix ( a major component of the dimer interface ) and the helices α4-α5 ( the DNA binding region ) , and an rmsd of 1 . 55Å ( Fig 7A ) . Similarly , the entire holo-homodimer could be closely superposed onto each of the apo-homodimers , showing rmsd values of 1 . 29Å and 1 . 31Å , and with more notable differences in the α6 helix positions ( Fig 7B ) . The slightly larger rmsd between the two apo-homodimers , rather than between apo- and holo-homodimers , further indicate that apo-NadR possesses a notable degree of intrinsic conformational flexibility . To further investigate the conformational rearrangements of NadR , we performed local structural alignments using only a subset of residues in the DNA-binding helix ( α4 ) . By selecting and aligning residues Arg64-Ala77 of one α4 helix per dimer , superposition of the holo-homodimer onto the two apo-homodimers revealed differences in the monomer conformations of each structure . While one monomer from each structure was closely superimposable ( Fig 8A , left side ) , the second monomer displayed quite large differences ( Fig 8A , right side ) . Most notably , the position of the DNA-binding helix α4 shifted by as much as 6 Å ( Fig 8B ) . Accordingly , helix α4 was also found to be one of the most dynamic regions in previous HDX-MS analyses of apo-NadR in solution [21] . However , structural comparisons revealed that the shift of holo-NadR helix α4 induced by the presence of 4-HPA was also accompanied by several changes at the holo dimer interface , while such extensive structural differences were not observed in the apo dimer interfaces , particularly notable when comparing the α6 helices ( S3 Fig ) . In summary , compared to ligand-stabilized holo-NadR , apo-NadR displayed an intrinsic flexibility focused in the DNA-binding region . This was also evident in the greater disorder ( i . e . less well-defined electron density ) in the β1-β2 loops of the apo dimers ( density for 16 residues per dimer was missing ) compared to the holo dimer ( density for only 3 residues was missing ) . In holo-NadR , the distance separating the two DNA-binding α4 helices was 32 Å , while in apo-NadR it was 29 Å for homodimer AB , and 34 Å for homodimer CD ( Fig 8C ) . Thus , the apo-homodimer AB presented the DNA-binding helices in a conformation similar to that observed in the protein:DNA complex of OhrR:ohrA from Bacillus subtilis [26] ( Fig 8C ) . Interestingly , OhrR contacts ohrA across 22 base pairs ( bp ) , and similarly the main NadR target sites identified in the nadA promoter ( the operators Op I and Op II ) both span 22 bp [9 , 10] . Pairwise superpositions showed that the NadR apo-homodimer AB was the most similar to OhrR ( rmsd 2 . 6 Å ) , while the holo-homodimer was the most divergent ( rmsd 3 . 3 Å ) ( Fig 8C ) . Assuming the same DNA-binding mechanism is used by OhrR and NadR , the apo-homodimer AB seems ideally pre-configured for DNA binding , while 4-HPA appeared to stabilize holo-NadR in a conformation poorly suited for DNA binding . Specifically , in addition to the different inter-helical translational distances , the α4 helices in the holo-NadR homodimer were also reoriented , resulting in movement of α4 out of the major groove , by up to 8Å , and presumably preventing efficient DNA binding in the presence of 4-HPA ( Fig 8D ) . When aligned with OhrR , the apo-homodimer CD presented yet another different intermediate conformation ( rmsd 2 . 9Å ) , apparently not ideally pre-configured for DNA binding , but which in solution can presumably readily adopt the AB conformation due to the intrinsic flexibility described above . While previous studies had correctly suggested the involvement of several NadR residues in ligand binding [21] , the crystal structures presented here revealed additional residues with previously unknown roles in dimerization and/or binding to 4-HPA . To explore the functional involvement of these residues , we characterized the behavior of four new NadR mutants ( H7A , S9A , N11A and F25A ) in an in vivo assay using the previously described MC58-Δ1843 nadR-null mutant strain [9] , which was complemented either by wild-type nadR or by the nadR mutants . NadA protein abundance levels were assessed by Western blotting to evaluate the ability of the NadR mutants to repress the nadA promoter , in the presence or absence of 4-HPA . The nadR H7A , S9A and F25A complemented strains showed hyper-repression of nadA expression in vivo , i . e . these mutants repressed nadA more efficiently than the NadR WT protein , either in the presence or absence of 4-HPA , while complementation with wild-type nadR resulted in high production of NadA only in the presence of 4-HPA ( Fig 9 ) . Interestingly , and on the contrary , the nadR N11A complemented strain showed hypo-repression ( i . e . exhibited high expression of nadA both in absence and presence of 4-HPA ) . This mutagenesis data revealed that NadR residues His7 , Ser9 , Asn11 and Phe25 play key roles in the ligand-mediated regulation of NadR; they are each involved in the controlled de-repression of the nadA promoter and synthesis of NadA in response to 4-HPA in vivo .
NadA is a surface-exposed meningococcal protein contributing to pathogenesis , and is one of three main antigens present in the vaccine Bexsero [6] . A detailed understanding of the in vitro repression of nadA expression by the transcriptional regulator NadR is important , both because it is a relevant disease-related model of how small-molecule ligands can regulate MarR family proteins and thereby impact bacterial virulence , and because nadA expression levels are linked to the prediction of vaccine coverage [20] [27] . The repressive activity of NadR can be relieved by hydroxyphenylacetate ( HPA ) ligands [20] , and HDX-MS studies previously indicated that 4-HPA stabilizes dimeric NadR in a configuration incompatible with DNA binding [21] . Despite these and other studies [16] , the molecular mechanisms by which ligands regulate MarR family proteins are relatively poorly understood and likely differ depending on the specific ligand . Given the importance of NadR-mediated regulation of NadA levels in the contexts of meningococcal pathogenesis , we sought to characterize NadR , and its interaction with ligands , at atomic resolution . Firstly , we confirmed that NadR is dimeric in solution and demonstrated that it retains its dimeric state in the presence of 4-HPA , indicating that induction of a monomeric status is not the manner by which 4-HPA regulates NadR . These observations were in agreement with ( i ) a previous study of NadR performed using SEC and mass spectrometry [21] , and ( ii ) crystallographic studies showing that several MarR homologues are dimeric [16] . We also used structure-guided site-directed mutagenesis to identify an important conserved residue , Leu130 , which stabilizes the NadR dimer interface , knowledge of which may also inform future studies to explore the regulatory mechanisms of other MarR family proteins . Secondly , we assessed the thermal stability and unfolding of NadR in the presence or absence of ligands . All DSC profiles showed a single peak , suggesting that a single unfolding event simultaneously disrupted the dimer and the monomer . HPA ligands specifically increased the stability of NadR . The largest effects were induced by the naturally-occurring compounds 4-HPA and 3Cl , 4-HPA , which , in SPR assays , were found to bind NadR with KD values of 1 . 5 mM and 1 . 1 mM , respectively . Although these NadR/HPA interactions appeared rather weak , their distinct affinities and specificities matched their in vitro effects [9 , 20] and their biological relevance appears similar to previous proposals that certain small molecules , including some antibiotics , in the millimolar concentration range may be broad inhibitors of MarR family proteins [13 , 17] . Indeed , 4-HPA is found in human saliva [19] and 3Cl , 4-HPA is produced during inflammatory processes [28] , suggesting that these natural ligands are encountered by N . meningitidis in the mucosa of the oropharynx during infections . It is also possible that NadR responds to currently unidentified HPA analogues . Indeed , in the NadR/4-HPA complex there was a water molecule close to the carboxylate group and also a small unfilled tunnel ~5Å long , both factors suggesting that alternative larger ligands could occupy the pocket . It is conceivable that such putative ligands may establish different bonding networks , potentially binding in a 2:2 ratio , rather than the 1:2 ratio observed herein . The ability to respond to various ligands might enable NadR in vivo to orchestrate multiple response mechanisms and modulate expression of genes other than nadA . Ultimately , confirmation of the relevance of each ligand will require a deeper understanding of the available concentration in vivo in the host niche during bacterial colonization and inflammation . Here , we determined the first crystal structures of apo-NadR and holo-NadR . These experimentally-determined structures enabled a new detailed characterization of the ligand-binding pocket . In holo-NadR , 4-HPA interacted directly with at least 11 polar and hydrophobic residues . Several , but not all , of these interactions were predicted previously by homology modelling combined with ligand docking in silico [21] . Subsequently , we established the functional importance of His7 , Ser9 , Asn11 and Phe25 in the in vitro response of meningococcus to 4-HPA , via site-directed mutagenesis . More unexpectedly , the crystal structure revealed that only one molecule of 4-HPA was bound per NadR dimer . We confirmed this stoichiometry in solution using SPR methods . We also used heteronuclear NMR spectroscopy to detect substantial conformational changes of NadR occurring in solution upon addition of 4-HPA . Moreover , NMR spectra at 10°C suggested the existence of two distinct conformations of NadR in the vicinity of the ligand-binding pocket . More powerfully , our unique crystallographic observation of this ‘occupied vs unoccupied site’ asymmetry in the NadR/4-HPA interaction is , to our knowledge , the first example reported for a MarR family protein . Structural analyses suggested that ‘inward’ side chain positions of Met22 , Phe25 and especially Arg43 precluded binding of a second ligand molecule . Such a mechanism indicates negative cooperativity , which may enhance the ligand-responsiveness of NadR . Comparisons of the NadR/4-HPA complex with available MarR family/salicylate complexes revealed that 4-HPA has a previously unobserved binding mode . Briefly , in the M . thermoautotrophicum MTH313 dimer , one molecule of salicylate binds in the pocket of each monomer , though with two rather different positions and orientations , only one of which ( site-1 ) is thought to be biologically relevant [17] ( Fig 10A ) . In the S . tokodaii protein ST1710 , salicylate binds to the same position in each monomer of the dimer , in a site equivalent to the putative biologically relevant site of MTH313 [18] ( Fig 10B ) . Unlike other MarR family proteins which revealed multiple ligand binding interactions , we observed only 1 molecule of 4-HPA bound to NadR , suggesting a more specific and less promiscuous interaction . In NadR , the single molecule of 4-HPA binds in a position distinctly different from the salicylate binding site: translated by > 10 Å and with a 180° inverted orientation ( Fig 10C ) . Interestingly , a crystal structure was previously reported for a functionally-uncharacterized meningococcal homologue of NadR , termed NMB1585 , which shares 16% sequence identity with NadR [29] . The two structures can be closely aligned ( rmsd 2 . 3 Å ) , but NMB1585 appears unsuited for binding HPAs , since its corresponding ‘pocket’ region is occupied by several bulky hydrophobic side chains . It can be speculated that MarR family members have evolved separately to engage distinct signaling molecules , thus enabling bacteria to use the overall conserved MarR scaffold to adapt and respond to diverse changing environmental stimuli experienced in their natural niches . Alternatively , it is possible that other MarR homologues ( e . g . NMB1585 ) may have no extant functional binding pocket and thus may have lost the ability to respond to a ligand , acting instead as constitutive DNA-binding regulatory proteins . The apo-NadR crystal structures revealed two dimers with slightly different conformations , most divergent in the DNA-binding domain . It is not unusual for a crystal structure to reveal multiple copies of the same protein in very slightly different conformations , which are likely representative of the lowest-energy conformations sampled by the dynamic ensemble of molecular states occurring in solution , and which likely have only small energetic differences , as described previously for MexR ( a MarR protein ) [30] or more recently for the solute-binding protein FhuD2 [31 , 32] . Further , the holo-NadR structure was overall more different from the two apo-NadR structures ( rmsd values ~1 . 3Å ) , suggesting that the ligand selected and stabilized yet another conformation of NadR . These observations suggest that 4-HPA , and potentially other similar ligands , can shift the molecular equilibrium , changing the energy barriers that separate active and inactive states , and stabilizing the specific conformation of NadR poorly suited to bind DNA . Comparisons of the apo- and holo-NadR structures revealed that the largest differences occurred in the DNA-binding helix α4 . The shift of helix α4 in holo-NadR was also accompanied by rearrangements at the dimer interface , involving helices α1 , α5 , and α6 , and this holo-form appeared poorly suited for DNA-binding when compared with the known OhrR:ohrA complex [26] . While some flexibility of helix α4 was also observed in the two apo-structures , concomitant changes in the dimer interfaces were not observed , possibly due to the absence of ligand . One of the two conformations of apo-NadR appeared ideally suited for DNA-binding . Overall , these analyses suggest that the apo-NadR dimer has a pre-existing equilibrium that samples a variety of conformations , some of which are compatible with DNA binding . This intrinsically dynamic nature underlies the possibility for different conformations to inter-convert or to be preferentially selected by a regulatory ligand , as generally described in the ‘conformational selection’ model for protein-ligand interactions ( the Monod-Wyman-Changeux model ) , rather than an ‘induced fit’ model ( Koshland-Nemethy-Filmer ) [33] . The noted flexibility may also explain how NadR can adapt to bind various DNA target sequences [10] with slightly different structural features . Subsequently , upon ligand binding , holo-NadR adopts a structure less suited for DNA-binding and this conformation is selected and stabilized by a network of protein-ligand interactions and concomitant rearrangements at the NadR holo dimer interface . In an alternative and less extensive manner , the binding of two salicylate molecules to the M . thermoautotrophicum protein MTH313 appeared to induce large changes in the wHTH domain , which was associated with reduced DNA-binding activity [17] . Here we have presented two new crystal structures of the transcription factor , NadR , which regulates expression of the meningococcal surface protein , virulence factor and vaccine antigen NadA . Detailed structural analyses provided a molecular explanation for the ligand-responsive regulation by NadR on the majority of the promoters of meningococcal genes regulated by NadR , including nadA [10] . Intriguingly , NadR exhibits a reversed regulatory mechanism on a second class of promoters , including mafA of the multiple adhesin family–i . e . NadR represses these genes in the presence but not absence of 4-HPA . The latter may influence the surface abundance or secretion of maf proteins , an emerging class of highly conserved meningococcal putative adhesins and toxins with many important roles [11 , 12] . Further work is required to investigate how the two different promoter types influence the ligand-responsiveness of NadR during bacterial infection and may provide insights into the regulatory mechanisms occurring during these host-pathogen interactions . Ultimately , knowledge of the ligand-dependent activity of NadR will continue to deepen our understanding of nadA expression levels , which influence meningococcal pathogenesis .
In this study we used N . meningitidis MC58 wild type strain and related mutant derivatives . The MC58 isolate was kindly provided to us by Professor E . Richard Moxon , University of Oxford , UK , and was previously submitted to the Meningococcal Reference Laboratory , Manchester , UK [34] . Strains were routinely cultured , stocked , and transformed as described previously [10] . To generate N . meningitidis MC58 mutant strains expressing only the amino acid substituted forms of NadR , plasmids containing the sequence of nadR mutated to insert alanine codons to replace His7 , Ser9 , Asn11 or Phe25 were constructed using the QuikChange II XL Site-Directed Mutagenesis Kit ( Stratagene ) . The nadR gene ( also termed NMB1843 ) was mutated in the pComEry-1843 plasmid using couples of mutagenic primers ( forward and reverse ) . The resulting plasmids were named pComEry-1843H7A , -1843S9A , -1843N11A or -1843F25A , and contained a site-directed mutant allele of the nadR gene , in which the selected codons were respectively substituted by a GCG alanine codon , and were used for transformation of the MC-Δ1843 strain . Total lysates from single colonies of all transformants were used as a template for PCR analysis to confirm the correct insertion by double homologous recombinant event . When indicated , bacterial strains were grown in presence of 5 mM 4-HPA ( MW 152 , Sigma-Aldrich ) . The preparation of the expression construct enabling production of soluble NadR with an N-terminal His-tag followed by a thrombin cleavage site ( MGSSHHHHHHSSGLVPR↓GSH- ) ( where the arrow indicates the cleavage site ) and then NadR residues M1-S146 ( Uniprot code Q7DD70 ) , and methods to generate site-directed mutants , were described previously [21] . Briefly , site-directed mutagenesis was performed using two overlapping primers containing the desired mutation to amplify pET15b containing several NadR variants . ( Full oligonucleotide sequences of primers are available upon request ) . 1–10 ng of plasmid DNA template were amplified using Kapa HiFi DNA polymerase ( Kapa Biosystems ) and with the following cycling conditions: 98°C for 5 min , 15 cycles of ( 98°C for 30 s , 60°C for 30 s , 72°C for 6 min ) followed by a final extension of 10 min at 72°C . Residual template DNA was digested by 30 min incubation with FastDigest DpnI ( Thermo Scientific ) at 37°C and 1 μl of this reaction was used to transform E . coli DH5α competent cells . The full recombinant tagged NadR protein generated contained 166 residues , with a theoretical MW of 18746 , while after thrombin-cleavage the untagged protein contained 149 residues , with a theoretical MW of 16864 . The NadR expression constructs ( wild-type or mutant clones ) were transformed into E . coli BL21 ( DE3 ) cells and were grown at 37°C in Luria-Bertani ( LB ) medium supplemented with 100 μg/mL ampicillin , until an OD600 of 0 . 5 was reached . Target protein production was induced by the addition of 1 mM IPTG followed by incubation with shaking overnight at 21°C . For production of the selenomethionine ( SeMet ) derivative form of NadR for crystallization studies , essentially the same procedure was followed , but using the E . coli B834 strain grown in a modified M9 minimal medium supplemented with 40 mg/L L-SeMet . For production of 15N-labeled NadR for NMR analyses , the EnPresso B Defined Nitrogen-free medium ( Sigma-Aldrich ) was used; in brief , BL21 ( DE3 ) cells were grown in BioSilta medium at 30°C for 30 h , and production of the 15N-labeled NadR was enabled by the addition of 2 . 5 g/L 15NH4Cl and further incubation for 2 days . Cells were harvested by centrifugation ( 6400 g , 30 min , 4°C ) , resuspended in 20 mM HEPES pH 8 . 0 , 300 mM NaCl , 20 mM imidazole , and were lysed by sonication ( Qsonica Q700 ) . Cell lysates were clarified by centrifugation at 2800 g for 30 min , and the supernatant was filtered using a 0 . 22 μm membrane ( Corning filter system ) prior to protein purification . NadR was purified by affinity chromatography using an AKTA purifier ( GE Healthcare ) . All steps were performed at room temperature ( 18–26°C ) , unless stated otherwise . The filtered supernatant was loaded onto an Ni-NTA resin ( 5 mL column , GE Healthcare ) , and NadR was eluted using 4 steps of imidazole at 20 , 30 , 50 and 250 mM concentration , at a flow rate of 5 mL/min . Eluted fractions were examined by reducing and denaturing SDS-PAGE analysis . Fractions containing NadR were identified by a band migrating at ~17 kDa , and were pooled . The N-terminal 6-His tag was removed enzymatically using the Thrombin CleanCleave Kit ( Sigma-Aldrich ) . Subsequently , the sample was reloaded on the Ni-NTA resin to capture the free His tag ( or unprocessed tagged protein ) , thus allowing elution in the column flow-through of tagless NadR protein , which was used in all subsequent studies . The NadR sample was concentrated and loaded onto a HiLoad Superdex 75 ( 16/60 ) preparative size-exclusion chromatography ( SEC ) column equilibrated in buffer containing 20 mM HEPES pH 8 . 0 , 150 mM NaCl , at a flow-rate of 1 mL/min . NadR protein was collected and the final yield of purified protein obtained from 0 . 5 L LB growth medium was approximately 8 mg ( ~2 mg protein per g wet biomass ) . Samples were used immediately for crystallization or analytical experiments , or were frozen for storage at -20°C . MALLS analyses were performed online with SE-HPLC using a Dawn TREOS MALLS detector ( Wyatt Corp . , Santa Barbara , CA , USA ) and an incident laser wavelength of 658 nm . The intensity of the scattered light was measured at 3 angles simultaneously . Data analysis was performed using the Astra V software ( Wyatt ) to determine the weighted-average absolute molecular mass ( MW ) , the polydispersity index ( MW/Mn ) and homogeneity ( Mz/Mn ) for each oligomer present in solution . Normalization of the MALLS detectors was performed in each analytical session by use of bovine serum albumin . The thermal stability of NadR proteins was assessed by differential scanning calorimetry ( DSC ) using a MicroCal VP-Capillary DSC instrument ( GE Healthcare ) . NadR samples were prepared at a protein concentration of 0 . 5 mg/mL ( ~30 μM ) in buffer containing 20 mM HEPES , 300 mM NaCl , pH 7 . 4 , with or without 6 mM HPA or salicylate . The DSC temperature scan ranged from 10°C to 110°C , with a thermal ramping rate of 200°C per hour and a 4 second filter period . Data were analyzed by subtraction of the reference data for a sample containing buffer only , using the Origin 7 software . All experiments were performed in triplicate , and mean values of the melting temperature ( Tm ) were determined . Purified NadR was concentrated to 2 . 7 mg/mL ( ~160 μM ) using a centrifugal concentration device ( Amicon Ultra-15 Centrifugal Filter Unit with Ultracel-10 membrane with cut-off size 10 kDa; Millipore ) running at 600 g in a bench top centrifuge ( Thermo Scientific IEC CL40R ) refrigerated at 2–8°C . To prepare holo-NadR samples , HPA ligands were added at a 200-fold molar excess prior to the centrifugal concentration step . The concentrated holo- or apo-NadR was subjected to crystallization trials performed in 96-well low-profile Intelli-Plates ( Art Robbins ) or 96-well low-profile Greiner crystallization plates , using a nanodroplet sitting-drop vapour-diffusion format and mixing equal volumes ( 200 nL ) of protein samples and crystallization buffers using a Gryphon robot ( Art Robbins ) . Crystallization trays were incubated at 20°C . Crystals of apo-NadR were obtained in 50% PEG 3350 and 0 . 13 M di-Ammonium hydrogen citrate , whereas crystals of SeMet–NadR in complex with 4-HPA grew in condition H4 of the Morpheus screen ( Molecular Dimensions ) , which contains 37 . 5% of the pre-mixed precipitant stock MPD_P1K_PEG 3350 , buffer system 1 and 0 . 1 M amino acids , at pH 6 . 5 . All crystals were mounted in cryo-loops using 10% ethylene glycol or 10% glycerol as cryo-protectant before cooling to 100 K for data collection . X-ray diffraction data from crystals of apo-NadR and SeMet–NadR/4-HPA were collected on beamline PXII-X10SA of the Swiss Light Source ( SLS ) at the Paul Scherrer Institut ( PSI ) , Villigen , Switzerland . All diffraction data were processed with XDS [36] and programs from the CCP4 suite [37] . Crystals of apo-NadR and 4-HPA-bound SeMet-NadR belonged to space group P43 21 2 ( see Table 2 ) . Apo-NadR crystals contained four molecules ( two dimers ) in the asymmetric unit ( Matthews coefficient 2 . 25 Å3 Da−1 , for a solvent content of 45% ) , while crystals of SeMet–NadR/4-HPA contained two molecules ( one dimer ) in the asymmetric unit ( Matthews coefficient 1 . 98 Å3 Da−1 , for a solvent content of 38% ) . In solving the holo-NadR structure , an initial and marginal molecular replacement ( MR ) solution was obtained using as template search model the crystal structure of the transcriptional regulator PA4135 ( PBD entry 2FBI ) , with which NadR shares ~54% sequence identity . This solution was combined with SAD data to aid identification of two selenium sites in NadR , using autosol in phenix [38] and this allowed generation of high-quality electron density maps that were used to build and refine the structure of the complex . Electron densities were clearly observed for almost the entire dimeric holo-NadR protein , except for a new N-terminal residues and residues 88–90 of chain B . The crystal structure of apo-NadR was subsequently solved by MR in Phaser [39] at 2 . 7 Å , using the final refined model of SeMet-NadR/4-HPA as the search model . For apo-NadR , electron densities were clearly observed for almost the entire protein , although residues 84–91 of chains A , C , and D , and residues 84–90 of chain B lacked densities suggesting local disorder . Both structures were refined and rebuilt using phenix [38] and Coot [40] , and structural validation was performed using Molprobity [41] . Data collection and refinement statistics are reported in Table 2 . Atomic coordinates of the two NadR structures have been deposited in the Protein Data Bank , with entry codes 5aip ( NadR bound to 4-HPA ) and 5aiq ( apo-NadR ) . All crystallographic software was compiled , installed and maintained by SBGrid [42] . For heteronuclear NMR experiments , the NadR protein concentration used was 85 μM ( ~ 1 . 4 mg/mL ) in a solution containing 100 mM sodium phosphate buffer ( 90% H2O and 10% D2O ) and 200 mM NaCl , prepared in the apo-form or in the presence of a 200-fold molar excess of 4-HPA , at pH 6 . 5 . The stability , integrity and dimeric state of the protein in the NMR buffer was confirmed by analytical SEC ( Superdex 75 , 10/300 column ) prior to NMR studies . 1H-15N transverse relaxation-optimized spectroscopy ( TROSY ) -heteronuclear single quantum coherence ( HSQC ) experiments on apo-NadR and NadR in the presence of 4-HPA were acquired using an Avance 950 Bruker spectrometer , operating at a proton frequency of 949 . 2 MHz and equipped with triple resonance cryogenically-cooled probe at two different temperatures ( 298 K and 283 K ) . 1H-15N TROSY-HSQC experiments were recorded for 12 h , with a data size of 1024 x 232 points . Spectra were processed using the Bruker TopSpin 2 . 1 and 3 . 1 software packages . Western blot analysis was performed as described previously [10] . | Serogroup B meningococcus ( MenB ) causes fatal sepsis and invasive meningococcal disease , particularly in young children and adolescents , as highlighted by recent MenB outbreaks in universities of the United States and Canada . The Bexsero vaccine protects against MenB and has recently been approved in > 35 countries worldwide . Neisseria adhesin A ( NadA ) present on the meningococcal surface can mediate binding to human cells and is one of the three MenB vaccine protein antigens . The amount of NadA exposed on the meningococcal surface also influences the antibody-mediated serum bactericidal response measured in vitro . A deep understanding of nadA expression is therefore important , otherwise the contribution of NadA to vaccine-induced protection against meningococcal meningitis may be underestimated . The abundance of surface-exposed NadA is regulated by the ligand-responsive transcriptional repressor NadR . Here , we present functional , biochemical and high-resolution structural data on NadR . Our studies provide detailed insights into how small molecule ligands , such as hydroxyphenylacetate derivatives , found in relevant host niches , modulate the structure and activity of NadR , by ‘conformational selection’ of inactive forms . These findings shed light on the regulation of NadR , a key MarR-family virulence factor of this important human pathogen . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"dimers",
"(chemical",
"physics)",
"chemical",
"characterization",
"crystal",
"structure",
"chemical",
"compounds",
"salicylic",
"acid",
"nmr",
"spectroscopy",
"condensed",
"matter",
"physics",
"dna-binding",
"proteins",
"crystallography",
"research",
"and",
"analysis",
"methods",
"solid",
"state",
"physics",
"proteins",
"acids",
"chemistry",
"salicylates",
"binding",
"analysis",
"physics",
"biochemistry",
"stoichiometry",
"electron",
"density",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"chemical",
"physics",
"spectrum",
"analysis",
"techniques"
] | 2016 | Molecular Basis of Ligand-Dependent Regulation of NadR, the Transcriptional Repressor of Meningococcal Virulence Factor NadA |
Adeno-associated viruses ( AAV ) have evolved to exploit the dynamic reorganization of host cell machinery during co-infection by adenoviruses and other helper viruses . In the absence of helper viruses , host factors such as the proteasome and DNA damage response machinery have been shown to effectively inhibit AAV transduction by restricting processes ranging from nuclear entry to second-strand DNA synthesis . To identify host factors that might affect other key steps in AAV infection , we screened an siRNA library that revealed several candidate genes including the PHD finger-like domain protein 5A ( PHF5A ) , a U2 snRNP-associated protein . Disruption of PHF5A expression selectively enhanced transgene expression from AAV by increasing transcript levels and appears to influence a step after second-strand synthesis in a serotype and cell type-independent manner . Genetic disruption of U2 snRNP and associated proteins , such as SF3B1 and U2AF1 , also increased expression from AAV vector , suggesting the critical role of U2 snRNP spliceosome complex in this host-mediated restriction . Notably , adenoviral co-infection and U2 snRNP inhibition appeared to target a common pathway in increasing expression from AAV vectors . Moreover , pharmacological inhibition of U2 snRNP by meayamycin B , a potent SF3B1 inhibitor , substantially enhanced AAV vector transduction of clinically relevant cell types . Further analysis suggested that U2 snRNP proteins suppress AAV vector transgene expression through direct recognition of intact AAV capsids . In summary , we identify U2 snRNP and associated splicing factors , which are known to be affected during adenoviral infection , as novel host restriction factors that effectively limit AAV transgene expression . Concurrently , we postulate that pharmacological/genetic manipulation of components of the spliceosomal machinery might enable more effective gene transfer modalities with recombinant AAV vectors .
Viral pathogens are known to reorganize different components of the host cell machinery during the course of infection . For instance , adenoviruses have been shown to induce nuclear reorganization of host splicing factors and mislocalization of the DNA damage response machinery [1] . Similarly , herpesviruses can induce sequestration of cellular chaperone proteins and the 26S proteasome in nuclear foci to facilitate quality control during replication [2] . Adeno-associated viruses ( AAV ) are helper-dependent parvoviruses that have evolved strategies to replicate efficiently by exploiting host cell co-infection by adenoviruses or herpesviruses [3] . The infectious pathway of wild type AAV and recombinant AAV vectors consists of multiple stages starting with cell surface receptor binding , followed by endocytosis , endosomal escape , nuclear import , second-strand synthesis , and subsequent expression of the vector-encoded transgene [4 , 5] . The post-entry steps leading to AAV transduction are particularly subject to restriction by cell intrinsic factors [6 , 7 , 8] . Studies have identified impaired AAV vector transduction due to inefficient nuclear import [6] , uncoating of vector genomes [7] , or second-strand synthesis [8 , 9] . Treatment with proteasome inhibitors has demonstrated improved transduction by AAV vectors [10 , 11] , suggesting the involvement of proteasomal degradation pathways in restricting AAV transduction . Nevertheless , modest increases in accumulation of viral DNA following proteasomal inhibition cannot solely account for substantial increases in AAV transduction [12] , and the underlying mechanism remains elusive . Other host factors such as the FKBP52 [13] , Mre11/Rad50/Nbs1 complex [14 , 15] , APOBEC3A [16] and more recently , TRIM19/promyelocytic leukemia protein ( PML ) [17] have been shown to inhibit AAV replication by blocking second-strand synthesis . AAV has emerged as a promising vehicle to achieve long-term gene expression with low toxicity . Recombinant vectors based on naturally occurring AAV serotype capsids and libraries of engineered capsid mutants have demonstrated unique receptor usages and tissue tropisms , providing versatility for tissue-targeted gene expression [18 , 19 , 20 , 21 , 22] . For instance , AAV vectors with AAV serotype 9 ( AAV9 ) capsid efficiently transduce cardiac tissues , while vectors with AAV2 capsid show efficient transduction of kidney cells [23 , 24 , 25] . Importantly , recent phase I and phase II clinical trials using AAV vectors have established their safety , in some cases , with notable clinical benefits [26 , 27 , 28 , 29 , 30] , opening the door to AAV vector gene therapies for various human disease conditions . Currently , however , efficient gene transduction by AAV vectors typically requires high doses of vectors . This presents a major barrier for the widespread use of AAV vectors in the clinic , due to potential challenges in manufacturing clinical grade vectors for high dose studies as well as the increased risk of eliciting host immune responses or inducing insertional mutagenesis at high vector doses [31 , 32 , 33] . Improving AAV vector transduction efficiency would reduce vector doses required for efficient gene delivery , minimizing the risks associated with high dose AAV vectors . The current report is focused on the identification of novel host restriction factor ( s ) that limit expression from AAV vectors as well as proof-of-principle studies that would enable effective gene therapy with lower vector doses in clinical trials .
We screened an siRNA library , which covers 600 known and putative human genes in the ubiquitin and proteasome pathways , for AAV vector transduction . We identified 12 candidate genes ( Fig 1A ) . Disruption of those genes in HeLa cells increased luciferase expression by an AAV9 vector , AAV9 CMV-Luc , over 10-fold ( Fig 1A ) . Further verification with distinct siRNAs and lenti-shRNA vectors found disruption of PHF5A , RAB40A and PRICKLE4 reproducibly increased AAV9 transduction . Treatment of HeLa cells with two PHF5A siRNAs led to over 80% reduction in PHF5A transcripts ( Fig 1B ) and increased the transduction by AAV9 vectors up to 12-fold ( Fig 1C ) . In contrast , disruption of PHF5A expression did not strongly enhance luciferase expression of adenoviral or HIV-based lentiviral vectors ( Fig 1D ) . Similar results were observed upon disruption of RAB40A and PRICKLE4 ( S1 Fig ) . To rule out possible off-target effects of siRNA , we generated a lentiviral vector expressing an siRNA-resistant , HA-tagged PHF5A mutant , PHF5A-HA-Escape ( Fig 1E ) . When endogenous PHF5A expression was disrupted by the PHF5A siRNA , two independent HeLa cell lines with stable PHF5A-HA-Escape expression ( Fig 1E , right panel ) did not show enhanced AAV9 vector transduction ( Fig 1F ) . Thus , the increased expression from AAV9 vector by the PHF5A siRNA is PHF5A-specific , but not due to off-target effects . The AAV CMV-Luc vector construct used in the library screening contained a human beta globin intron . To rule out the possibility of PHF5A modulating the CMV promoter activity or the intronic unit , we first replaced the CMV promoter and intron sequence in the AAV vector genome with an intron-less retroviral SFFV promoter . Disruption of PHF5A increased transduction by multiple AAV serotypes ( Fig 2A ) , indicating that the PHF5A-mediated restriction was independent from internal promoters or receptors used by AAV vectors . Likewise , knocking down PHF5A was effective at increasing AAV vector transduction in other cell types , including A375 melanoma cells and primary cardiac fibroblasts ( Fig 2B ) . Next , we examined the influence of PHF5A ablation on multiple stages of AAV vector transduction . No notable effects were observed on AAV cellular or nuclear entry ( Figs 2C and S2 ) . Additionally , approximately 30% of total AAV DNA detected was DNase-resistant at 24 hours post infection ( p . i . ) ( Fig 2D ) , indicating that PHF5A does not affect uncoating process of AAV vectors . Southern blot analysis demonstrated no notable increase in double-stranded-monomers in cells pretreated with the PHF5A siRNA ( Fig 2E ) . Upon transduction with a GFP-expressing self-complementary AAV ( scAAV ) vector , which does not rely on second-strand synthesis for transgene expression , we found significant increases in GFP-expressing cell populations in HeLa cells treated with the PHF5A siRNA ( Fig 2F ) . These results indicate that PHF5A blocks the process of AAV vector transduction after second-strand synthesis . We then explored the effects of PHF5A disruption on the transcription of AAV9 CMV-Luc vector . Northern blot analysis showed that pretreatment with the PHF5A siRNA increased the levels of luciferase-specific transcripts ( Fig 2G and 2H ) , suggesting that PHF5A affects the step before translation . When HeLa cells were transfected with the AAV vector genome plasmid , pAAV CMV-Luc , or single-stranded AAV vector genomic DNA from purified AAV vector particles , PHF5A ablation caused no increase in luciferase expression of transfected viral genome ( Fig 2I ) . Together , this suggests that PHF5A acts to restrict AAV vector transduction somewhere between AAV second-strand synthesis and the transcription of the AAV vector transgene . It also appears that PHF5A does not directly target AAV vector genome . Additionally , introduction of disruptive mutations in any of the three GATA-type zinc finger motifs in PHF5A led to the loss of anti-AAV activity ( S3 Fig ) . PHF5A has been reported to interact with various proteins , including the U2 snRNP proteins , SF3B1 , SF3B2 , SF3B3 [34 , 35 , 36] , U2AF1 , ATP-dependent helicases EP400 and DDX1 , and arginine-serine-rich domains of splicing factor SFRS5 [36] . Additionally , through co-immuno-precipitation of HA-tagged PHF5A , we identified potential PHF5A-interacting proteins , including FUS , EEF1 , EEF2 and HIST1H4B . To further understand the underlying mechanism , we assessed the effects of disrupting those proteins on expression from AAV vectors . After verification of reduction in corresponding transcripts upon transfection of specific siRNAs ( S4A Fig ) , siRNA-treated cells were infected with AAV9 CMV-Luc vectors at 24 hours post transfection , with luciferase activity assayed 48 hours p . i . Ablation of U2 snRNP components and U2 snRNP-associated factor ( U2AF1 ) resulted in a substantial increase in luciferase activity relative to HeLa cells pre-treated with a control siRNA ( Figs 3A and S4B ) . Disruption of HIST1H4B , one of histone H4 genes , also showed a modest increase , while ablation of other factors showed no notable effect . Of note , disruption of spliceosome proteins involved in other splicing steps , including SNRNP200 and PRPF31 , essential factors for U4/U6-U5 formation and function , did not increase the AAV vector transduction ( S4A and S4B Fig ) . These results suggest that PHF5A blocks AAV vector transduction through an interaction with U2 snRNP proteins and associated U2AF1 , independently of cellular RNA spliceosome function . Similar to the effects of PHF5A knockdown , disruption of U2 snRNP components or U2AF1 did not enhance the luciferase expression from an adenoviral vector or a transfected AAV vector plasmid , pAAV CMV-Luc ( Fig 3B and 3C ) . Taken together , we conclude that infectious AAV particles and all steps in intracellular trafficking pathway are essential for the restriction of transduction by U2 snRNP and associated proteins . To further confirm the role of U2 snRNP proteins in the restriction of AAV vectors , we assessed the influence of pharmacological inhibition of U2 snRNP on expression from AAV vectors . One drug we employed was meayamycin B , a potent SF3B1 inhibitor , synthesized according to the literature [37] . When HeLa cells were pre-treated with this drug at an increasing dose 3 hours before AAV9 vector infection , dose-dependent increases ( up to 49-fold ) in relative luciferase activity were seen ( Fig 3D ) . As we reported previously , treatment of HeLa cells with over 20 nM of meayamycin B for two days showed cytostatic effects [37] . A related SF3B1 inhibitor , meayamycin , also demonstrated a substantial increase in AAV transduction ( S4C Fig ) . In contrast , other drugs reported to block other splicing steps , including isoginkgetin at the prespliceosome/A complex stage [38] and 3-Aminophenylboronic acid ( ABA ) at the second stage ( excision of the lariat intron ) , did not show notable increases in AAV vector transduction ( Figs 3E and S4D ) although isoginkgetin blocked cellular mRNA processing ( Fig 3F ) . Of note , a low dose ( 5 nM ) meayamycin B treatment substantially enhanced AAV vector transduction without strongly affecting mRNA splicing ( Fig 3D and 3F ) , indicating that inhibition of the general splicing process is not necessary to enhance AAV vector transduction . Additionally , we found that pretreatment with the drug is not needed in order for it to enhance AAV vector infection ( Fig 3G ) . The largest increase in luciferase activity ( 464-fold ) was observed when cells were treated by meayamycin B 9 hours p . i . In contrast , treatment at 33 hours post p . i . showed relatively weak effects . To further map the optimal timing of U2 snRNP inhibition for AAV vector transduction , we treated AAV2 and AAV9 vector-infected HeLa cells with 5 nM meayamycin B at various time points and duration , and assessed luciferase activity at 3 days p . i . Treating with meayamycin B 3 hours p . i . and washing cells 1 , 2 or 3 days after receiving the drug resulted in similarly high levels of enhanced luciferase expression ( S4E Fig ) . Washout of meayamycin B for 48 hours after 3–24 hours of treatment did not strongly compromise its effects on AAV vector transduction . In contrast , the effects of meayamycin B on AAV vector transduction were impaired when drug was added either 24 or 48 hours p . i . ( S4E Fig ) . Thus , optimal enhancement of AAV vector transduction requires initiation of U2 snRNP inhibition prior to 24 hours post AAV vector infection . This indicates that U2 snRNP blocks AAV vectors at a particular post-entry step of viral infection , likely occurring before 24 hours p . i . Similar to PHF5A disruption , meayamycin B also enhanced the transduction by both single-stranded AAV and scAAV vectors through increasing the number as well as the fluorescent intensity of GFP-positive cells ( Fig 3H ) . In addition , dual treatments with meayamycin B and PHF5A or SF3B1 siRNAs showed no additional impact on the AAV vector transduction ( Figs 3I and S4F ) , verifying that meayamycin B and PHF5A/SF3B1 target a common pathway . On the other hand , dual treatment with a proteasomal inhibitor MG132 and meayamycin B showed a synergistic effect ( Fig 3J ) , indicating that the U2 snRNP proteins block AAV restriction independently from the proteasomal pathway . We also tested the influence of adenovirus co-infection on the U2 snRNP-mediated restriction of AAV vectors . Although human adenovirus 5 ( Ad5 ) co-infection alone enhanced AAV2 transduction 30-fold ( Fig 3J ) , dual treatments of Ad5 co-infection and SF3B1 knockdown showed minimal additive effects on AAV transduction ( Fig 3K ) . Similar results were observed with meayamycin and Ad5 dual treatments ( S4G Fig ) . These results strongly suggest that Ad5-mediated activation of AAV vector transduction is through U2 snRNP inhibition . Indeed , when the influence of Ad5 infection on subcellular localization of U2 snRNP was determined , Ad5 infection showed notable PHF5A and SF3B1 displacement in HeLa cells ( Figs 3L and S4H ) . On the other hand , meayamycin B treatment failed to increase AAV vector production , or rescue AAV vector production in the absence of the adenovirus helper plasmid ( S4I Fig ) , suggesting that U2 snRNP inhibition is not sufficient to provide adenoviral helper function during AAV production . Next , we assessed the interaction between PHF5A and AAV vector components . Upon pull-down of HA-tagged PHF5A from AAV vector-infected cells ( Figs 4A and S5 ) , total DNA in the pellets was assessed for AAV vector genomes . Quantitative real-time PCR detected significantly more AAV vector genome DNA in the HA pulldown from PHF5A-HA-over-expressing cells as opposed to control cells ( Fig 4B ) . We also tested the influence of heat-mediated conformational changes of viral capsids , which lead to the exposure of the hidden VP1 N-terminal and viral genomic DNA [39 , 40] , on the interaction with PHF5A . A three-fold increase in AAV genome copies was detected in the HeLa-PHF5A-HA pulldown when the cell lysates were incubated with pre-heated AAV2 CMV-Luc vector than non-heated vector ( Fig 4C ) . Those data indicate interaction of PHF5A with AAV vector genome , directly or indirectly . Next , immunohistochemistry was performed in order to identify the subcellular localization of PHF5A and AAV capsid . The A20 anti-AAV2 capsid antibody was used to detect AAV capsids ( Fig 4D ) . The majority of AAV capsid signals were found in the cytoplasm at 4 and 12 hours p . i . ( Fig 4D ) . Although endogenous PHF5A was predominantly found in the nucleus , especially in the nucleoli , of uninfected cells ( Fig 4D ) , a notable increase in cytoplasmic PHF5A signals was found in AAV2 vector-infected cells at 1 and 4 hours p . i . ( S6A Fig ) . Of note , AAV2 capsid signals frequently co-localized with the cytoplasmic PHF5A body signals ( Fig 4E ) . When HeLa cells were exposed to an empty AAV2 vector , similar cytoplasmic recruitment of PHF5A to AAV2 capsids was also evident ( Fig 4E , lower panels ) , suggesting that the PHF5A translocation ( or de novo recruitment in the endosome ) to AAV2 capsids is independent of AAV vector genome . Analysis of Z-stack images of AAV-infected cells at 4 hours p . i . showed comparable perinuclear and nuclear accumulation of AAV capsids between control and the PHF5A-siRNA treatment , further supporting that PHF5A does not affect AAV vector trafficking and nuclear import ( Figs 4F and S7 ) . When the subcellular localization of the major U2 snRNP component , SF3B1 , was assessed , nuclear speckles in the nucleoli and a diffuse cytoplasmic signal were observed ( S8A Fig ) . Although the widespread SF3B1 signals often overlapped with the AAV2 capsid signals , the diffuse cytoplasmic signal made it difficult to verify co-localization with AAV2 vector particles ( S8B Fig ) . To validate the AAV2 capsid and SF3B1 interaction , we employed the gradient technique to separate free-SF3B1 forms from particulated AAV capsid-associated SF3B1 ( Fig 4G ) , which was used to determine the interaction between retroviral capsids and a cytoplasmic retroviral restriction factor TRIM5alpha [41] . Upon ultracentrifugation through the 25% iodixanol layer , AAV2 capsid proteins VP1 , VP2 and VP3 ( 87 , 72 , 62 kDa ) were detected in the pellets ( Fig 4H ) . When the same samples were probed for SF3B1 ( 175 kDa ) , endogenous SF3B1 was seen in the input and upper layer samples of untreated HeLa cells ( Fig 4H ) . In AAV2-treated lysates , additional SF3B1 bands were seen in the pellet ( Fig 4H , S8C and S8D Fig ) . When the co-precipitation of PHF5A with AAV2 capsid was assessed , no intact PHF5A protein bands ( 15 kDa ) were detected after incubation at 4°C for 1 hour , likely due to its instability ( Fig 4H ) . Instead , multiple high molecular weight signals were detected in the input and top layer samples by anti-PHF5A and anti-HA antibodies ( Figs 4H and S8D ) . Notably , high molecular weight PHF5A signals were detected in the pellet of AAV2-treated samples but not in the pellet of untreated samples . It is possible that PHF5A is modified ( or modifies other proteins ) upon interaction with AAV components . To further map the responsible region for the interaction between SF3B1 and AAV2 capsid , we performed the same experiments using empty AAV capsid , made with the VP3 protein alone . The VP3-only capsid was able to enrich SF3B1 in the pellets ( Fig 4I ) . In contrast , multiple attempts to enrich SF3B1 or PHF5A through pulling down non-assembled VP1 proteins were unsuccessful ( Fig 4J ) . These results suggest that U2 snRNP proteins interact with the AAV2 capsid structure within the VP3 region , but not AAV2 vector genomic DNA or non-assembled AAV2 VP1 protein . Finally , we tested the ability of meayamycin B to boost AAV transduction in various cell types , relevant to gene therapy applications . When primary pancreatic islets were transduced with AAV8 CMV-GFP and treated with 2 nM meayamycin B 3 hours p . i . , there were increased numbers of GFP expressing cells in drug treated mouse islets ( Fig 5A ) . When primary human pancreatic islets were infected with AAV2 or AAV9 CMV-Luc vectors and treated with 0 , 2 , 5 , or 20 nM meayamycin B at 7 hours p . i . , we found dose-dependent increases in luciferase expression in AAV2 and AAV9 infected cells ( Fig 5B ) . Likewise , meayamycin B treatment increased AAV2 and AAV9 transduction of primary neonatal rat cardiomyocytes as well as porcine hepatocytes ( Fig 5C and 5D ) . These results demonstrate that meayamycin B enhances AAV vector transduction of a variety of cell types from different host species . Although we typically observed no notable toxicity in primary cells treated with 5 nM meayamycin B , prolonged treatment with over 10 nM meayamycin B showed anti-proliferative effects as we reported previously [42] . Since meayamycin B is rapidly cleared from circulation by unknown mechanism ( s ) [42] , we were unable to evaluate drug doses high enough to test the impact on AAV vector transduction in vivo .
Here , we have demonstrated that PHF5A and U2 snRNP proteins , such as SF3B1 , restrict AAV vector transduction through recognition of incoming AAV capsids . Of particular relevance to gene therapy applications , genetic and pharmacological inhibition of PHF5A or U2 snRNP-associated proteins strongly increased the transduction efficiency of AAV vectors . Thus , transient suppression of U2 snRNP or designing AAV vectors to avoid this restriction can provide a novel strategy to achieve efficient AAV vector transduction with reduced vector doses , which in turn could lead to improved safety profiles for AAV-mediated gene therapy applications . Several strategies have demonstrated the potential to enhance AAV vector transduction efficiency , including treatments with genotoxic agents [43 , 44 , 45] , adenoviral E1b55k/E4orf6 proteins [14] , a specific EGFR protein tyrosine kinase inhibitor ( Tyrphostin-23 ) [46] , and proteasome inhibitors [10 , 11] . A major effect of the genotoxic treatments , such as hydroxyurea and topoisomerase inhibitors , is improved double-strand synthesis of the input vector genome [8 , 9 , 47] . In contrast , the adenoviral proteins degrade the cellular Mre11 repair complex ( MRN ) to promote AAV vector transduction as well as provide crucial helper functions for wild-type AAV replication [14] , although a recent study suggests a role for MRN in gene expression [15] . Tryphostin-23 dephosphorylates FKBP52 , a protein binding to the viral single-strand DNA , and improves viral second-strand DNA synthesis [46] and intracellular trafficking of AAV vectors [48] . Importantly , Tryphostin-23 does not show a synergistic effect with the proteasome inhibitor MG132 , suggesting that both drugs target a common step of AAV vector transduction [12 , 48] . We found U2 snRNP inhibition had no notable effect on AAV vector second strand synthesis . Although we initiated the screening of the study using a commercially available siRNA library targeting known and putative proteasomal pathway proteins , MG132 treatment showed an additive effect with SF3B1 inhibition , suggesting U2 snRNP inhibition and MG132 work on distinct pathways . In contrast , dual treatments with Ad5 co-infection and SF3B1 inhibition showed no additive effect , suggesting a common target shared by Ad5 and SF3B1 inhibition . Since Ad5 infection induced PHF5A and SF3B1 displacement , it is plausible that Ad5 co-infection increases expression from AAV vectors , at least in part , through U2 snRNP inhibition . Dissection of the mechanism by which PHF5A and U2 snRNP components block AAV vector transduction might allow rational design of next generation AAV vectors that can potentially circumvent this host restriction machinery . Based on the following observations , we conclude that U2 snRNP restricts AAVs at an early stage of infection . First , optimal enhancement of AAV vector transduction required U2 snRNP inhibition at an early time point post AAV vector infection ( 3–24 hours p . i . ) , while washout of a U2 snRNP inhibitor for the following 2 days did not impair the effects . Thus , short-term U2 snRNP suppression appears to change the fate of AAV vectors up to 2 days post-incubation . Secondly , U2 snRNP inhibition showed no enhancing effect when purified AAV vector genomic DNA or AAV vector genome plasmids were introduced by transfection . Third , although PHF5A was predominantly found in the nuclei in uninfected HeLa cells , we found frequent co-localization of AAV vector particles and PHF5A signals , both in the nucleus and the cytoplasm of cells exposed to AAV vectors . We speculate that newly synthesized PHF5A likely interacts with incoming AAV capsids in the cytoplasm . Finally , pull-down and capsid co-precipitation assays using AAV vectors and empty AAV particles indicate that PHF5A and SF3B1 interact with the capsid , likely mediated by domains within the VP3 region . Of note , the use of heated AAV particles , which leads to exposure of the hidden VP1 N-terminal and viral genome release [39 , 40 , 49] , increased co-precipitation of capsid-associated AAV genome by PHF5A pull-down , or SF3B1 co-precipitation by the capsid . Consistent with these observations , previous studies have demonstrated the majority of viral DNA can remain associated with the capsid upon thermally induced DNA release [40] . A recent study has also implicated AAV capsid proteins in playing a role in second strand synthesis as well as the transcription of vector genomes [50] , supporting prolonged association of AAV capsid proteins with vector genomes at the time of transcription in the nucleus . Taken together , our results strongly support the notion that direct interaction of PHF5A and U2 snRNP components in a cooperative fashion with conformationally altered AAV capsids and the exposed vector genome blocks subsequent transcription . Although the exact mechanism is currently under investigation , one potential path being explored hinges on the involvement of U2 snRNP proteins in chromatin regulation . For instance , Isono et al . [51] have reported the essential role of SF3B1 ( and likely other U2 snRNP proteins ) in mammalian polycomb-mediated epigenetic silencing of homeotic genes . Sudemycin E , a U2 snRNP inhibitor , has also been shown to cause changes in histone modifications [52] . SF3B1 and SF3B2 are also found to associate with the histone H3 tails [53] . The ability of PHF5A and SF3B proteins to recruit additional factors to the AAV capsid and its associated vector genome is currently unknown , but if true these findings would provide further insight into the mechanism of host restriction . Further insight into the latter mechanism can potentially be derived from earlier reports that suggest that the splicing machinery is significantly remodeled during host cell infection by helper viruses such as adenoviruses [54] . As outlined earlier , the spatial organization of host splicing factors into distinct clusters within the nucleus appears to be regulated during adenoviral infection [1 , 55] . Similarly , herpes simplex virus infection induces snRNP-containing bodies [56] through interaction between IE63 protein ( ICP27 ) with SF3B2 ( SAP145 ) [57] . Thus , it is tempting to speculate that wild type AAV might have evolved to exploit the mislocalization/sequestration of splicing factors during helper virus co-infection , while recombinant AAV are unable to evade such host restriction factors in the absence of helper viruses . It is also possible that U2 snRNP plays a role as a broad spectrum antiviral factor , while helper viruses have evolved to counteract this restriction through sequestration of snRNP proteins . Based on the aforementioned reasons , our current working model is the U2 snRNP recognition of incoming AAV capsid , leading to subsequent block of AAV transcription . However , some observations suggest potential U2 snRNP-mediated AAV restriction at a late stage of transduction . For instance , at very late time points in infection in cell cultures , there was still a substantial enhancement in AAV vector ( 100-fold at 24 hr and 20-fold at 33 hr p . i . ) . At this late time point , most of the genomes are considered to be in the nucleus , and it is less likely that U2 snRNP can target incoming AAV capsid . One plausible explanation is that U2 snRNP can also target AAV genome-associated capsid in the nucleus for blocking AAV vector expression . Another point is on our Northern blot analysis of vector transcripts upon PHF5A knockdown . We found a notable increase in cytoplasmic AAV transcripts , but lesser degree in nuclear transcripts . Thus it remains possible that U2 snRNP can also target the nuclear export/cytoplasmic accumulation of AAV transcripts . Another caveat of our experimental system was the use of rapidly dividing cells , where some , or even the majority , of vector genomes can be lost at later time points . In addition to mechanistic analysis , we have compared FR901464 analogs and herboxidiene , and have identified that meayamycin B is the most potent SF3B1 inhibitor [58] . Importantly , treatment with meayamycin B substantially enhanced AAV vector transduction in various clinically relevant cell types , including primary cardiomyocytes , pancreatic islets and hepatocytes . Thus , pharmacological inhibition of U2 snRNP components may provide a novel strategy to improve AAV vector transduction . Unfortunately , however , intravenous administration of meayamycin B leads to rapid clearance , likely due to absorption , distribution , metabolism and/or excretion [42] . Additionally Meayamycin B also has a potent anti-proliferative effect at higher doses [37 , 58] . These features present a barrier to immediate in vivo applications of meayamycin B for improved AAV gene delivery . Nevertheless , since a low dose meayamycin B substantially increased AAV vector transduction without strongly affecting host RNA splicing in vitro , designing a novel U2 snRNP inhibitor with reduced cytostatic effects and in vivo stability may allow co-administration of the inhibitor with AAV vectors for improved AAV gene therapy with reduced vector doses . In conclusion , we demonstrate that the U2 snRNP spliceosome inhibits AAV vector transduction and genetic/chemical modulation of this machinery improves transduction efficiency . This finding may lead to approaches that might help reduce AAV vector doses in clinical applications . Further understanding the underlying mechanism would provide novel insights into host-virus interactions and could inform the rational or combinatorial design of next generation AAV vectors with improved transduction efficiency and safety profile .
Primary human islets were obtained through the Integrated Islet Distribution Program ( IIDP ) and the use of the cells was approved by the Mayo Institutional Review Board ( IRB10429 ) . All animal experiments were conducted according to the National Institute of Health guidelines and approved by the Institutional Animal Care and Use Committee ( IACUC A33214 and IACUC A9014 ) . pAAV-CMV-Luc vector genome construct , which drives firefly luciferase expression by a CMV internal promoter followed by the human beta globin intron , was described previously [24] . pAAV-SFFV-Luc was generated by replacing the CMV promoter and the beta globin intron region by Mlu1-BamHI with the intron-less SFFV retroviral promoter from a lentiviral vector plasmid , pSIN-Luc . The HA-tagged wildtype PHF5A-expressing lentiviral vector , pSIN-PHF5A-HA , was constructed by amplifying the human PHF5A ORF ( GenBank accession number BC075808 ) in pCMV-SPORT6 ( OpenBiosystems , MHS1010-97228317 ) by primers with a 3’ hemagglutinin ( HA ) tag , followed by cloning into the BamHI and NotI sites of a pHR-SIN CSGW PGK Puro ( gift from Prof . Paul J . Lehner ) . Site-directed mutagenesis was performed to generate pSIN-PHF5A-Escape , with three point mutations in the PHF5A siRNA #1 targeting site . Further site-directed mutagenesis was performed to generate zinc finger mutant vectors , pSIN-PHF5A-HA-Esc-C46A/C49A , C58A/C61A and C72A/C75A ( zinc fingers 3 , 1 and 2 mutants , respectively ) . Primers used in the cloning are 5’-BamHI-F , 5'-GTCGGATCCGCCACCATGGCTAAACATCATCCTGA; 3’-NotI-HA-R , 5'-GGAGCGGCCGCTCAGGCGTAGTCAGGCACGTCGTAAGGATACCTCTTCTTGAAGCCGTATT; PHF5A-Escape-F , 5'-GCCGCAAGCAGGCAGGGGTGGCCATCGGAAG; PHF5A-Escape-R , 5'-CTTCCGATGGCCACCCCTGCCTGCTTGCGGC; PHF5A-ZF1m-F , 5'-ACCAGGGGCGCGCTGTGATCGCTGGAGGACCTGGGG; PHF5A-ZF1m-R , 5'-CCCCAGGTCCTCCAGCGATCACAGCGCGCCCCTGGT; PHF5A-ZF2m-F , 5'-GGTCTCTGATGCCTATTATGCTAAGGAGGCCACCATCCAGG; PHF5A-ZF2m-R , 5'-CCTGGATGGTGGCCTCCTTAGCATAATAGGCATCAGAGACC; PHF5A-ZF3m-F , 5'-GGTGCGCATAGCTGATGAGGCTAACTATGGATCTTACCAG; PHF5A-ZF3m-R , 5'-CTGGTAAGATCCATAGTTAGCCTCATCAGCTATGCGCACC . HeLa ( ATCC ) , 293T ( ATCC ) , A375 ( ATCC ) and primary human cardiac fibroblast cells ( ScienCell Research Laboratories ) were cultured in Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum ( FBS ) ( GIBCO ) and antibiotics ( penicillin 100 U/mL and streptomycin 100 μg/mL ) ( Corning Cellgro ) . HeLa cells stably expressing a series of PHF5A mutants were generated by transduction of corresponding lentiviral vector , followed by puromycin selection . Human islets were obtained through Integrated Islet Distribution Program and cultured in RPMI1640 medium supplemented with 10% FBS and antibiotics . Murine islets were harvested through intraductal collagenase perfusion and enzymatic digestion of the pancreas as previously described [59] , and maintained in RPMI1640 medium supplemented with 10% FBS and antibiotics . Porcine hepatocytes were isolated from 15–20 kg pigs by a 2-step collagenase perfusion technique as previously described [60] , and cultured in DMEM medium supplemented with 10% FBS , 10mM HEPES and antibiotics . Primary cardiomyocytes were isolated from newborn Dahl salt-sensitive rats using the Neonatal Cardiomyocytes Isolation System ( Worthington , Lakewood , NJ ) according to the manufacturer’s instruction . Beating cardiomyocytes were plated in gelatin/fibronectin-coated plates in DMEM medium supplemented with 10% FBS . Helper-free AAV vectors were produced by transfection of three plasmids as described previously [61] . Briefly , 293T cells were transfected with three plasmids , including pHelper ( Stratagene ) , one of the RepCap-expression plasmids ( pRep2Cap2 , pRep2Cap6 , pRep2Cap9 , or pRep2Cap8 , kindly provided by Dr . James Wilson ) and a transfer vector plasmid ( pAAV-CMV-Luc , pAAV-SFFV-Luc , pAAV-CMV-Emerald GFP , or pScAAV-CMV-GFP [62] . pScAAV-CMV-GFP plasmid was kindly provided by Dr . R Jude Samulski through the National Gene Vector Biorepository . The resulting vectors were gradient purified using iodixanol ( Optiprep Density Gradient Medium , SigmaAldrich ) , desalted and concentrated using Amicon Ultra-15 100k filtration ( Amicon , Billerica , MA , USA ) and resuspended in PBS . The genome copies ( gc ) of concentrated AAV vector stocks were determined by quantitative PCR as described previously [24] . Luciferase- or shRNA-carrying lentiviral vectors were produced as described previously [63] . Human adenovirus 5 ( ATCC VR1516 ) was purchased from ATCC . Unless otherwise stated , no helper virus co-infection was used during AAV vector transduction . Human siGENOME Ubiquitin Conjugation Subsets #1 ( 89 genes ) , #2 ( 115 genes ) and #3 ( 396 genes ) , a SMARTpool siRNA Library in Reverse Transfection Format ( RTF ) covering 600 gene targets , were purchased from Thermo Fisher Scientific . According to the provided RTF protocol , 5 , 000 cells/well HeLa cells were seeded , followed by AAV9 CMV-Luc infection at a multiplicity of infection of 100 ( gc/cell ) . 48 hours after infection , luciferase assay was performed using the ONE-Glo Luciferase Assay System ( Promega ) . HeLa cells were seeded in a 96-well plate at 5 , 000 cells/well for one day . Cells were then transfected with 0 . 5 μL of 10 μM siRNA using DharmaFECT Transfection Reagents ( Thermo Fisher Scientific ) according to the manufacturer’s instruction . Following siRNAs were used; control siRNA ( siKrt1 5 SI02636732 from Qiagen ) , siPHF5A#1 and #2 ( PHF5A 6 SI04210892 and 7 SI04310621 ) from Qiagen , siGenome Smart Pool siRNAs for PHF5A-interacting proteins;—siHIST1H4B ( NM_003544 , cat# M-011463-00 ) , siU2AF1 ( NM_001025203 , cat# M-012325-01 ) , siSF3B1 ( NM_001005526 , cat# M-020061-02 ) , siSF3B2 ( NM_006842 , cat# M-026599-03 ) , siSF3B3 ( NM_012426 , cat# M-020085-01 ) . Twenty four hours post transfection , cells were infected with luciferase- or GFP-expressing vectors for 2 days . Meayamycin B was described previously [58] . Isoginkgetin was purchased from Millipore and resuspended in DMSO . 3-Aminophenylboronic acid was purchased from Sigma and resuspended in DMSO . cDNA synthesis was performed with one μg RNA using RNA to cDNA EcoDry Premix ( Clontech ) . Primers used were as follows: Fig 1B PHF5A ( cat# Hs00754435_s1 , Invitrogen ) ; Fig 2C luciferase ( cat# Mr03987587_mr , Invitrogen ) ; Fig 4B and 4C AAV polyA ( Forward 5’-CCTGGGTTCAAGCGATTCTC-3’ , Reverse 5’-AGCTGAGCCTGGTCATGCAT-3’ , Probe 5’-/FAM/TGCCTCAGCCTCCCGAGTTGT , IDT ) . Western blotting was performed as described previously [64] . Following primary antibodies were used: rabbit anti-PHF5A ( Sigma HPA028885-100UL ) 1:50 , rat anti-HA clone 3F10 ( Roche 11867423001 ) 1:250 , rabbit anti-VP1 , 2 , 3 ( American Research Products , Inc . 03–61084 ) 1:250 , mouse anti-SAP155 ( SF3B1 ) ( MBL International D221-3 ) 1:250 . ImageJ software was used to quantify Western blots from immunoprecipitations . DIG High Prime DNA Labeling and Detection Starter Kit II ( Roche ) was used for Southern blotting to detect the luciferase DNA in the AAV vector genome . A luciferase DNA fragment from pSIN-Luc was labeled according to the manufacturer’s instruction . HeLa cells were seeded in a 6-well plate at 200 , 000 cells per well , followed by transfection with control or PHF5A siRNAs . 24 hours post transfection , AAV9 CMV-Luc ( MOI 8 x 104 ) was added for 1 , 3 or 6 hours . Cells were harvested in lysis buffer for nuclear fractionation . Nuclear lysates were purified as in “Cell fractionation and analysis of nuclear rAAV genomes” section . 2 μg of DNA sample was run on a 2% agarose gel without ethidium bromide at 50V for 1 . 5 hours . The gel was prepared for transfer in the following washes with rocking: 0 . 25N HCl 10 min , rinse ddH20 , denaturation buffer ( 0 . 5N NaOH , 1 . 5M NaCl ) 15 min , denaturation buffer 30 min , neutralization buffer ( 0 . 5M Tris , 1 . 5M NaCl ) 15 min , and neutralization buffer 30 min . The gel was then blotted overnight by capillary transfer with 10x SSC on a positively charge nylon membrane ( Roche ) . DNA was fixed to the membrane by UV-crosslinking and the luciferase probe was hybridized to the DNA overnight at 43 . 5°C . The membrane was washed and developed according to the protocol ( Roche ) . Cells were prepared for Northern blot by seeding at 200 , 000 cells per well in a 6-well plate , transfected with control and PHF5A siRNAs , followed by transduction with AAV9 CMV-Luc vector ( MOI 4 x 105 ) . 36 hours post transduction cells were harvested and nuclear and cytoplasmic RNA was isolated using the PARIS kit ( Ambion ) . 1 μg RNA was run on a formaldehyde gel , washed , and blotted by capillary transfer overnight according to the DIG Northern Starter Kit ( Roche ) . The RNA was fixed to the membrane by UV-crosslinking and the DIG-labeled luciferase DNA probe from Southern blotting was incubated with the pretreated membrane overnight at 50°C . The membrane was washed and developed according to the manufacturer’s instructions . Cells were resuspended in cytoplasmic lysis buffer ( 1 . 3M sucrose , 20mM MgCl2 , 4mM Tris , 4 . 2% Triton X-100 ) and incubated on ice for 10 min . The lysates were homogenized using a 21-guage needle and syringe , spun at 14 , 000 rpm for 15 min at 4°C and the supernatant ( cytoplasmic fraction ) was collected . The pellet ( nuclear fraction ) was washed with PBS . RNA was eliminated by RNaseA ( 200U/mL ) treatment . A Qiagen QIAamp DNA Mini kit was used to further purify the DNA . Quantitative real-time PCR was performed to determine AAV luciferase genomic copy numbers . To assess encapsidated AAV genomes in the nucleus , the above procedure was followed with the addition of DNase ( Invitrogen ) treatment at 37°C for 30 min ( both control and DNase treated samples ) at the beginning of DNA purification and quantitative real-time PCR detection . AAV genomic DNA was isolated using the QIAamp DNA mini kit following the provided Protocols for Viral DNA ( Qiagen ) . The genomic DNA was isolated from 4 x 1011 vector genomes of purified AAV9 CMV-Luc vector . HeLa cells transfected with control or PHF5A siRNAs were transfected with the purified AAV genomic DNA ( 0 . 1 μg/well ) by FuGENE6 ( Promega ) , and luciferase expression was analyzed 48 hours after viral DNA transfection . Semi-confluent HeLa cells with or without stable overexpression of the HA-tagged PHF5A were infected with AAV2 or AAV9 CMV-Luc ( MOI 4 x 105 ) in a 6-well plate for 6 hours at 37°C . Cells were then harvested on ice in RIPA buffer containing protease inhibitor , followed by pull-down with 20 μL anti-HA agarose beads ( Pierce ) . After 15 cycles of washing , pellets were resuspended in 0 . 5 mL PBS and split into 2 aliquots . One aliquot was used for Western blotting of AAV capsid proteins , and the other was used for the isolation of total DNA by QIAamp DNA Mini kit for RT-qPCR detection of AAV genomic DNA . For pull-down assay using heated AAV particles , 100 μL cell lysate was combined with 3 x 1010 gc of AAV2 CMV-Luc vector particles that were unheated or pre-heated for 30 min at 65°C , followed by precipitation by anti-HA agarose as above . The Lab-TekII 8-well chamber slides ( Thermo Fisher Scientific ) were pretreated for 5 min with poly-d-lysine ( Sigma , 0 . 1 mg/mL ) . HeLa cells were plated at 1 x 104 cells/well and were infected with AAV2 CMV-Luc ( 4 x 1010 gc/well ) or empty AAV2 ( 25 μl/well ) at 37°C for 5 min , 4 hours , or 12 hours . Cells were fixed in 4% paraformaldehyde for 20 min at room temperature , permeabilized with 0 . 3% Triton X-100 for 15 min , and blocked with 5% FBS/PBS for 30 min . Primary antibody was added , and cells were incubated for 1 . 5 hours at room temperature in a humidified chamber . Secondary antibody followed according to the same procedure . Then cells were washed three times with PBS , treated with DAPI ( Sigma , 1:2000 ) for 1 min , washed three times with PBS , and mounted with Dako fluorescent mounting media . Confocal microscopy was performed on an LSM 780 confocal microscope ( Zeiss ) . The following primary and secondary antibodies were used for immunocytochemistry of uninfected and AAV infected HeLa cells: anti-AAV particles ( A20 ) mouse monoclonal antibody ( American Research Products ) at 1:100 followed by FITC-conjugated donkey anti mouse IgG ( H+L ) ( Jackson 715-095-151 ) 1:500; rabbit anti-PHF5A ( Sigma HPA028885-100UL ) 1:250 and Alexa Fluor 594-conjugated donkey anti-rabbit IgG ( H+L ) ( Invitrogen A-21207 ) 1:2000; rabbit anti-phospho-SF3B1 ( MBL International PD043 ) 1:500 and Alexa Fluor 594-conjugated donkey anti-rabbit IgG ( H+L ) 1:500 . AAV2 CMV-Luc vectors ( 5 x 1010 gc/tube ) and purified AAV2 empty , VP3 only particles were left unheated or pre-heated at 65°C for 30 min and placed on ice . HeLa and HeLa-PHF5A-HA cells were harvested by incubating 1 well of a 6-well plate with 500 μL RIPA buffer supplemented with protease inhibitors on ice for 10 min . Cells were harvested by scraping , homogenized using a 21-guage needle and syringe , and spun for 5 min at 13 , 200 rpm . 400 μL of the cell lysate was added to the virus and samples were rotated for 1 hour at 4°C . The 25% iodixanol solution was prepared using 3 . 2 mL 1x PBS , 2 . 8 mL 9:1 Optiprep to 10x PBS , and 0 . 15% phenol red . 30 μL of the lysate virus mixture was removed and used as an input . The remaining lysate-virus mix was layered on top of 0 . 5 mL 25% iodixanol . These samples were spun at 4°C for 1 hour at 14 , 000 rpm . After spinning the clear upper layer , red lower layer , and pellet were harvested for Western blot . The following antibodies were used in this experiment: rabbit anti-PHF5A ( Sigma HPA028885 ) 1:100 , rat anti-HA clone 3F10 ( Roche 11867423001 ) 1:500 , rabbit anti-VP1 , 2 , 3 ( American Research Products , Inc . 03–61084 ) 1:250 , mouse anti-SF3B1 ( MBL International D221-3 ) 1:500 , rabbit anti-phospho-SF3B1 ( MBL International PD043 ) 1:400 , rabbit anti-histone H2B ( Cell Signaling #8135 ) 1:1000 , and rabbit anti-histone H3 ( Cell Signaling #4499 ) 1:1000 in blocking buffer . To note , the membrane that received phospho-SF3B1 primary antibody was blocked in 5% BSA/PBS + 0 . 05% Tween-20 . Antibodies were diluted in 1x TBS + 0 . 001% Tween-20 . HeLa cells at 80% confluency were treated with pre-mRNA splicing inhibitors at the following concentrations: 3-Aminophenylboronic acid ( 5mM and 1mM ) , Isoginkgetin ( 25uM and 12 . 5uM ) , and meayamycin B ( 10nM and 5nM ) . Eight hours post drug RNA was isolated using TRIzol ( Invitrogen ) , and cDNA synthesis was performed with one μg RNA using RNA to cDNA EcoDry Premix ( Clontech ) . cDNA was amplified by KOD Hot Start DNA Polymerase ( EMD Millipore ) using primers for MAPT exon 10 5’-AAGATCGGCTCCACTGAGAA-3’ and 5’-ATGAGCCACACTTGGAGGTC-3’ . | Mammalian cells have developed diverse innate/intrinsic immune strategies to counteract viral infections . Post-entry infection steps of a single-strand DNA virus , adeno-associated virus ( AAV ) , are subject to such restrictions . Here , we screened an siRNA library to identify a novel cellular factor involved in AAV restriction . We found PHF5A , a component of the U2 snRNP mRNA splicing factor , blocks expression from recombinant AAV vectors . Disruption of PHF5A expression specifically enhanced AAV vector performance . Moreover , genetic and pharmacological inhibition of other U2 snRNP proteins , but not spliceosome proteins involved in other splicing steps , strongly increased transgene expression from AAV vectors . Further study demonstrated that U2 snRNP proteins recognize incoming AAV capsids to mediate this cellular restriction at the step after second-strand synthesis . In summary , we identify the U2 snRNP spliceosome complex as novel host factors that effectively restrict recombinant AAV vectors . Considering frequent reorganization of host splicing machinery in DNA virus infections , it is conceivable that U2 snRNP plays a role as a broad spectrum antiviral factor and helper viruses have evolved to counteract this restriction through sequestration of snRNP proteins . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | An siRNA Screen Identifies the U2 snRNP Spliceosome as a Host Restriction Factor for Recombinant Adeno-associated Viruses |
Dissolution of many plant viruses is thought to start with swelling of the capsid caused by calcium removal following infection , but no high-resolution structures of swollen capsids exist . Here we have used microsecond all-atom molecular simulations to describe the dynamics of the capsid of satellite tobacco necrosis virus with and without the 92 structural calcium ions . The capsid expanded 2 . 5% upon removal of the calcium , in good agreement with experimental estimates . The water permeability of the native capsid was similar to that of a phospholipid membrane , but the permeability increased 10-fold after removing the calcium , predominantly between the 2-fold and 3-fold related subunits . The two calcium binding sites close to the icosahedral 3-fold symmetry axis were pivotal in the expansion and capsid-opening process , while the binding site on the 5-fold axis changed little structurally . These findings suggest that the dissociation of the capsid is initiated at the 3-fold axis .
Non-enveloped icosahedral viruses often contain binding sites for divalent cations , usually . The ions are typically bound between coat proteins or on the icosahedral symmetry axes . This is broadly observed in three plant virus taxa: the family Tombusviridae ( and an associate satellite virus ) , the genus Sobemoviruses and the family Bromoviridae [1]–[4] . Binding sites for calcium ions have also been found in bacteriophages of the Leviviridae family [5] , fish and insect viruses of the Nodaviridae family [6] and in the Picornaviridae family , e . g . several human rhinoviruses [7] . In many of the plant viruses it is possible to induce a conformational change in vitro by removing the ions , either by a chelating agent such as ethylenediaminetetraacetic acid ( EDTA ) or by exhaustive dialysis against deionized water . Ion-deprived virions reversibly expand on the order of 5–10% at neutral or slightly alkaline pH . In the swollen state internal parts of the virion as well as the RNA molecule may become susceptible to degrading enzymes [8] , [9] . Chelation of the metal ions is also required for synthesis of virus proteins in cell-free translation systems [9] . Only two low-resolution crystal structures of expanded virons are available: tobacco bushy stunt virus ( TBSV ) at 8 Å [10] and satellite tobacco necrosis virus ( STNV ) at 7 . 5 Å [11] . The radial increases are about 11% and 4% , respectively . In addition , an expanded cowpea chlorotic mottle virus ( CCMV ) virion was imaged with cryo-electron microscopy at 29 Å and interpreted using rigid body fitting of the high-resolution structures of the native proteins [4] . The dynamic nature of the swelling process as well as the limited resolution of swollen virus particles structures prompted us to perform a simulation study of the capsid of STNV , with and without bound , over one microsecond . The simulations allowed us to reproduce the swelling behavior upon removal of the calcium in silico and develop an atomistic description of the process . The T = 1 capsid of STNV consists of 60 identical coat proteins with one protein per icosahedral asymmetric unit . The coat protein is 195 amino acid residues long where residues 25–195 make up the main domain that constitutes the capsid shell . The virions readily crystallize and the major part of the coat protein has been resolved by X-ray crystallography [2] , [12]–[14] . The shell domain at the C-terminus folds as a -jelly roll similar to many other single-stranded RNA plant viruses . Residues 12–24 form a helical structure that together with the helices of two neighboring subunits form a short stalk that projects inwards into the central cavity around the icosahedral 3-fold axis . The first 11 residues at the N-terminus are disordered and cannot be detected in the electron density maps – in the simulations these residues were modeled as a helix as well . This N-terminal arm and the interior surface of the capsid are lined with positively charged residues that presumably interact with the single-stranded positive-sense RNA molecule [14] . The 1239 nucleotide long genome encompasses only one open reading frame that encodes the coat protein and hence STNV is dependent on the co-infection of a helper virus ( tobacco necrosis virus ) for copying its RNA genome . The capsid has three different types of binding sites ( Figure 1 ) . Type I is between two subunits close to the 3-fold symmetry axis . The protein ligands are the carboxyl groups of Asp194 and Glu25 as well as the main chain carbonyl oxygens of Ser61 and Gln64 . Type II is on the 3-fold symmetry axis 8 . 05 nm from the center of the virion . It is coordinated by the carboxyl groups of three Asp55 residues . Type III is on the 5-fold symmetry axis 9 . 04 nm from the center . This is coordinated by the main chain carbonyl oxygen of five Thr138 residues . In total the capsid can accommodate 92 ions ( 60 at type I sites , 20 at type II sites and 12 at type III sites ) . Simulations were performed of the capsid with and without at two different salt concentrations for one microsecond each ( Table 1 ) . The carboxyl groups of one of the three Asp55 residues at each of the type II calcium binding sites were protonated in the two simulations without , effectively simulating the capsid at a slightly acidic pH to mimic the conditions of the expanded capsid in the 7 . 5 Å crystal structure [11] . The RNA molecule was not included in our simulations since it cannot be modeled completely in the electron density maps [14] , [15] . The aim of this work was to probe the dynamic behavior of a virus capsid over timescales that are more than an order of magnitude longer than what has been reported from simulations of viruses previously [16]–[18] , and therewith to investigate the role of the structural calcium ions in initiating the dissolution of the satellite tobacco necrosis virus . We particularly looked into the structural features facilitating breaking up of the capsid associated with virus infection .
The capsids were stable and remained intact throughout all four trajectories . Removing the had a pronouced effect on the capsid radius . An increase in the radius of gyration ( ) of 2 . 6% in and of 2 . 4% in was found over the course of 1 . The two trajectories with bound ( and ) showed a weak tendency to increase in size , 0 . 78% and 0 . 58% respectively ( Table 1 and Figure 2 ) . The capsids retained their overall spherical shape with just some minor degree of elongation . Figures 3A and 3C emphasize the local anisotropy in the structural changes . The largest changes occurred in the parts of the shell close to the icosahedral 3-fold axes . This area accommodates four calcium binding sites and all of them have charged carboxyl groups as ligands . The charge repulsion induced the formation of small water-filled cavities between the proteins at this protein/protein interface ( Figure 4D ) . An analysis where the root mean square deviation ( RMSd ) from the crystal structure for protein dimers , trimers and pentamers was computed is presented in Table 1 . The trimers have clearly larger than average RMSd , whereas dimers and pentamers form very stable complexes . This effect is more pronounced in the simulations without . The secondary structure elements and the overall fold of all the coat proteins were stable throughout the simulations . The N-terminal arm was the most flexible element ( Figures 1 , 3 and 5A ) . The -helix in the N-terminal arm observed in the crystal structure was stable: at the end of the simulation the number of -helical residues in the N-terminal arm was close to 11 , slightly lower in the two trajectories without bound than the simulations with bound ( Table 1 ) . Residues 1–11 did not show any propensity to stay in a helical conformation . These residues were modeled as a helix in the starting structure , but they progressively lost that structure . This might be a result of the absence of the RNA molecule since the addition of molecules that mimic the phosphate backbone of RNA has been shown to promote formation of helices in the positively charged N-terminus of CCMV , that presumably plays a similar RNA-binding role [19] , [20] . The number of intermolecular protein-protein hydrogen bonds was slightly lower in the STNV capsids without , in particular the number of hydrogen bonds between pairs of 2-fold and 3-fold related subunits decreased ( Table 1 ) . The atoms in the shell domain moved on average 0 . 27 nm in , 0 . 26 nm in , 0 . 15 in and 0 . 14 in predominantly due to the overall radial expansion of the capsid ( Figure 5A ) . The RMSd after fitting each protein to the crystal structure individually was small ( 0 . 1 nm ) apart from the termini and some of the loop regions ( Figure 5B ) . The flexibility was highest in the two termini , in the loops between secondary structure elements and in the short helix centered at residue Thr119 ( Figure 5C ) . The – loop ( using the same nomenclature as in [2] ) centered at residue Thr80 and the – loop centered at residue Leu180 show high flexibility both with and without bound ( Figure 5C ) . Both of these loops are located close to the 5-fold axis , facing the exterior of the capsid ( Figure 1 ) . The – loop at residue Asp55 , the – loop at residue Thr160 and the C-terminus on the other hand show an increased flexibility mainly in the two capsids that had no ( Figure 5C ) . These elements are all located close to the 3-fold axis and Asp55 is the ligand for the type II sites . When the ions were removed from the capsid , the type III calcium binding sites on the icosahedral 5-fold symmetry axis were populated by either ( only ) or water . had 7 out of 12 type III binding sites occupied by sodium ions throughout the simulation . The remaining type III sites as well as all the type III sites in the capsid of were occupied by one or two water molecules . The sodium ions were bound at the same position as the calcium ions – at the geometric center of the five Thr138 carbonyl groups – but half of the time , one of the five carbonyl groups pointed away from the 5-fold axis and engaged in hydrogen bonds with other water molecules . Water molecules bound to the type III sites were located on the 5-fold axis slightly exterior or interior to the binding site for cations such that they could make hydrogen bonding interactions with two or three carbonyl groups . In these cases the 5-fold symmetry of the protein subunits was broken with one ( when two water molecules were bound ) or two ( when one water molecule was bound ) carbonyl groups turned away and engaged in hydrogen bonds with water molecules from the bulk . The two types of calcium binding sites close to the icosahedral 3-fold symmetry axis were less stable than the type III sites upon removal as reflected in the higher RMSd and RMSf of residues close to the 3-fold axis ( Figure 5 ) . In all type II sites were occupied by one or two sodium ions subsequent to the equilibration simulation ( in which the protein was restrained from moving away from the crystal structure ) . However , at the end of the production simulation only 1 of the 20 sites was intact with all three carboxyl groups of Asp55 coordinating a sodium ion . The rest of the type II sites were broken up with the carboxyl groups facing other directions ( Figure 4D ) . The type I site also lost its structure in the calcium-free capsids . The four residues that contributed oxygen atoms to coordinate the calcium ion had high RMSf ( Figure 5C ) and did not retain their relative positions . The negatively charged carboxyl side chains of all the type I sites together bound approximately 30 sodium ions in an irregular fashion . The capsids were permeable to water in all trajectories . Assuming a homogeneous permeability across the entire surface of the capsid , an osmotic water permeability coefficient , , was calculated [21] . With bound there were about 10 , 000 permeation events in either direction and the capsid had an average permeability coefficient = cm/s . After removing the increased to about cm/s ( Table 1 ) . The water transport resulted in a net inflow of water in conjunction with the swelling . In order to detect structural features associated with water permeability , the crossing events were mapped onto the virus particle . For each water molecule traversing the width of the capsid shell successfully , the closest protein residue ( ) was determined for the water molecule half-way ( in time ) through , and statistics of permeation per residue were gathered . In the simulations with bound the permeation mainly occurred between the icosahedral 2-fold and 3-fold symmetry axis , at the junction between three subunits ( Figures 6A–C and 4 ) . The protein–water contacts suggest that this potential water pore is lined by five motifs: the short helix centered at residues 118–119 , residues 156–158 in the – loop , the end of the – loop , the beginning of the -strand and to some degree residues 25–30 close to the flexible loop connecting the N-terminal arm to the shell domain ( Figure 5D ) . In the simulations without bound the permeability increased at this site , as well as at the protein/protein interface at the 3-fold symmetry axis ( Figures 5D , 6D–F ) . If we consider the region around each icosahedral 3-fold axis to be a water pore , we can estimate the single pore permeability coefficient , , to be in the capsid with bound and without these ions ( Table 1 ) .
Four different crystals of STNV treated with EDTA were investigated by Montelius et al . , but the capsid expanded in only one of those [11] and that particular crystal diffracted to a resolution of 7 . 5 Å only . In our simulations we could observe a higher degree of fluctuations as well as loss of the icosahedral symmetry in the calcium-deprived capsids compared to the ones with bound calcium . Both higher flexibilty and a lower degree of symmetry would contribute to a lower crystal quality and could explain why there have not been any successful attempts to solve a high-resolution structure of an expanded capsid so far . The trajectories did not quite reach an equilibrium swollen state . The radius of the calcium-depleted capsid can be extrapolated to an “equilibrium radius” by fitting the curve ( Figure 2 ) to = − ( − ) . With this fit an equilibrium radius of is predicted , about a quarter of a percent higher than the value at the end of the simulation . The weak increase in the radius of the calcium-containing capsids is probably an artifact due to the lack of the RNA molecule and confirms the long-term unstable nature of the RNA-free capsid . Swollen STNV capsids can be returned to their native radius by lowering the pH [8] , suggesting that the mechanism behind the swelling is electrostatic repulsion of charged aspartate and glutamate ligands . A similar effect was deduced from electrostatics calculations of the native and expanded structures of the CCMV capsid [23] . The protonation state of the carboxyl groups is probably coupled to the magnitude of the expansion . The crystals of the expanded STNV formed at pH 6 . 5 and the expansion was only moderate , so the capsid may have been protonated at one of the calcium binding carboxyl groups . Since the type II site has the highest local density of negative charge , we decided to protonate one of the three carboxyl groups at each type II binding site in our simulations of the calcium-free capsid . Analytic ultracentrifugation measurements estimate that the STNV particle can expand up to 7% when treated with EDTA [8] . The crystal structure of the expanded virion showed a radial expansion of between 0 . 1–0 . 4 nm , equivalent to 1%–5% ( higher closer to the icosahedral 3-fold axis ) . This agrees well with what we observed in the simulations: an average increase in the of 2 . 5% ( Table 1 ) and peak RMSd values of the of the shell domain above 0 . 35 nm at the 2-fold subunit interface , e . g . residues 96 and 122 , and at the 3-fold axis , e . g . residues 25 and 195 ( Figure 5A ) . The osmotic water permeability of the capsid is comparable to lipid membranes , which usually have = cm/s [24] . The protein shell is thinnest at the 5-fold axis , but the calcium ion at the type III site is the one that is most difficult to chelate with EDTA [25] , something which was corroborated by spectroscopic measurements showing that the type III site has a remarkably high binding affinity to the analog ( nM ) [26] . In the simulations of the capsid without calcium , the coordinating cage of oxygen ligands was best preserved at the type III site . Sodium ions or water molecules replaced the divalent ion on the icosahedral 5-fold axis and obstructed the opening , resulting in minimal permeability even without . Instead , we observed extensive water permeation on the 3-fold axis and in a region close to the 3-fold axis between the 3-fold and the 2-fold axis ( Figure 4 ) . The cluster of four calcium binding sites around the 3-fold axis contains several carboxyl groups from aspartate and glutamate residues . Removing the calcium ions introduces a large amount of net negative charge that caused the subunits to move apart , creating water pockets at the 3-fold axes causing increased water permeability . If the entire region around the 3-fold axis is considered a water pore , the permeability of it is comparable to that of membrane proteins that function as water pores , e . g . mammalian aquaporins that have reported single pore permeability of [27] . The difference in size and triangulation number makes it difficult to compare the expanded structure of TBSV and STNV . The capsid of TBSV consists of 180 identical subunits in a T = 3 arrangement where each of the 60 icosahedral asymmetric units consists of three proteins in slightly different configuration . At the center of these three proteins , there is a so called quasi-3-fold axis that relates three approximately equivalent protein positions [28] . The six calcium binding sites of TBSV are located pairwise between pairs of subunits in the asymmetric unit and each site has five acidic residues from both proteins . The swollen TBSV was crystalized at pH 7 . 5 and the structure is about 7% larger than the native one . The most predominant structural change is that large openings appear between the quasi-3-fold and 2-fold related subunits [10] . The 3-fold axis of STNV resembles the quasi-3-fold axis of TBSV . The interfaces between the proteins around these axes contain six ( TBSV ) respectively four ( STNV ) binding sites ( Figure 1 ) , coordinated by carboxylic groups . In both capsids the largest structural differences between the expanded and native structures can be found here . Not all viruses that have calcium binding sites in the capsid show a swelling behavior . The rhinoviruses have a calcium binding site on the 5-fold axis [7] , but do not swell upon removal of these ions . Interestingly , this binding site show striking similarities to the type III calcium binding site in STNV . In both cases five backbone carbonyl oxygens point at the 5-fold axis where the ions are bound . The 5-fold axis is a region with high degree of symmetry constraints and putting an ion there solves the problem of fulfilling the symmetry at a very congested interface . We therefore propose that the carbonyl type of binding sites have a more structural role , while the carboxyl type of binding sites may play a role in the dissolution of the capsid upon infection . This would explain the low degree of structural change at the 5-five fold site in our simulation and their high propensity to bind other cations or water molecules , and this is in line with the relatively higher affinity for ions at these sites [25] , [26] . There are no atomic structures of the STNV genome available , although a recently published high-resolution structure shows short fragments of RNA [14] . RNA-free virus-like particles of the STNV coat protein have not been observed so far , but the coat proteins of similar viruses readily form empty shells [29] , [30] . Rather than inserting a modeled RNA structure into the capsid we decided to perform the simulations without RNA . The fact that RNA nevertheless may contribute to the stability of the capsid is evidenced from our simulations at physiological salt concentration , providing more shielding of electrostatic interactions . The radial expansion is less pronounced in this case ( Figure 2 ) and the number of protein-protein hydrogen bonds seems to increase slightly ( Table 1 ) , a typical “salting out” effect . Previous all-atom molecular dynamics simulations of capsids and virus-like particles focused on specific properties like the mechanical strength using force probes [17] , [18] or the effect of hydration on coherent diffraction from single virus particles [31] . It has become clear though that virus simulations are sensitive to the starting conditions , like a balanced amount of water on the inside , and they require long equilibration times [18] , [32] . Therefore we paid special attention to the preparation of the starting structures with an extensive equilibration protocol with part of the counter ions specifically added to the interior cavity to avoid a sudden influx of solvent ions that could disrupt the protein-protein interactions and by carefully balancing of the hydrostatic pressure inside and outside of the capsid . By doing control simulations with and be varying the ionic strength we can draw firm conclusions on the effect of removal on STNV . The simulations presented here illustrate the mechanism by which an entire virus capsid can transform from a closed configuration into an open one with significantly increased water permeability . The magnitude of the expansion in the simulations is in good agreement with experiments . The higher flexibility and the degradation of the icosahedral symmetry in combination explain why it has been difficult to crystallize expanded capsids . Our work strongly suggests that there are two types of calcium binding sites , playing different roles in the virus lifecycle . The binding site on the 5-fold axis has a more structural role and is less involved in the capsid expansion . The binding sites between 2- and 3-fold related subunits contain many charged carboxylic side chains . Dissolution of the capsid is initiated here due to the electrostatic repulsion between these residues , if the ions are removed .
The starting structures were prepared from the X-ray crystal structure of the coat protein of STNV ( PDB ID: 2buk ) [2] . Since residue 1–11 can not be resolved in the crystal structure , these were modeled as an -helix in a direction that did not cause steric clashes with neighboring proteins . The full capsid was generated by applying the icosahedral rotation-translation matrices in the PDB file . One ion was kept at each of the 92 calcium binding sites . The Amber99sb-ILDN forcefield [34] , [35] was used for the protein combined with TIP3P water [36] in a rhombic dodecahedron simulation box with a side of about 25 nm . Water molecules on the inside of the capsid were replaced with ions to obtain a neutral system ( ) . Additional and ions were added to obtain a system with an approximately physiological ion concentration of 150 mM ( ) . The calcium ions were removed , a proton was added to one Asp55 side chain at each of the type II calcium bindings sites and the number of counter ions was adjusted to obtain two neutral calcium-free systems ( and respectively ) . Each system consisted of roughly 1 . 2 million atoms ( Table 1 ) . The starting structures were energy minimized and subsequently the solvent was equilibrated for 10 ns while restraining the protein atoms and the calcium ions to the crystal coordinates with harmonic potentials with a force constant of 1000 . During the equilibration a 2 fs integration timestep was used and the neighbor lists were updated every timestep . Short range non-bonded Van der Waals ( Lennard-Jones ) and Coulomb interactions were calculated within a cut-off radius of 1 . 15 nm . The long range electrostatic interactions were calculated with the particle mesh Ewald ( PME ) method [37] with a grid spacing of 0 . 133 nm . The long range Lennard-Jones interactions were analytically corrected for in the calculation of the pressure and the energy . The pressure of the simulation box was kept at an average of 1 bar using the isotropic Berendsen barostat [38] with a time constant of 25 ps and a compressibility of . The solvent and the capsid were coupled separately to an external heat bath at 300 K with the velocity-rescaling thermostat [39] using a time constant of 0 . 5 ps . Water molecules were constrained using the SETTLE algorithm [40] and the covalent bonds in the proteins were constrained using the P-LINCS algorithm [33] . Boundaries were treated periodically . After the equilibration , the position restraints were removed and an integration time step of 4 fs was used to generate 1 trajectories . In addition to constraining bond lengths , virtual hydrogen atoms were used [41] which allows slightly longer time steps . The isotropic Parrinello-Rahman barostat [42] , [43] was used to keep the average pressure at 1 bar with a time constant of 1 ps . The calcium ions were tethered to the oxygen ligands using harmonic potentials with force constants of 5000 . All other simulation parameters were the same as during the equilibration . The trajectories were sampled every 50 ps for analysis . The production simulations were calculated in parallel on a Cray XE6 system over 2016 cores ( 612 of which for PME calculations ) at a speed of 30 ns/day . Unless otherwise stated , all trajectories were analyzed at 1 ns intervals and final values were calculated as the average over the last 100 ns . Root-mean-square displacement ( RMSd ) and fluctuation ( RMSf ) was calculated as: ( 1 ) ( 2 ) The number of -helical residues was calculated using the g_helix program of the GROMACS package . In the water permeability analysis water molecules were counted when they had traversed the entire width of the capsid shell . Visual inspection did not imply a single-file type of permeation mechanism , which justifies that the distinction between a diffusive and osmotic permeation coefficients was not required [21] . The osmotic water permeability coefficient , , and the single pore osmotic water permeability coefficient , , was calculated as the average permeability in both directions using these formulas: ( 3 ) ( 4 ) Where N is the number of permeation events , t is the duration , A is the area and is the concentration gradient , i . e . 55 mol/L for pure water . The capsid was approximated to have the same area as a sphere with radius 8 nm . | We have studied the capsid of satellite tobacco necrosis virus using large scale molecular dynamics simulations , where the atomic motions of 1 , 2 million particles were tracked over one microsecond . We find that the capsid swells in the simulations , and that the permeability for water increases 10-fold upon removal of the structural calcium ions . The water leaks in predominantly near the three-fold symmetry axis , suggesting that this is the spot where capsid dissociation is initiated following infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"dynamics",
"plant",
"biology",
"macromolecular",
"assemblies",
"plant",
"science",
"plant",
"pathology",
"biophysics",
"simulations",
"chemistry",
"biology",
"biophysics",
"macromolecular",
"complex",
"analysis",
"plant",
"pathogens",
"computational",
"chemistry",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] | 2012 | Virus Capsid Dissolution Studied by Microsecond Molecular Dynamics Simulations |
During mouse sex determination , transient expression of the Y-linked gene Sry up-regulates its direct target gene Sox9 , via a 3 . 2 kb testis specific enhancer of Sox9 ( TES ) , which includes a core 1 . 4 kb element , TESCO . SOX9 activity leads to differentiation of Sertoli cells , rather than granulosa cells from the bipotential supporting cell precursor lineage . Here , we present functional analysis of TES/TESCO , using CRISPR/Cas9 genome editing in mice . Deletion of TESCO or TES reduced Sox9 expression levels in XY fetal gonads to 60 or 45% respectively relative to wild type gonads , and reduced expression of the SOX9 target Amh . Although human patients heterozygous for null mutations in SOX9 , which are assumed to have 50% of normal expression , often show XY female sex reversal , mice deleted for one copy of Sox9 do not . Consistent with this , we did not observe sex reversal in either TESCO-/- or TES-/- XY embryos or adult mice . However , embryos carrying both a conditional Sox9 null allele and the TES deletion developed ovotestes . Quantitative analysis of these revealed levels of 23% expression of Sox9 compared to wild type , and a significant increase in the expression of the granulosa cell marker Foxl2 . This indicates that the threshold in mice where sex reversal begins to be seen is about half that of the ~50% levels predicted in humans . Our results demonstrate that TES/TESCO is a crucial enhancer regulating Sox9 expression in the gonad , but point to the existence of additional enhancers that act redundantly .
Cell fate choices during development often depend on the expression of key genes that must themselves be regulated in a controlled manner by the action of transcription factors at critical cis-regulatory regions or enhancers . Some of these genes play major roles in multiple cell types and stages , as evidenced by the effects of loss-of-function mutations . Both how they are regulated in a tissue specific manner and how they in turn exert their actions on different sets of target genes depend on the precise cellular context , including the combination of both partner factors and antagonists or competitors , the activity of signaling pathways , and chromatin states [1] . But while particular combinations of transcription factors are required to generate specificity , some appear to be rate limiting . This might also be true of the enhancers that integrate this regulatory input to expression in a particular tissue . The regulation and action of Sox9 during sex determination and testis differentiation illustrates many of these issues . SOX9 is a key transcriptional activator required for the specification , differentiation and maintenance of multiple cell types during development and for several stem and progenitor cells in adults [2–7] . Loss-of-function mutations in the gene have pleiotropic effects and levels of expression are critical . In humans , heterozygosity for null mutations affecting protein function , or for translocations that disrupt gene expression , lead to campomelic dysplasia ( CD , OMIM 114290; [8] ) , a severe syndrome , invariably fatal within a few years of birth , with notable defects not only in cartilage development , but also in a range of other tissues including the CNS , gut , kidneys , and sensory systems [7 , 9 , 10] . In addition , about 70% of XY CD patients show male-to-female sex reversal , indicating that 50% of normal levels of SOX9 is at a threshold for testis differentiation in humans [11 , 12] . Heterozygous null mutant mice also show a range of defects in many of the same tissues , and die after birth due to skeletal problems that compromise breathing [9 , 13 , 14] . In contrast to humans , heterozygosity for a null mutation in Sox9 in mice does not lead to XY female sex reversal [14 , 15] . Homozygous Sox9-null mutant mouse embryos die from cardiac neural crest defects at around 11 . 5 days post coitum ( dpc ) , about 24 hours prior to the overt differentiation of testes in XY embryos . However , isolating the XY gonads at 11 . 5 dpc and placing them in an organ culture system revealed that they began to differentiate as ovaries rather than as testes [14] . Moreover , homozygous conditional deletion of Sox9 using an early acting Sf1-Cre driver was shown to give complete XY sex reversal [15 , 16] . The trigger for the early gonad to differentiate as a testis rather than an ovary is the transient expression of the Y-linked sex-determining gene , Sry [17–22] . Both the Sry gene and its protein product show little conservation between mammalian species apart from an HMG box type of DNA binding domain . Nevertheless , current evidence ( reviewed by [23] ) suggests that SRY proteins act as weak transcriptional activators within the supporting cell precursor lineage in the XY genital ridge , where their most critical role is to upregulate the expression of Sox9 . Sox9 is transcribed at low levels in both XX and XY genital ridges from about 10 . 5 dpc [24 , 25] . Sry expression in XY supporting cell precursors then sharply upregulates Sox9 expression in XY genital ridges [19 , 22 , 26 , 27] . Once SOX9 reaches a critical threshold , several autoregulatory loops are recruited , including SOX9 acting on its own transcription , to further upregulate its expression and trigger the differentiation of the cell lineage into Sertoli cells . In the absence of Sry in an XX gonad , Sox9 expression is repressed by the action of several ovary-determining and/or anti-testis genes , including components of the WNT-signaling pathway; cells of the supporting cell lineage differentiate into granulosa cells , and an ovary develops ( reviewed by [4 , 28] ) . The expression of these anti-testis factors may well account for the time-sensitivity of SRY action; if its expression is delayed by just a few hours , Sox9 can no longer be upregulated and ovaries develop [29] . Similarly , ectopic activation of the WNT pathway via expression of an activated form of β-catenin leads to XY female sex reversal , although this reflects a failure to maintain rather than initially upregulate Sox9 [30] . The critical role for SOX9 in testis differentiation is also revealed by gain-of-function studies . XX male sex reversal was found in a human patient with a duplication of the Sox9 gene [31] . In mice , XX male sex reversal occurs in Wt1–Sox9 transgenic mice [32] and in the Odd Sex ( Ods ) transgenic insertional mutant [33 , 34] , both of which constitutively express Sox9 in the early gonad irrespective of chromosomal sex . Moreover , when Ods is bred onto XY mice carrying an Sry deletion , the resulting males are fertile , suggesting not only that SOX9 can substitute for SRY in sex determination , but that it is probably the only critical target gene of SRY [35] . Because Sox9 is expressed in many tissues , it is not surprising that it has a very complex regulatory region . Indeed , it is thought that this extends about 2 Mb upstream , in a region characterized as a gene desert [36] . The first attempt to dissect and reveal the gonad-specific regulatory regions of Sox9 began with a mouse Sox9 bacterial artificial chromosome ( BAC ) clone ( -70 to +50 kb ) carrying a lacZ reporter gene to generate transgenic mice . Expression of the reporter replicated the endogenous Sox9 expression pattern in the developing gonads , as well as in some , but not all other sites [37] . LacZ expression was first detected in both XX and XY genital ridges around 10 . 5 dpc , increased by 11 . 5 dpc , particularly in XY genital ridges , and then became restricted to the testis from about 12 . 5 dpc , when it became localized to Sertoli cells . A reiterative process testing the ability of progressively smaller regions of the BAC to drive reporter gene expression ( LacZ or eGFP ) in transgenic mice eventually revealed a 3 . 2 kb fragment , located between -13 kb to -10 kb upstream of the Sox9 transcriptional start site , to be sufficient to mimic the gonad-specific expression of Sox9 . This element was termed TES ( for Testis-specific Enhancer of Sox9; [37] ) . Chromatin immunoprecipitation ( ChIP ) assays showed that SRY and steroidogenic factor 1 ( SF1 , encoded by the gene Nr5a1 ) directly bind several sites within TES in vivo at 11 . 5 dpc . SOX9 was also shown to be bound to TES at 13 . 5 dpc , where it is likely to replace SRY in the interaction with SF1 to regulate its own expression in the testis ( see Fig 1 , S1 Fig and [37] ) . In support of this , genetic studies indicated that both SRY and SOX9 contributed to reporter gene expression driven by TES . Furthermore , a more highly conserved element of 1 . 4 kb was further refined within TES and termed TESCO ( for TES core element ) . Mutating the three SRY/SOX9 and the six SF1 binding sites in TESCO abolished the enhancer activity in co-transfection assays in vitro and in transgenic mice [37] . TES and TESCO also contain putative binding sites for several other transcription factors known to have roles in sex determination or to maintain supporting cell fate . For example , FOXL2 , together with estrogen receptors , are required to maintain granulosa cell fate , such that conditional deletion of Foxl2 from the adult mouse ovary leads to the expression of SOX9 and transdifferentiation of granulosa cells into Sertoli-like cells [38] . FOXL2 was found to bind TES and to repress Sox9 transcription in vivo and in vitro . All of the above suggests that TES/TESCO plays a major role in integrating both positive ( i . e . transcriptional activator ) and negative ( i . e . repressor ) effects on Sox9 transcription during gonad development . To determine the extent to which TES or TESCO are required for Sox9 expression in the developing gonad in mice , and if this enhancer is indeed rate limiting for testis differentiation , we deleted TES/TESCO using CRISPR/Cas9 genome editing . Homozygous deletion of either TESCO or TES led to a reduction of Sox9 expression in the XY gonad , to approximately 60% or 45% of levels seen in wild type controls ( respectively ) , indicating that TES/TESCO is crucial for , but not the sole element involved in , regulating Sox9 transcription levels during sex determination .
To delete TESCO , zygotes were injected with Cas9 mRNA and a pair of sgRNAs , targeting either side of the TESCO sequence ( Fig 1 , Table 1 and S1 Fig ) . A total of 26 mice were born after zygote injections of which seven appeared to be mosaic or heterozygous for insertions or deletions at the targeted locus based on PCR analysis . Two males and one female appeared to have the correct sized deletion; the two males were test-bred with C57BL/6J females to confirm transmission of the deleted allele . Sequencing of heterozygous offspring from these matings confirmed a precise 1260 bp deletion within TESCO between the two-targeted Cas9 cleavage sites in both cases; offspring of one of the males carried an additional unrecognised sequence of 11 bp inserted at the deletion site . The 1260 bp deletion included 1252 bp ( 97% ) of the 1282 bp TESCO element . Stable lines were established from these heterozygous offspring . None of the lines showed overt signs of sex reversal in either heterozygous or homozygous TESCO-deleted XY or XX embryos or liveborn mice , and the breeding behavior and fertility of adults appeared normal . To look for more subtle phenotypes , gonadal histology and marker expression were studied in detail in one TESCO-/- line at three time points: at 12 . 5 dpc , when testis cords and the testis-specific coelomic blood vessel are usually apparent and Sox9 expression levels have reached their peak , while in XX gonads ovarian patterns of gene expression are being established , including the onset of Foxl2 expression ( see , for example: [39] ) ; at 14 . 5 dpc; and at 6 weeks postnatal . The gonads of TESCO-/- XY and XX embryos and adults at these three stages showed no histological signs of dysmorphology , sex reversal or defective gametogenesis ( Fig 2A , S2A Fig ) . To study the phenotype at the molecular level , we used immunofluorescence for the embryonic testis marker AMH and ovary marker FOXL2 ( Fig 2B ) [40 , 41] . Both 12 . 5 dpc and 14 . 5 dpc TESCO-/- XY gonads were indistinguishable from wild type testes , with AMH expression seen clearly within testis cords at embryonic stages , SOX9 in adult Sertoli cells and no detectable FOXL2 expression ( Fig 2B and 2C ) . Immunofluorescence on sectioned TESCO-/- XX gonads also revealed normal ovarian morphology and FOXL2 expression , and no signs of testis marker expression at embryonic ( AMH; S2B Fig ) or adult stages ( SOX9; S2C Fig ) . Therefore , the TESCO deletion does not seem to affect sex determination or subsequent gonad development or fertility in either chromosomal sex . Although TESCO is highly conserved among mammals [37 , 42] and is bound and regulated by SRY , SF1 and SOX9 itself , it is only half the size of the full TES enhancer in mice , which contains additional SRY/SOX9 and SF1 binding sites . We therefore wanted to explore the possibility that deleting the larger enhancer may result in gonadal phenotypes or sex reversal . To delete TES we used two sgRNAs that target each side of TES , transfected them into ES cells with a Cas9 expression plasmid and screened for clones carrying TES deletions . Of 70 clones screened , 14 ( 20% ) exhibited a heterozygous deletion of TES by PCR , which was verified by sequence analysis ( S3A Fig ) . Indels were always present at both sgRNA target sites in alleles that retained TES . Two clones were injected into 8-cell stage embryos . Several male animals with 100% agouti coat color , corresponding to the ES cell genotype ( C57BL/6J x CBA ) , were obtained . These males were bred to C57BL/6J females and the TES deletion was found in approximately 50% of offspring . We initially analyzed embryos and offspring with a mixed genetic background . There were no obvious signs of sex reversal and as evident in Fig 3A , testis size and morphology were similar between wild type , TES+/- and TES-/- mice . There was also no difference in the weight of testes from 7 month-old mice ( S3B Fig ) . Fertility was examined over a period of 5 months on TES+/- and TES-/- mice , revealing no significant difference in number of litters or of average litter size during this period ( S3C and S3D Fig ) . C57BL/6J mice are more sensitive than other strains to mutations likely to give XY female development [43] . Genital ridges from C57BL/6J embryos show overall higher levels of expression of genes characteristic of ovary development than those from 129S8 [44] . We therefore backcrossed the TES-deleted mice onto the C57BL/6J genetic background for four generations , but still saw no obvious signs of sex reversal . Using immunofluorescence , SOX9 was found to be expressed in XY TES-/- fetal gonads; moreover , no FOXL2 protein could be detected ( Fig 3B ) . Testis cords had formed in the TES deleted XY gonads and they appeared normal ( Fig 3B ) . Testes from 8 week-old TES-/- mice also showed normal seminiferous tubule structure . We noted a slight , consistent reduction in the intensity of SOX9 staining , but stress this is speculative given that immunofluorescence is not a quantitative assay ( Fig 3C ) . Immunofluorescence analysis of TES-deleted XX ovaries revealed normal morphology and expression of FOXL2 , and no SOX9 expression ( S4 Fig ) . Since deleting either TESCO or TES did not affect sex determination or testis differentiation , we used qRT-PCR to ask whether the deletions had any effect on the levels of expression of Sox9 or other Sertoli cell markers , notably Amh , a direct target gene of SOX9 [45 , 46] , and Sox8 which is expressed in XY gonads from 12 . 5 dpc and acts redundantly with Sox9 [14 , 47–49] . We also examined the granulosa cell markers Foxl2 and Wnt4 . At 14 . 5 dpc , XY TESCO+/- and TESCO-/- gonads expressed Sox9 mRNA at around 72% and 62% of wild type levels , respectively ( both at p<0 . 0001; Fig 4A ) . Amh mRNA levels were also reduced in the mutant testes , with TESCO-/- showing 67% of wild type levels ( p = 0 . 002 ) . In contrast , Sox8 , Foxl2 and Wnt4 mRNA levels were unchanged in mutant versus wild type XY gonads at 14 . 5 dpc ( Fig 4A ) . Gonads from six week-old XY mice gave similar results , with TESCO+/- having 73% ( p = 0 . 006 ) and TESCO-/- 58% ( p = 0 . 0004 ) of wild type Sox9 mRNA levels ( Fig 4B ) . Foxl2 levels were unchanged in the TESCO-deleted XY gonads ( Fig 4B ) . Analysing gonad-mesonephros pairs at 12 . 5 dpc also revealed a decrease in Sox9 mRNA levels with XY TESCO+/- and TESCO-/- gonads showing approximately 81% and 73% of wild type levels , respectively ( S5A Fig ) . The decrease is less than at later stages , but this may be due to “contaminating” Sox9 expression in the mesonephros . Foxl2 mRNA levels were also unaffected at this stage ( S5A Fig ) . These results clearly indicate that the TESCO enhancer is an important regulator of Sox9 expression throughout testis development , accounting for around 40% of its expression levels . Since there are additional putative transcription factor binding sites in TES that are not present in TESCO , including several for SF1 , SRY and SOX9 ( Fig 1 and S1 Fig ) , we hypothesized that deleting the larger TES enhancer may result in a further decrease in Sox9 mRNA levels . Consistent with this , qRT-PCR assays on RNA from XY gonads at 13 . 5 dpc revealed that TES+/- expressed 75% and TES-/- 44% of the wild type levels of Sox9 mRNA ( p<0 . 0001; Fig 4C ) . XY TES-/- gonads also had 61% of the wild type levels of Amh mRNA ( P<0 . 0001; Fig 4C ) , while Sox8 mRNA levels were unchanged . Sry expression is usually downregulated as soon as SOX9 levels increase in supporting cell precursors [20] . This suggests that SOX9 directly or indirectly represses Sry . We therefore examined Sry mRNA levels , finding them to be 2-fold higher in TES-/- compared to wild type at 13 . 5 dpc ( Fig 4C ) . However , the difference was not statistically significant , perhaps due to the low levels of Sry transcripts at this stage and considerable variability of precise stage between embryos . Similarly , there appeared to be a 2-fold increase in Foxl2 mRNA levels in TES-/- compared to controls , but again this was not statistically significant and it represents only trace levels compared to expression in XX gonads at 13 . 5 dpc ( Fig 4C ) . However , a 2-fold increase in Wnt4 mRNA levels was statistically significant ( p = 0 . 005; Fig 4C ) . Unlike Foxl2 , which is usually first expressed only as granulosa cells begin to differentiate , Wnt4 is expressed equally in XX and XY gonads at early stages , before increasing in ovaries and declining in testes as these develop , but still with robust levels at 13 . 5 dpc [44] . As in the embryo , the deletion of TES had a greater effect than deletion of TESCO in the adult testis , with TES+/- and TES-/- having 47% of Sox9 mRNA ( p = 0 . 001 and 0 . 02 respectively ) and 81- and 72-fold increases in Foxl2 mRNA ( p = 0 . 01 ) compared to wild type levels ( Fig 4D ) . Even though Foxl2 expression was increased , it was still far below the level of expression in adult XX ovaries , which is 540-fold that of wild type XY levels . The similar levels of expression seen in the testes of adult heterozygous and homozygous TES mutants is in contrast to the additive effects of each mutant allele seen in the embryo , and in both adult and embryonic TESCO mutants . This could be explained by disruption in TES , but not TESCO mutants , of a positive regulatory feedback loop specific to the adult testis , thereby abrogating the contribution of the remaining copy of TES to Sox9 transcription . This could involve DMRT1 , which is known to be a direct transcriptional activator of Sox9 in adult Sertoli cells , where it binds sequences both upstream and downstream of the coding region , while expression of SOX9 in granulosa cells leads to upregulation of Dmrt1 [38 , 50] . Assaying mRNA levels of Sox9 and Foxl2 in XX gonads carrying TESCO ( S5B Fig ) or TES deletions ( S5C Fig ) at embryonic stages did not show any difference in expression compared to wild type ovaries ( S5B and S5C Fig ) . All three members of the group E Sox transcription factor gene family , Sox8 , Sox9 and Sox10 , are expressed in the developing mouse testis [48 , 51–53] . The three proteins are very similar in structure and are likely to be able to regulate overlapping , if not the same , sets of target genes . Sox8 expression starts at 12 . 5 dpc , shortly after Sox9 is upregulated [52] . Sox8 homozygous null males show no embryonic or early postnatal phenotypes , including in the gonad [48] , but seminiferous tubule failure and infertility develop at around 5 months [54] . Although SOX8 function is dispensable with regard to embryonic testis development , several studies have revealed that it has a redundant role with SOX9 . When an early-acting and efficient Sf1-Cre driver is used with a conditional Sox9 allele , the result is complete XY female sex reversal [15] . However , if the Cre driver is inefficient and/or acts late , XY female sex reversal or a failure to maintain Sertoli cells is only seen on a Sox8 null background [14 , 49] . This indicates that SOX8 reinforces SOX9 function during testis formation . We therefore sought to determine whether Sox8 could be a major modifier affecting whether the TES deletion can lead to XY female sex reversal or not . The TES deletion allele was bred onto a Sox8 null background and gonads analysed by immunofluorescence at 14 . 5 dpc ( Fig 5A ) and 8 weeks ( Fig 5B ) . The XY TES-/-; Sox8-/- gonads retained SOX9 expression , with no obvious induction of FOXL2 , and had normal testis cord or seminiferous tubule morphology . We analysed gonadal gene expression by qRT-PCR at 14 . 5 dpc , when Sox8 expression was expected to be maximal . Unexpectedly , Sox9 mRNA levels were increased by about 2-fold upon deletion of Sox8 ( TES+/+; Sox8+/+ vs . TES+/+; Sox8-/- p<0 . 0001 , Fig 5C ) . TES deletion lowered Sox9 mRNA levels to 77% in TES+/-; Sox8-/- and to 50% in TES-/-; Sox8-/- compared to the TES+/+; Sox8-/- levels ( p = 0 . 01; p<0 . 0001 , respectively , Fig 5C ) . Amh mRNA levels were also significantly reduced , with TES+/-; Sox8-/- showing 56% ( p = 0 . 0007 ) and TES-/-; Sox8-/- 29% ( p<0 . 0001 ) of the TES+/+; Sox8-/- Amh mRNA levels ( Fig 5C ) . Lastly , Foxl2 mRNA levels showed a significant increase on a Sox8 null background ( Fig 5C ) , with an ~11-fold increase in Foxl2 expression in TES-/-; Sox8-/- compared to TES+/+; Sox8-/- or TES+/+; Sox8+/+ ( p<0 . 0001 ) . The above results indicate that the absence of Sox8 in addition to TES did not exert an additive effect on Sox9 expression levels nor on the fate of the gonad . However , the additional loss of Sox8 did lead to higher levels of Foxl2 compared to the TES deletion on its own ( Fig 5C ) . This suggests that SOX8 as well as SOX9 is normally involved in the repression of Foxl2 during testis development , supporting their functional redundancy . If about 45% ( as in TES-/- ) or 50% ( as in Sox9+/- ) of normal levels of Sox9 are sufficient to promote testis development in mice , but no Sox9 ( Sox9-/- ) gives complete XY female sex reversal with ovary development , then this raises the question of what level constitutes a minimum threshold level of Sox9 expression able to direct normal testis development in mice . To address this question , we combined one allele of Sox9 deleted for TES ( Sox9ΔTES ) with a conditional Sox9 null mutant allele ( Sox9fl ) and a ubiquitous Cre driver ( β-Actin:Cre ) . This combination would be predicted to reduce Sox9 expression in the gonad to about 25% of wild type . Irrespective of genotype , all XY embryos examined at 13 . 5 dpc appeared to have normal testes except for the embryos that were Sox9-/ΔTES; β-Actin:Cre . All gonads from the latter had a disorganized testis cord structure in the centre and an absence of cords at both poles . Immunofluorescence staining confirmed that they were ovotestes , with SOX9 expressed in the central testicular portion and FOXL2 expression marking the ovarian domains at the poles ( Fig 6A ) . Mice heterozygous for Sox9-null mutations die at birth , making it impossible to study the postnatal phenotype and to ask if the ovotestes resolve into testes , ovaries , or stay mixed . To determine the levels of Sox9 in the various mutant gonads , and ask if these matched predictions , we performed qRT-PCR assays on RNA from whole XY gonads at 13 . 5 dpc from the following genotypes: Sox9+/+ ( wild type controls that contain two normal Sox9 alleles , and which lack the Cre driver; referred to as Cre- in Fig 6B ) ; Sox9+/+; β-Actin:Cre+ ( wild type controls that contain two normal alleles of TES and Sox9 as well as the β-Actin:Cre transgene ( referred to as Cre+ in Fig 6B ) , to rule out any effect of the latter ) ; Sox9-/+; β-Actin:Cre+ ( where one Sox9 allele is wild type , but Cre activity has converted the other floxed allele into a Sox9 null allele ) ; Sox9fl/ΔTES ( embryos carrying one floxed allele and one TES-deleted allele of Sox9 , but without the Cre-driver ) ; and Sox9-/ΔTES; β-Actin:Cre+ ( as before , but with the Cre-driver , resulting in the floxed allele becoming null mutant for Sox9 ) . Sox9 mRNA levels were similar between Sox9+/+; Cre- and Sox9+/+; Cre+ indicating the β-Actin:Cre transgene has no effect on Sox9 expression levels . Having one null allele of Sox9 resulted in 49% of the wild type levels of Sox9 mRNA ( p<0 . 0001; Fig 6B ) . Sox9fl/ΔTES; Cre- embryos exhibited 71% of the wild type levels ( p<0 . 01; Fig 6B ) ( N . B . This is similar to the level found previously for the TES-deletion alone , suggesting that the floxed allele is equivalent to wild type ) . Finally , having a combination of a Sox9 null allele and the TES-deleted allele resulted in 23% of wild type levels of Sox9 mRNA ( p<0 . 0001; Fig 6B ) . Amh mRNA levels in the same 13 . 5 dpc testes exhibited a similar trend , with Sox9-/+; Cre+ , Sox9fl/ΔTES; Cre- , and Sox9-/ΔTES; β-Actin:Cre+ having 66% ( p = 0 . 0003 ) , 100% and 36% ( p<0 . 0001 ) of the wild type levels respectively ( Fig 6B ) . In contrast , Foxl2 mRNA levels were 9-fold ( p = 0 . 03 ) higher , unaffected and 32-fold ( p = 0 . 001 ) higher in the same samples , respectively ( Fig 6B ) . Because Foxl2-positive cells in the ovotestes are not expressing Sox9 ( see Fig 6A ) , the proportion of cells expressing Sox9 is reduced compared to controls . It is therefore possible that the level of Sox9 mRNA per cell is a little higher than 23% of normal . However , given all the other variables , this is still close to the predicted value of about 25% . Together , these results demonstrate that while 50% of Sox9 mRNA levels are sufficient to permit testis development in mice , 23% is below a threshold for normal testis development , but above the level at which the XY gonad becomes fully sex reversed .
TES , and its core TESCO , is the only Sox9 enhancer identified to date giving reporter expression that mimics that of the endogenous gene in the gonads [37] . Moreover , it is able to bind relevant transcription factors and integrate positive and negative control over Sox9 expression . However , although it is the only enhancer identified , by transgenic assays , within a 120 kb BAC , including 70kb of upstream sequences , it still remained unclear whether it is essential or indeed the only such enhancer . We show here that normal levels of Sox9 expression in the testis depend on TES/TESCO at both embryonic and adult stages . The TES deletion led to a more pronounced decrease in Sox9 mRNA levels than the TESCO deletion , which is most likely due to the presence of additional SF1 , SRY and SOX9 binding sites in the former . Reduced levels of Amh were also seen , consistent with this being an early direct target of SOX9 [45 , 46] . However , apart from a small increase in Foxl2 transcripts , there were no signs of sex reversal in either TES or TESCO homozygous deleted embryos or adults . Moreover , even the former , with only 45% normal levels of Sox9 mRNA , appeared to have no defects in fertility ( S3 Fig ) . Sox9 has perhaps four different phases of expression during gonadal and testis development . First , there is the very low level in the supporting cell precursors prior to the onset of Sry expression , and consequently this expression is seen in both XX and XY genital ridges . There is then the phase of upregulation in XY embryos due to the direct effect of SRY acting together with SF1 , and the repression seen in XX embryos , presumably due to the activity of anti-testis factors , for example the effect of β-catenin , which may work by inhibiting SF1 activity [55] . There is then a phase characterized by further upregulation and consolidation of high levels of SOX9 , which leads to Sertoli cell differentiation . Sox9 levels are maintained due to several positive feedback loops , including SOX9 acting on itself , as well as FGF9 and prostaglandin D2 [56–59] . In the fetal ovary , it would appear that several factors maintain repression of Sox9 , including WNT signaling , perhaps FOXL2 , and one or more factors yet to be identified [40 , 56 , 59 , 60] . The final phase is seen postnatally , when maintenance of Sox9 expression in the testis becomes dependent on DMRT1 , whereas its repression in the ovary is dependent essentially only on FOXL2 [38 , 50 , 61–63] . We cannot be certain from the results presented here that TES/TESCO has any role in the expression of Sox9 during the very early phase . However , assays involving ChIP , TES and TESCO transgenic reporters , and co-transfection in vitro , all suggest that both SRY and SF1 do act on the enhancer [37] . Nevertheless , we can conclude that TES cannot be the only enhancer required at these early stages , otherwise TES deletion would have resulted in XY sex reversal . We can also conclude that levels of expression of Sox9 at 45% of normal are sufficient for Sertoli cell differentiation and maintenance in the mouse . This was perhaps expected from previous studies looking at mice heterozygous for Sox9 null mutations , where Sox9 mRNA levels would likely have been around 50% of normal ( as shown here ) . We found Sertoli cell differentiation even in the absence of Sox8 , in TES-/-; Sox8-/- embryos , despite the testes expressing even lower levels of Amh and about 11-fold higher levels of Foxl2 compared to TES-/- embryos ( Fig 5C ) . Although still far from reaching the levels in the ovary ( ~202-fold compared to TES+/+; Sox8-/- XX; Fig 5 ) ; this level of Foxl2 may represent the first sign of a shift in the balance from male to female . We noted that Sox9 expression was significantly higher in the absence of Sox8 , which was not commented on by Barrionuevo et al . [49] , even though it is apparent in their data . Although Sox8 expression is known to be dependent on SOX9 [14 , 49] , this suggests that SOX8 by itself or as heterodimers with SOX9 , can repress or interfere with Sox9 transcription . Perhaps the two proteins have overlapping but not quite identical functions . Several ovary determining or anti-testis factors have been shown to act on TES/TESCO . They include FOXL2 [38] , WNT signaling factors [55] , and DAX1 [64] , where both the latter seem to act via inhibition of SF1 . It was therefore conceivable that deletion of TES or TESCO might lead to signs of testis differentiation in XX gonads . This was not the case , however , as the XX embryos showed normal ovary development and gave rise to fertile females , and we found no evidence of Sox9 induction ( S5B and S5C Fig ) . This implies that there are other enhancers that can integrate the negative control over Sox9 transcription by these anti-testis factors , and/or that the upregulation of Sox9 seen in , for example , Wnt4 or Foxl2 mutants , is dependent on transcriptional activators acting on TES/TESCO , which they cannot do if it is deleted [40 , 56] . When we combined a conditionally deleted null allele of Sox9 with a TES deleted allele , which further reduced Sox9 levels to 23% of normal , XY gonads showed typical signs of ovotestes development , with SOX9 expressed in the central domain , in testis cords that were poorly organized , and FOXL2 expressed in cord-free domains at the poles . Ovotestes often lose one component or the other and resolve into hypoplastic testes or ovaries by birth . Such mice , with smaller than normal testes , can show normal fertility; indeed , having only one functional testis is sufficient [65] . This is probably similar to the situation in humans with CD , where heterozygosity for SOX9 null mutations leads to XY female sex reversal in the majority of cases . This suggests that the threshold level for Sox9 is about 25% in mice and 50% in humans . If TES accounts for only about half the normal levels of Sox9 expression in the mouse testis , this suggests that there must be one or more additional enhancers responsible for the remainder . The notion of redundant or “shadow” enhancers is now well established ( for reviews , see [66 , 67] ) . Indeed , multiple enhancers upstream of Sox9 have recently been shown to regulate its expression in cartilage [68] . Additional enhancers relevant to the gonad are likely to be outside the 120 kb BAC that was originally screened by transgene reporter assays [37] . Evidence from human patients showing DSD associated with deletions or duplications involving SOX9 flanking regions suggests that these may map at a considerable distance upstream . One region located 516–584 kb upstream of SOX9 , termed RevSex , may be relevant . This region was originally defined based on four cases of 46 , XX SRY-negative individuals who presented with testis development . Each patient had a duplication involving a genomic region located ~600 kb upstream of SOX9 [69–73] . In addition , a 46 , XY female with gonadal dysgenesis was reported to harbor a deletion of this region [69] . A recent study further refined this region to about 40 kb in individuals who did not exhibit any other phenotype apart from the sex reversal [74] . A second region implicated in SOX9 expression in the gonad has been termed XY Sex Reversal ( XY SR ) . This region was recently identified in four 46 , XY SRY-positive female individuals carrying heterozygous deletions of a minimal 32 . 5 kb interval located 607 . 1–639 . 6 kb upstream [73] . Given that no human case of DSD has yet been described with mutations affecting the TES/TESCO homologous region close to the SOX9 coding region [75] , these far upstream regions may have particular importance for levels of SOX9 expression in the human testis . However , whether these rather large regions contain true enhancers or are involved in chromatin organization or domain structure is not yet known . Nevertheless , there is a degree of conservation between these regions in the human and mouse suggesting that it might be informative to ask if they contain functional enhancers . Therefore , despite the significant contribution made by TES/TESCO , further research is needed in order to gain a comprehensive picture of the array of enhancers that regulate Sox9 expression in the gonads .
Two pairs of 20-mer single guide RNAs ( sgRNAs ) were designed ( www . crispr . mit . edu/ ) to flank and delete either the mouse TESCO ( 1260 bp deletion , including 1252 bp ( 97% ) of the 1293 bp TESCO sequence ) or TES sequences ( 3194 bp deletion , including the full 3161 bp TES sequence ) [37] by CRISPR/Cas9 genome editing . sgRNAs and Cas9 mRNA were prepared as described elsewhere [6] . Briefly , for each sgRNA the complementary pair of oligos ( Table 1 ) was annealed and cloned into pX330 ( Addgene #42230 ) . For the TESCO deletion , A T7-sgRNA PCR product was amplified with a T7 promoter sequence introduced on the forward primer in conjunction with a universal reverse primer sgRNA-uni . R ( Table 1 ) . This product was used as the template for in vitro transcription ( IVT ) using the MEGAshortscript T7 IVT kit ( Life Technologies ) . The Cas9 coding region was released from pX330 and subcloned into pBluescript II ( pBS-Cas9 ) . XhoI linearized pBS-Cas9 was used as the template for IVT using the mMESSAGE mMACHINE T7 ULTRA kit ( Life Technologies ) . Both sgRNAs and Cas9 mRNA were purified using the MEGAclear kit ( Life Technologies ) and eluted in RNase-free water . 30 ng/μl of Cas9 mRNA and 15 ng/μl of each sgRNA diluted in RNase-free water were injected into one pronucleus of C57BL/6J x CBA F1 hybrid one-cell embryos , as described [76 , 77] . Injected embryos were cultured overnight to the two-cell stage , and then surgically transferred into oviducts of day-of-plug pseudopregnant CD1 mice . Two pX330 plasmids containing the sgRNAs ( Table 1 ) were transfected along with the pPGK-Puro plasmid ( Addgene #11349 ) to C57BL/6J x CBA F1 hybrid ES cells using Nucleofector according to the manufacturer’s protocol ( Mouse ES cells Nucleofector kit , Lonza ) . ES cells were cultured in standard 2i/LIF media: serum-free media NDiff ( Stem Cell Sciences ) supplemented with MEK inhibitor PD0325901 ( 1 μM; Axon Medchem ) , GSK3 inhibitor CH99021 ( 3 μM; Axon Medchem ) and 1000 U/ml leukaemia inhibitory factor ( LIF ) ( Millipore ) . Approx . 36 hours post transfection the cells were selected with 2 μg/ml Puromycin ( Life Technologies ) for 48 hours . Following selection , the ES cells were returned to 2i/LIF media and clones were picked a week later . Single clones were screened for the deletion using PCR spanning the TES deletion site ( TES del-F and TES del-R which amplify a 3 . 5kb band in WT cells and a 327 bp band in TES deleted cells , Table 2 ) . All clones were heterozygous for the deletion . Sequencing was carried out to verify the deletion as well as to examine the undeleted allele , which in all cases contained indel mutations on both sides of TES . Transgenic founders with the targeted deletion of the TESCO sequence were identified amongst live born mice by PCR genotyping ( all genotyping primers are listed in Table 2 ) . All genotyping was performed using genomic DNA extracted from tail tissue of embryos or ear notch tissue of juvenile animals . The primer pair TESCO del-F1 and TESCO del-R2 amplified a single 1775 bp product in wild type animals whereas a shorter product of approximately 492 bp ( depending on the exact size of the deletion ) was preferentially amplified or co-amplified with the larger product in animals heterozygous or mosaic for the TESCO deletion . Founder males were bred with C57BL/6J females to verify that deleted alleles were transmitted to progeny . The deletions were verified by cloning and sequencing PCR amplified products from heterozygous offspring of founders . Stable lines were established from two founders , both of which bore a precise 1260 bp deletion between the two targeted Cas9 cleavage sites ( with one also bearing an 11 bp insertion of unrecognised sequence ) ; only one of these lines was used for subsequent analysis . For the TES deletion , two lines of ES cells harbouring the TES deletion were injected into 8-cell stage embryos from outbred Parkes female mice , which were then transferred into oviducts of day-of-plug pseudopregnant C57BL/6J x CBA F1 hybrid recipients . Several 100% Agouti pups ( “chimeras” ) were born . Males were bred to C57BL/6J females to give germline transmission and expand the colony . Genotyping of animals was carried out using the same PCR primer set used to genotype the ES cells ( Table 2 ) . Sox8-null mutant mice were kindly provided by Michael Wegner , University of Erlangen , Germany [48] . All other strains , including C57BL/6J and CBA were obtained from colonies maintained within the two institutions . Embryos or offspring homozygous for either the TES or the TESCO deletion were produced by crossing heterozygous animals . Embryos were collected from timed-mating at embryonic day 12 . 5 dpc , 13 . 5 dpc or 14 . 5 dpc , with noon of the day of plug designated as 0 . 5 dpc . At 12 . 5 dpc , gonadal sex is usually but not always possible to judge by visual assessment , however by 13 . 5 dpc and 14 . 5 dpc testis cords are clearly visible . For postnatal analysis , phenotypic sex was first assessed by inspection of external genital anatomy ( including ano-genital distance ) . Pups were then killed and dissected for inspection of the reproductive tracts and gonads . For all embryos and pups , chromosomal sex was determined by PCR genotyping with primers Sex-F and Sex-R [78] or by PCR for the Y chromosome gene , Zfy1 [18] . To clearly distinguish between heterozygous and homozygous animals , we employed two complementary PCR reactions: the first to amplify the deleted allele ( TESCO del-F1 x TESCO del-R2 for TESCO deletion and TES del-F and TES del-R for TES deletion ) while the second amplified the wild type allele ( primer pair TESCO del-F3 x TESCO del-R2 for TESCO deletion and TES Int-F x TES del-R for TES deletion , Table 2 ) . For 12 . 5 dpc embryos , paired gonad-mesonephros complexes were dissected out , with the mesonephros trimmed to the length of the gonads , and immediately stored in RNAlater RNA stabilization solution ( Invitrogen ) until required for gene expression analysis by qRT-PCR . For 13 . 5 dpc or 14 . 5 dpc embryos , single gonad without mesonephric tissue were collected . For adults at 6 or 8 weeks , an entire ovary or part of a single testis was similarly preserved in RNAlater solution . Whole embryos ( 12 . 5 dpc , 13 . 5 dpc and 14 . 5 dpc ) or whole gonads ( 6 or 8 weeks postnatal ) were also fixed overnight in 4% paraformaldehyde in phosphate-buffered saline ( PBS ) at 4°C , washed three times with PBS at 4°C , then dehydrated and embedded in paraffin or OCT . The embedded samples were sectioned for immunofluorescence or haematoxylin and eosin staining . Total RNA was extracted and subjected to DNaseI treatment using either an RNeasy Micro or Mini kit ( Qiagen ) , for fetal and postnatal tissue samples , respectively . RNA yield was quantified with a NanoDrop spectrophotometer ( NanoDrop Technologies ) , and 100–200 ng RNA ( 12 . 5 dpc , 13 . 5 dpc and 14 . 5 dpc ) or 1000–1500 ng RNA ( 6 and 8 weeks ) was used to synthesize cDNA with the High Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) or the SuperScript® III Reverse Transcriptase ( Invitrogen ) . qRT-PCR reactions were performed in triplicate or quadruplicate using SYBR Green PCR master mix ( Invitrogen ) and 150 nM each of forward and reverse primers , and analyzed on a Viia7™ Real-Time PCR System ( Invitrogen ) or the Applied Biosystems 7500 Real-Time PCR System ( Thermo Fischer Scientific ) . Primers are listed in Table 3 . Relative mRNA levels were determined by calculating 2-ΔΔCt values relative to the normalizer genes Tbp or Hprt ( 12 . 5 dpc , 13 . 5 dpc and 14 . 5 dpc ) or Hprt ( 6 and 8 weeks ) . Relative gene expression is presented as the mean 2-ΔΔCt values ( Error bars are SEM of the 2-ΔΔCt ) for multiple single gonads from individual embryos ( sample sizes as indicated on charts ) . Statistical analysis was performed using unpaired , two-tailed t-tests on the 2-ΔΔCt values . H&E histological staining was performed on 7μm-thick sagittal sections ( for 12 . 5 dpc , 14 . 5 dpc and 6 weeks ) according to standard protocols . For the experiments on the TESCO deletion , immunofluorescence staining was also performed on 7μm-thick sagittal sections ( for TESCO ) or 12 μm-thick sections ( for TES ) , as described elsewhere [53] , using ( as primary and secondary antibodies , respectively ) : goat anti-AMH ( 1:500 , Santa Cruz Biotechnology ) and donkey anti-goat Alexa Fluor 488 ( 1:200 , Invitrogen ) ; mouse anti-SOX9 ( 1:100 , Abnova ) and donkey anti-mouse Alexa Fluor 647 ( 1:200 , Invitrogen ) ; rabbit anti-FOXL2 ( 1:650 , generated as described by [53] and donkey anti-rabbit Alexa Fluor 647 ( 1:200 , Invitrogen ) . For the experiments on the TES deletion , immunofluorescence staining was performed using rabbit anti-SOX9 ( 1:300 , a generous gift from Francis Poulat , Institute of Human Genetics , Montpellier , France ) with donkey anti-rabbit Alexa Fluor 488 ( 1:500 , Invitrogen ) and goat anti-Foxl2 ( 1:300 , Novus ) with donkey anti-goat Alexa Fluor 568 ( 1:500 , Invitrogen ) . All immunofluorescence slides were also stained with 4' , 6-diamidino-2-phenylindole ( DAPI , Molecular Probes ) , to visualize nuclear DNA . All procedures involving animals and their care conformed to institutional , state and national guidelines or laws . This study was approved by the University of Queensland Animal Ethics Committee and by the UK Home Office ( PPL 70/8560 ) . | SOX9 , a member of the SOX family of developmental transcription factors related to the Y-chromosomal sex-determining factor SRY , plays pivotal roles in cell differentiation in a variety of developmental contexts including formation of the testes , skeleton , brain , skin , pancreas , gut and kidneys . During mammalian male sex determination , Sox9 is the critical effector gene through which SRY directs differentiation of Sertoli cells and hence drives testis formation; structural mutation or deletion of Sox9 causes XY sex reversal in humans and mice . Despite its importance , how Sox9 is regulated in time and location is poorly understood . Previous studies identified an enhancer element , TES , containing a core element , TESCO , either of which direct reporter gene expression to the developing testis in transgenic mice . However , no loss-of-function mutations have been identified in humans or created in mice to date . Here , we delete TES and TESCO in mice using CRISPR/Cas9 gene editing technology . As a result , Sox9 expression levels in fetal XY gonads were reduced by ~50% , but no sex reversal occurred . Our results confirm that intact TES/TESCO is required for directing appropriate Sox9 expression levels in the testis , and highlight the presence of additional enhancers that remain to be identified . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"gonads",
"developmental",
"biology",
"molecular",
"biology",
"techniques",
"embryos",
"immunologic",
"techniques",
"morphogenesis",
"research",
"and",
"analysis",
"methods",
"embryology",
"artificial",
"gene",
"amplification",
"and",
"extension",
"gene",
"expression",
"immunoassays",
"molecular",
"biology",
"immunofluorescence",
"sex",
"determination",
"ovaries",
"testes",
"anatomy",
"genetics",
"biology",
"and",
"life",
"sciences",
"polymerase",
"chain",
"reaction",
"genital",
"anatomy"
] | 2017 | Normal Levels of Sox9 Expression in the Developing Mouse Testis Depend on the TES/TESCO Enhancer, but This Does Not Act Alone |
The ubiquitin proteasome system in plants plays important roles in plant-microbe interactions and in immune responses to pathogens . We previously demonstrated that the rice U-box E3 ligase SPL11 and its Arabidopsis ortholog PUB13 negatively regulate programmed cell death ( PCD ) and defense response . However , the components involved in the SPL11/PUB13-mediated PCD and immune signaling pathway remain unknown . In this study , we report that SPL11-interacting Protein 6 ( SPIN6 ) is a Rho GTPase-activating protein ( RhoGAP ) that interacts with SPL11 in vitro and in vivo . SPL11 ubiquitinates SPIN6 in vitro and degrades SPIN6 in vivo via the 26S proteasome-dependent pathway . Both RNAi silencing in transgenic rice and knockout of Spin6 in a T-DNA insertion mutant lead to PCD and increased resistance to the rice blast pathogen Magnaporthe oryzae and the bacterial blight pathogen Xanthomonas oryzae pv . oryzae . The levels of reactive oxygen species and defense-related gene expression are significantly elevated in both the Spin6 RNAi and mutant plants . Strikingly , SPIN6 interacts with the small GTPase OsRac1 , catalyze the GTP-bound OsRac1 into the GDP-bound state in vitro and has GAP activity towards OsRac1 in rice cells . Together , our results demonstrate that the RhoGAP SPIN6 acts as a linkage between a U-box E3 ligase-mediated ubiquitination pathway and a small GTPase-associated defensome system for plant immunity .
To resist pathogen invasion , plants have evolved two layers of innate immunity: pathogen-associated molecular pattern ( PAMP ) -triggered immunity ( PTI ) and effector-triggered immunity ( ETI ) [1] . The first layer is PTI , which employs pattern-recognition receptors ( PRRs ) to perceive a broad range of PAMPs in order to activate basal defense signaling . The second layer , ETI , involves a rapid and robust defense response triggered by the direct or indirect interaction between a pathogen avirulence ( Avr ) protein and its cognate host resistance ( R ) protein . A major hallmark of ETI is a strong hypersensitive response ( HR ) , which represents a form of programmed cell death ( PCD ) and which activates a set of innate immunity signaling pathways that result in ion fluxes [2] , generation of reactive oxygen species ( ROS ) [3] , and release of nitric oxide ( NO ) [4] . The ubiquitin proteasome system ( UPS ) is involved in selective degradation of proteins in the cells of eukaryotic organisms . It consists of three main kinds of enzymes: ubiquitin-activating enzymes ( E1 ) , ubiquitin-conjugating enzymes ( E2 ) , and ubiquitin ligases ( E3 ) . In plants , many ubiquitin E3 ligases have been implicated in growth , development , and responses to abiotic and biotic stresses [5 , 6] . The U-box E3 ligase SPL11 is a negative regulator of PCD and defense response in rice [7] . The spl11 mutant confers broad-spectrum resistance to rice pathogens and has elevated defense gene expression and ROS levels [8 , 9] . Recently , PUB13 ( plant U-box protein 13 ) , one of the SPL11 orthologs in Arabidopsis , was also found to negatively regulate PCD and resistance to biotrophic pathogens [10 , 11] , indicating that SPL11-like proteins have conserved functions in both monocot and dicot plants . Interestingly , PUB13 is required for the polyubiquitination and degradation of the PRR FLS2 in the presence of the PAMP effector flg22 . The polyubiquitination of FLS2 by PUB12/13 depends on the PUB12/13 phosphorylation mediated by the receptor-like kinase ( RLK ) BAK1 , a component involved in the brassinosteroid receptor BRI1 and in multiple PRR-mediated signaling pathways [12] . These findings suggest that the SPL11- and PUB12/13-mediated protein degradation pathway has a significant role in PTI signaling in plants . However , the components that interact with SPL11 or PUB13 in the control of PCD and defense response remain unknown . By enhancing active , GTP-bound small GTPases into the inactive GDP state , the small GTPase-activating proteins ( GAPs ) function as an important regulator in small GTPase-mediated cellular signaling [13] . Like other organisms , plants contain a large number of GAPs [14] , most of which are involved in cell morphogenesis and polarization . For example , the rice genome contains 85 GAP genes . Among them , 23 belong to the RhoGAP family [14] . Until now , only a few GAPs have been found to be involved in plant defense signaling . RanGAP2 in Nicotiana benthamiana , for example , by directly associating with the nucleotide-binding-leucine-rich repeat ( NB-LRR ) R protein Rx , regulates the partitioning of Rx in both the cytoplasm and nucleus , and thereby enhances resistance against Potato virus X ( PVX ) [15 , 16] . In barley , microtubule-associated ROP GAP MAGAP1 interacts with a small GTPase RACB and inhibits Blumeria graminis penetration by influencing the polar organization of cortical microtubules [17] . OsGAP1 is the only GAP in rice that is known to interact with the small GTPase YchF as a consequence of this interaction , OsGAP1 contributes to resistance to bacterial pathogens when it is constitutively expressed [18 , 19] . In rice , the small GTPase OsRac1 is a critical defense component because it integrates multiple ETI and PTI signaling pathways [20] . OsRac1 at the plasma membrane ( PM ) interacts directly with the rice blast NB-LRR R protein Pit and contributes to Pit-mediated ROS production and HR . Furthermore , the active form of Pit activates OsRac1 at the PM [21] . Interestingly , OsRac1 is also activated at the PM by its indirect association with the chitin-receptor complex formed by the chitin-binding protein OsCEBiP and the RLK OsCERK1 [22 , 23] . A recent report showed that OsRacGEF1 , which is a guanine nucleotide exchange factor ( GEF ) responsible for the activation of small GTPase by promoting GTP binding , activates OsRac1 at the PM by stimulating the formation of active , GTP-bound OsRac1 [23] . In addition , OsRacGEF1 is phosphorylated by OsCERK1 , and the activation of OsRac1 is triggered by the association of OsCEBiP/OsCERK1 and OsRacGEF1 . However , the components responsible for OsRac1 inactivation remain to be identified . In this study , we report that the RhoGAP SPIN6 protein interacts with SPL11 and is ubiquitinated in vitro and degraded in vivo by SPL11 via the 26S proteasome-mediated pathway . Silencing of Spin6 in transgenic rice causes PCD and enhanced resistance to the blast pathogen Magnaporthe oryzae and to the bacterial blight pathogen Xanthomonas oryzae pv . oryzae ( Xoo ) , suggesting that Spin6 plays a negative role in the regulation of PCD and immunity in rice . Strikingly , we found that SPIN6 interacts with OsRac1 and catalyze the active form of OsRac1 into an inactive form in rice cells . Together , these results show that SPIN6 is a key molecule that transduces defense signals from the E3 ligase SPL11 to the small GTPase OsRac1 for the regulation of PCD and innate immunity in rice .
To search for novel components in the SPL11-mediated PCD and defense signaling pathway , we identified eight SPL11-interacting proteins ( SPINs ) in a yeast two-hybrid ( Y2H ) screen [24] . Among them , Spin6 ( Os07g46450 ) encodes a RhoGAP protein with a Pleckstrin Homology ( PH ) domain , a RhoGAP domain at the N-terminal and a C-terminal coiled coil ( CC ) motif ( Fig . 1A ) . In a BLAST search of the rice genome using the Spin6 sequence as a query , we found two PH/GAP-type SPIN6 homologs ( Os03g11140 and Os03g24180 ) ( Figure S1 in S1 Text ) . Sequence alignment analysis using both the cDNA and protein sequences of these genes showed that Spin6 shares an identity of 61 . 4% , 81 . 7% at the nucleotide level and 40 . 7% , 79 . 7% at the protein level with Os03g11140 and Os03g24180 , respectively ( Table S1 and S2 in S2 Text ) . A protein sequence alignment analysis of the three rice genes with three Arabidopsis PH/RhoGAP orthologs revealed that the PH , GAP and CC domains in these proteins are highly conserved in both plants ( Figure S2 in S1 Text ) . In searching the rice genome databases , we found three Spin6 putative splicing isoforms , i . e . , Os07g46450 . 1/Spin6 , Os07g46450 . 2/Spin6 . 2 and Os07g46450 . 3/Spin6 . 3 . In this study , we used the longest isoform Spin6 because Spin6 . 2 lacks the PH and RhoGAP domains , and Spin6 . 3 only contains a truncated RhoGAP domain and a truncated C-terminal ( Fig . 1A ) . The alignment of the three protein sequences is shown in Figure S3 in S1 Text . Because Spin6 . 2 and Spin6 . 3 are unlikely to be functional rice Rho/GAP genes , we focused on the functional analysis of Spin6 in this study . A further analysis of the phylogenetic relationships among RhoGAP proteins in rice , Arabidopsis , human , and Drosophila revealed that RhoGAPs in rice and Arabidopsis can be assigned to two subgroups based on the composition of the N-terminal motif ( Figure S4A in S1 Text ) and that the evolutionary pathway of RhoGAPs has been quite different in plants than in humans and Drosophila ( Figure S4B in S1 Text ) . These results suggested that SPIN6 belongs to the highly conserved and evolutionarily specific subfamily of plant RhoGAPs . The interaction between SPIN6 and SPL11 in yeast was confirmed with a Y2H experiment with full-length SPIN6 , full-length SPL11 , the SPL11 ARM domain ( ARM ) , and a truncated SPL11 with a three-amino-acid deletion at C314P315T316 in the U-box domain causing loss of E3 ligase activity ( SPL11m ) [7] . The Y2H assay showed that SPIN6 strongly interacted with the two truncated forms , ARM and SPL11m , but interacted only weakly with the full-length SPL11 ( Fig . 1B ) . Further analysis of interacting domains in yeast revealed that the C-terminal of SPIN6 and the ARM domain of SPL11 are required for their interaction ( Figure S5A and S5B in S1 Text ) . To determine the interaction specificity between SPIN6 and SPL11 , we tested the interaction between SPL11 and the SPIN6 homolog RhoGAP protein Os03g24180 in yeast . The analysis showed that the two proteins did not interact ( Figure S6A in S1 Text ) . Similarly , we obtained negative result in the Y2H assay between SPIN6 and the SPL11 homolog protein OsPUB12 ( Os06g01304 ) ( Figure S6B in S1 Text ) . These results demonstrated that the interaction between SPL11 and SPIN6 is specific . To further confirm the interaction between SPIN6 and SPL11 , we performed an in vitro GST pull-down assay . The results showed that both GST fusion SPL11 and ARM proteins bind to SPIN6 ( Fig . 1C ) . Subsequently , a bimolecular fluorescence complementation ( BiFC ) assay in N . benthamiana revealed that SPIN6 interacts with SPL11 at the PM when leaves are co-infiltrated with the combinations of the NeYFP:ARM and Spin6:CeYFP plasmids or the NeYFP:SPL11m and Spin6:CeYFP plasmids ( Fig . 1D , first and second panel , respectively ) . However , no signal was observed with the combination of the NeYFP:Spl11 and Spin6:CeYFP plasmids , probably because the wild type ( WT ) SPL11 was not stable in N . benthamiana cells due to self-ubiquitination ( Fig . 1D , third panel , also see results in Fig . 2B ) . The results of the control combinations are shown in Figure S7 in S1 Text . These results suggested that SPIN6 interacts with SPL11 in vitro and in vivo . To determine whether SPIN6 is a substrate of SPL11 , we performed an in vitro ubiquitination assay that including the following purified proteins of E1 , E2 , MBP:SPL11 , MBP:SPL11m , the E3 ligase dead mutant [7] , and GST:SPIN6 were included . The E3 ligase activity of the wild-type SPL11 and SPL11m were confirmed by the immunoblot analysis with the anti-ubiquitin antibody ( Fig . 2A , second and third lane , respectively , in the second panel ) . Interestingly , high molecular weight bands were only observed in the reaction with MBP:SPL11 but not with MBP:SPL11m in the immunoblot with the anti-GST antibody ( Fig . 2A , second and third lane , respectively , in the first panel ) . To further confirm the ubiquitination result , we washed the GST:SPIN6 beads with the PBST solution ( Phosphate Buffered Saline with 0 . 5% Triton X-100 , [7] ) after the E3 ligase reaction and performed an immunoblot analysis either with anti-ubiquitin or anti-GST antibody . The analysis with the anti-GST antibody showed that the high molecular weight bands above GST:SPIN6 were only present in the reaction with the wild type SPL11 but not with SPL11m ( Figure S8A in S1 Text , second lane in the first panel ) and that the ubiquitinated GST:SPIN6 was only detected by the anti-ubiquitin antibody only in the reaction with the wild-type SPL11 ( Figure S8A in S1 Text , second lane in the second panel ) . These results clearly indicate that SPIN6 is ubiquitinated by SPL11 in vitro . To investigate whether the ubiquitination of SPIN6 by SPL11 can lead to instability of SPIN6 in vivo , we co-infiltrated different combinations of agrobacteria carrying the Spl11:Myc and GFP:Spin6 plasmids into N . benthamiana leaves with or without the 26S proteasome inhibitor MG132 . Total protein was extracted 3 days after the infiltration , and MG132 was infiltrated into the same leaves 18 h before tissue collection . The immunoblot analysis revealed that the GFP:SPIN6 was degraded by SPL11:Myc ( Fig . 2B , lane 1 in the second panel ) but that the SPIN6 degradation was inhibited by MG132 ( Fig . 2B , lane 2 in the second panel ) . In contrast , SPIN6 was not degraded when the mutated Spl11m:Myc plasmid without E3 ligase activity was expressed with or without the MG132 treatment ( Fig . 2B , lane 3 and 4 in the second panel ) . The SPL11:Myc fusion protein was not stable in N . benthamiana with or without MG132 treatment ( Fig . 2B , lane 1 and 2 in the first panel ) , probably due to self-ubiquitination . However , the SPL11 fusion protein was slightly visible when the immunoblot was overexposed for 15 min ( Figure S8B in S1 Text , second lane in the first panel ) and it was clearly visible for over 20 min ( Figure S8B in S1 Text , second lane in the second panel ) . To understand the biological function of Spin6 , we made an RNAi construct that targets the 302-bp 3’ UTR region in Spin6 ( Figure S9A and B in S1 Text ) . To search possible similar sequences in the rice genome , we performed a whole genome similarity search with the 302-bp silencing fragment using the Mega BLAST program at the NCBI website . The analysis showed that this fragment only specifically targets the Spin6 gene ( Figure S10A in S1 Text ) . Further , a BLASTN search using the RNAi fragment indicated that all of the possible 21-bp nucleotides to be generated from the RNAi sequence only target to the Spin6 sequence with 100% matches ( Figure S10B in S1 Text ) . The Spin6-silencing transgenic rice was generated by transforming the RNAi construct in the calli of Nipponbare ( NPB ) via Agrobacterium-mediated transformation . Over 20 independently transformed lines were obtained in the T0 generation . The presence of the RNAi transgene and the hygromycin gene was determined in both the T0 and T1 generation by PCR . We identified six homozygous lines that had a single transgene insertion in the genome and that showed cell death phenotypes in the greenhouse . To confirm the cell death phenotypes , we grew two homozygous lines of the T3 generation ( 16–2 and 22–2 ) in a growth chamber under normal growth conditions ( see details in Experimental Procedures ) . About 4 weeks after planting , obvious cell death-like lesions were evident in both RNAi lines but not in the NPB plants ( Fig . 3A , Figure S11A and B in S1 Text ) . The expression level of Spin6 was significantly lower in these two lines than in NPB ( Fig . 3B ) . The cell death phenotype was confirmed in the next three generations . Intriguingly , the transcription level of Spl11 was also significantly suppressed in the Spin6 RNAi lines ( Fig . 3B ) , indicating that the suppression of Spin6 transcripts might have a feedback effect on the expression of Spl11 . Because knock-out of Spl11 in rice enhances non-race-specific resistance to both M . oryzae and Xoo [7] , we also evaluated the resistance of Spin6 RNAi lines to both pathogens . When rice leaves were inoculated with the compatible M . oryzae isolate RO1–1 by the punch inoculation method [25] , the lesion size , spore number , and relative fungal biomass in the lesions were significantly lower in the two Spin6 RNAi lines ( 16–2 and 22–2 ) than in NPB ( Fig . 3C , D and E ) . Similarly , lesions caused by Xoo strain race 6 were much shorter in the Spin6 RNAi lines than in NPB ( Fig . 3F and G ) . To further confirm the phenotype of the Spin6 RNAi lines , we obtained a Spin6 T-DNA insertion mutant from Korea [26] . PCR-based genotyping analysis indicated that the T-DNA fragment was inserted into the C-terminal of the 13th intron of the Spin6 gene ( Figure S12A and B in S1 Text ) , and the transcription level of Spin6 was significantly reduced ( Figure S12C in S1 Text ) . Strikingly , spin6 displayed much more severe cell death phenotypes than to the RNAi lines ( Figure S13A in S1 Text ) and no seeds or few seeds were harvested from the mutant plants under high humidity and strong sunlight conditions . We evaluated the resistance phenotype of the mutant to M . oryzae and Xoo in growth chamber conditions . Like the Spin6 RNAi plants , the spin6 mutant plants showed enhanced resistance to M . oryzae isolate RB22 ( Figure S13B in S1 Text ) ; when inoculated with RB22 , spin6 plants supported lower M . oryzae spore production ( Figure S13C in S1 Text ) and lower fungal biomass than the wild-type Hwayoung ( Figure S13D in S1 Text ) . Similarly , lesions were much shorter on spin6 than on the wild-type Hwayoung when the plants were inoculated with Xoo race RB6 ( Figure S13E in S1 Text ) . Taken together , these results suggested that Spin6 negatively regulates both plant cell death and disease resistance to fungal and bacterial pathogens in rice . To further investigate which pathways are involved in the Spin6-mediated defense signaling pathway , we used chemical luminescence to monitor the dynamics of ROS generation in the Spin6 RNAi plants treated with the PAMPs chitin and flg22 , these PAMPs trigger PRR OsCEBiP/OsCERK1- and OsFLS2-mediated PTI signaling in rice [25 , 27] . The results revealed that ROS accumulation was significantly higher in the two Spin6 RNAi lines than in NPB in response to both chitin and flg22 treatments; the peak ROS level , which occurred about 10 min after the treatments were applied , was two- to four-times greater in the Spin6 lines than in NPB ( Fig . 4A and B ) . Even in the water control , the basal ROS level was three-times higher in the Spin6 RNAi lines than in NPB ( Fig . 4A and B ) . This finding was confirmed by quantification of the endogenous H2O2 level , which in the water control was about two-times greater in the Spin6 lines than in NPB ( Figure S14A in S1 Text ) . Similarly , the spin6 mutant also showed a higher ROS accumulation ( Figure S13F and G in S1 Text ) and endogenous H2O2 level ( Figure S14B in S1 Text ) in response to both chitin and flg22 treatments comparing to the wild type . Therefore , these results indicated that silencing and knock-out of Spin6 in rice enhances chitin- and flg22-triggered ROS accumulation . To determine whether Spin6 suppression alters the expression of defense genes , we analyzed the expression patterns of three defense-related genes ( PBZ1 , PAL , and PR1a ) in the Spin6 RNAi line 22–2 and in NPB after treatment with chitin and flg22 . At 1 h after chitin treatment , the expression of the genes PBZ1 and PAL was > 10-times greater in the Spin6 RNAi plants than in NPB , but the expression returned to the normal level in the RNAi plants at 3 h after chitin treatment ( Fig . 4C and D ) . At 1 and 3 h after chitin treatment , PR1a expression was more than 80 times in the Spin6 RNAi line than that in NPB ( Fig . 4E ) . Conversely , only PBZ1 was significantly induced in the RNAi line relative to NPB at 1 h after flg22 treatment ( Fig . 4F ) . The expression of both PAL and PR1a was not significantly induced by the flg22 treatment in either the RNAi line or NPB ( Figure S15A and B in S1 Text ) . Together , these results suggested that Spin6 plays a negative role in the regulation of PTI by suppressing ROS generation and defense gene signaling in rice . In addition , we investigated the expression pattern of Spin6- and defense-related genes in the Spin6 RNAi and wild type NPB plants before and after blast inoculation . Before inoculation , the expression of Spin6 and Spl11 was significantly lower and the expression of RbohB was slightly low in the Spin6 RNAi plants than that in NPB plants ( Figure S16A , B and C in S1 Text ) . The expression of other five genes , i . e . , OsRac1 , PR1a , OsNAC4 , PR5 and PBZ1 was the same in both genotypes ( Figure S16E , F , G and H in S1 Text ) . At 24 h after blast inoculation , the expression of Spl11 was induced but the expression of Spin6 and OsRac1 was reduced in both genotypes . Interestingly , except for the highly induced expression of OsNAC4 in both genotypes , the expression of the three defense-related genes , PR1a , PR5 and PBZ1 , was highly induced only in the Spin6 RNAi plants at 24 h after inoculation ( Figure S16E , F , G and H in S1 Text ) . These results provided further evidence for the enhanced resistance in Spin6 RNAi plants against M . oryzae as shown in Fig . 3A . Because RhoGAPs can facilitate small GTPases hydrolysis , we speculated that the RhoGAP SPIN6 might target some Rac proteins in rice . Among the seven OsRacs , OsRac1 is a crucial component in the regulation of plant cell death and innate immunity [21] . We analyzed the expression patterns of three components in the OsRac1-mediated complex: OsRac1 , OsSGT1 , and OsRAR1 . The qRT-PCR analysis revealed that , after both chitin and flg22 treatments , the expression of all three genes was significantly up-regulated in the Spin6 RNAi plants relative to NPB ( Figure S15C-H in S1 Text ) , suggesting a possible relationship between Spin6 and OsRac1 . Then , we performed a Y2H experiment to determine whether SPIN6 can interact with OsRac1 . The assay showed that SPIN6 interacts with OsRac1 in yeast ( Fig . 5A ) . This interaction was further confirmed by both Co-IP and BiFC assays in N . benthamiana . The Co-IP assay showed that GFP:SPIN6 can immunoprecipitate Myc:Rac1 , while neither GFP nor Myc controls were able to immunoprecipitate OsRac1 or SPIN6 ( Fig . 5B ) . In the BiFC assay , a yellow fluorescence at the PM of leaf cells was detected when the Spin6:CeYFP and NeYFP:OsRac1 plasmids but not the control combinations were co-expressed in N . benthamiana ( Fig . 5C; Figure S6 in S1 Text ) . Together , these results suggested that SPIN6 interacts with OsRac1 in vitro and in vivo . To determine whether SPIN6 possesses the RhoGAP activity that facilitate the hydrolysis of the GTP-bound OsRac1 , we performed an in vitro RhoGAP activity assay . The result showed that the fluorescence signal in the reaction containing the OsRac1 and SPIN6 proteins was as low as that in the two negative controls because the fluorescent GTP-bound OsRac1 was hydrolyzed to GDP-bound forms in the presence of the SPIN6 protein ( Fig . 5D ) . In contrast , the fluorescence signal in the reaction with the OsRac1 and GST proteins increased rapidly 10 min after the reaction began . No fluorescence signal was observed in the MBP and SPIN6 or MBP and GST combinations . These results suggested that SPIN6 can catalyze the GTP-bound OsRac1 into the GDP-bound inactive form of OsRac1 . To monitor in vivo GAP activity of SPIN6 toward OsRac1 , we used the FRET sensor called Raichu-OsRac1 [21] . The YFP/CFP fluorescence ratio of Raichu-OsRac1 provides an estimate of the activation state of OsRac1 in vivo , with low and high ratios of YFP/CFP fluorescence corresponding to low and high levels of OsRac1 activation , respectively . Using Raichu-OsRac1 , we monitored the activation level of OsRac1 in the presence of SPIN6 in vivo . Because the basal activation level of OsRac1 was very low ( Fig . 6A , left photo ) , thus , we expressed the PRONE domain of OsRac1GEF1 ( OsRacGEF1 PRONE ) that activates OsRac1 in rice cells [23] . The ratio of YFP/CFP fluorescence was significantly higher in the cells expressing OsRacGEF1 PRONE than in the cells expressing the control GUS vector , which showed that OsRacGEF1 PRONE activates OsRac1 in rice protoplasts ( Fig . 6A , middle photograph ) . In contrast , this activation was remarkably suppressed by the expression of SPIN6 ( Fig . 6A , right photograph ) . The normalized emission ratio ( cFRET/CFP ) was much lower in the rice protoplasts transfected with SPIN6 than that in the GUS control ( Fig . 6B ) . Taken together these results provided evidence that SPIN6 has GAP activity towards OsRac1 in rice protoplasts .
The rice U-box E3 ligase SPL11 and its Arabidopsis ortholog PUB13 play important roles in the regulation of both defense and flowering [7 , 10 , 11] . Previously , we identified eight Spin genes from the rice cDNA library in a Y2H screen [24] . Among them , SPIN1 is involved in flowering-time regulation . However , the component that transduces the SPL11 signal for PCD and defense response signaling was unknown before the current study . In this study , we found that SPL11 interacts with the RhoGAP SPIN6 in vitro and in vivo and ubiquitinates SPIN6 in vitro . Co-infiltration assays in N . benthamiana showed that SPL11 degrades SPIN6 via the 26S proteasome-dependent pathway . Silencing of Spin6 enhances PCD and non-race-specific resistance to both the rice blast and the bacterial blight pathogens and results in phenotypes similar to those in the spl11 mutant . We also found that Spl11 transcription is significantly down-regulated in Spin6 RNAi lines , indicating a feedback regulation of Spin6 over Spl11 . These results suggest that SPIN6 negatively regulates PCD and disease defense by directly associating with SPL11 . In addition , the results from the Co-IP and hydrolysis activity assays demonstrated that SPIN6 interacts with OsRac1 and can catalyze GTP-bound OsRac1 into the GDP-bound state in vivo . The FRET analysis in rice protoplasts demonstrated that SPIN6 has in vivo GAP activity towards OsRac1 . Our study has provided clear evidence for the linkage between a U-box E3 ligase-mediated ubiquitination pathway and a small GTPase-associated defensome system . Such a linkage has not been previously reported in any plant . Although RhoGAPs are known to have critical roles in diverse cell processes [13] , there are only few reports on the function of these proteins in plant immunity , and these include RanGAP2 in N . benthamiana [15 , 16] , MAGAP1 in barley [17] , and OsGAP1 in rice [18 , 19] . How these proteins are regulated in plant immune responses is largely unknown . In this study , we found that the RhoGAP SPIN6 is ubiquitinated and degraded by SPL11 via the 26S proteasome pathway in planta . The E3 ligase activity of SPL11 is required for the degradation . Because the C-terminal of SPIN6 is involved in the interaction with SPL11 in yeast , we speculate that SPL11 ubiquitinates the SPIN6 C-terminal region . It seems that SPIN6 is poly-ubiquitinated by SPL11 because many high molecular weight bands above the SPIN6 band were observed the in vitro ubiquitination assays . In addition , we found that the Spl11 transcript level is lower in the Spin6 RNAi plants than in NPB , suggesting that down-regulation of Spin6 also suppresses Spl11 expression probably due to an unknown feedback regulation . When the SPL11 and SPIN6 antibodies become available , it will be interesting to determine the relationship between SPL11 and SPIN6 at the protein level . Additionally , the Spl11 gene was demonstrated to be involved in flowering time regulation through mono-ubiqutinating the RNA-bing protein SPIN1 [24] . We also found that the Spin6 RNAi plants showed delayed flowering time under both short day ( SD ) and long day ( LD ) conditions ( Figure S17A and B in S1 Text ) . We checked the expression of the three flowering time-related marker genes; OsGI , Hd1 , Hd3a , we found that the OsGI and Hd3a are suppressed in RNAi lines under both SD and LD conditions , but for Hd1 , it is only suppressed in SD condition ( Figure S17C , D and E in S1 Text ) . The function of Spin6 in flowering time regulation will be further investigated . Our current data show that ROS levels are significantly up-regulated in both Spin6 RNAi and T-DNA insertion mutant plants after chitin and flg22 treatments . Even without any treatment , the basal ROS level and endogenous H2O2 content are higher in the Spin6 RNAi or mutant plants than in the wild type . This is consistent with previous reports of the high level of ROS and a rapid ROS burst in the spl11 mutant after treatment with an elicitor from the rice blast fungus [8 , 9] . In addition , the analysis of defense-related gene expression showed that PBZ1 is highly up-regulated after both chitin and flg22 treatments , while PAL and PR1a were up-regulated only after chitin treatment . These results suggested that the chitin- and flg22-triggered PTI signaling involves in SPIN6-meidated defense signaling regulation . However , what components are involved in Spin6-mediated defense signaling in rice requires further investigation A recent study showed that OsRacGEF1 acts as a guanine nucleotide exchange factor for OsRac1 [23] . OsRacGEF1 interacts with the chitin co-receptor OsCERK1 , and the activated OsRacGEF1 is required for chitin-driven immune responses and resistance to M . oryzae . Because SPIN6 is also involved in chitin-triggered immunity , the relationship between SPIN6 and OsCERK1 and OsRacGEF1 during rice immunity warrants investigation RhoGAP proteins regulate small GTPases by altering their GTP state [13] . In our study , we found that SPIN6 interacts with the rice small GTPase OsRac1 and has RhoGAP activity in that it catalyze OsRac1 hydrolysis . In addition , the transcription level of OsRac1 is highly induced in the Spin6 RNAi plants after chitin and flg22 treatment , and the expression of two major components in the OsRac1 complex , OsSGT1 and OsRAR1 , is significantly up-regulated . These results indicate that SPIN6 plays an important role in regulating OsRac1-dependent immune signaling in rice . As shown in previous studies , OsRac1 is a key signaling integrator for both PTI and ETI pathways and interacts with OsRBOH to transduce cell death and defense signaling in rice [20] . One important function of SPIN6 might be to maintain an optimum level of active OsRac1 so that all biological processes regulated by OsRac1 in rice cells are well controlled and balanced . A recent study found that OsRacGEF1 activates inactive forms of OsRac1 and is a positive regulator of rice immunity [23] . It will be important to determine the relationship between SPIN6 and OsRacGEF1 in the control of OsRac1 and to determine whether OsRac1 , OsRacGEF1 , and SPIN6 form a protein complex to regulate defense responses in rice . Furthermore , because OsRacGEF1 is phosphorylated by OsCERK1 [23] , future research should determine whether SPIN6 can also be phosphorylated by OsCERK1 and how SPIN6 contributes to chitin-mediated immunity . Over the last 10 years , our laboratory has obtained considerable information on the function of the rice U-box E3 ligase SPL11 in the regulation of PCD and defense . We previously found that SPL11 is a functional U-box E3 ligase and negatively regulates PCD and defense . In this study , we found that SPL11 ubiquitinates the RhoGAP SPIN6 and degrades it through the 26S proteasome-mediated pathway . We also found that Spin6 is a negative regulator of PCD and defense because the expression of the three defense-related genes , PR1a , PR5 and PBZ1 , is highly induced in the Spin6 RNAi plants after blast inoculation , consistent with their enhanced resistance to M . oryzae . In vitro and in vivo GAP analysis showed that SPIN6 is the RhoGAP protein of OsRac1 that can switch OsRac1 from active forms to inactive forms . Knocking out or down of the Spin6 gene may lead to the accumulation of active forms of OsRac1 , which causes ROS generation , defense activation and cell death . Because OsRac1 is a signal integrator of the PAMP elicitor chitin , which is perceived by PRRs like OsCERK1 , the SPL11-SPIN6-mediated pathway might connect with the chitin-mediated pathway to regulate PCD and immune responses . Based on previously published results and the new results from this study , we propose a working model to illustrate the relationships among SPL11 , SPIN6 , and OsRac1 in rice immunity ( Fig . 7 ) .
Japonica cultivar Nipponbare ( NPB ) was used for rice transformation . Rice seeds were germinated as previously described [28] . After germination , rice seedlings were transferred to soil and grown in a growth chamber at 22°C , 80% relative humidity ( RH ) , and a 12-h/12-h light/dark photoperiod . N . benthamiana seeds were germinated in the same manner as rice seeds . After germination , N . benthamiana seedlings were transferred to soil and grown in a growth chamber at 22°C , 70% RH , and a 16-h/8-h light/dark photoperiod . N . benthamiana plants were used for agroinfiltration when they were 4 to 7 weeks old . The construct pANDA-SPIN6RNAi was made for the generation of SPIN6 RNAi transgenic rice ( Figure S9A in S1 Text ) [29] . Agrobacterium-mediated transformation was used to generate the transgenic rice plants as described previously [30] . All genes accession and primer pairs used in this study are listed in SI data ( Table S3 in S2 Text ) . A punch inoculation method was used to inoculate rice plants with M . oryzae isolate RO1–1 as described by [25] . Briefly , leaves of 5- to 8-week-old rice plants were wounded with a hole-punch ( one leaf per plant ) , and 5 μl of an M . oryzae spore suspension ( 5x105 spores ml-1 ) was applied to the injured area , which was sealed with cellophane tape . The inoculated plants were kept in darkness at 80% RH for 24 h before they were transferred to a growth chamber at 28°C , 80% RH , and a 12-h/12-h light/dark photoperiod . After the plants had been in the growth chamber for 10 days , lesion size , spore number per lesion , and fungal biomass per lesion were determined . A leaf-clipping method was used to inoculate rice plants at the booting stage with Xoo isolate race 6 as previously described [31] . The Xoo-inoculated plants were maintained as described for M . oryzae-inoculated plants . Lesion lengths were measured 14 days after inoculation . RNA was extracted from rice leaves with Trizol reagent ( Invitrogen ) and was treated with Dnase I . A 1-μg quantity of RNA in a 20-ul reaction volume was used for cDNA synthesis using the Promega reverse transcription system . After the cDNA was diluted 5–10 times , 1 μl of the diluted cDNA in a 25-μl reaction volume with BioRad SYBR supermix buffer was used for real-time quantitative PCR with a BioRAD iQ2 machine and the following program: 40 cycles at 95°C for 15 s , 60°C for 30 s , and 72°C for 20 s . Three replications were performed . The data were normalized with ubiquitin , and the means of three replications are presented . ROS was detected as previously described [25] . Briefly , leaves were cut into pieces ( 0 . 25 cm2 ) and submerged in distilled water for 3 h . Three leaf pieces were then placed in a 1 . 5-ml microcentrifuge tube with 100 μl of luminol ( Bio-Rad Immun-Star horseradish peroxidase substrate 170–5040 ) , 1 μl of horseradish peroxidase ( Jackson ImmunoResearch ) , and 100 nM flg22 or 8 nM hexa-N-acetyl-chitohexaose , or distilled water as a control . The samples were immediately placed in a Glomax 20/20 luminometer ( Promega ) , and luminescence was measured at 10-s intervals for 20 min . The ProQuest yeast two-hybrid system ( Invitrogen ) was used according to the manufacturer’s protocol . SPL11-interacting genes were screened as previously described [24] . For interaction confirmation , the cDNAs of the genes Spl11 , Spin6 , and OsRac1 were separately cloned into the BD vector pDBLeu or the AD vector pPC86 . Corresponding BD and AD constructs were then co-transformed into the yeast strain Mav203 and selected on synthetic dextrose medium without Trp or Leu ( SD-Leu-Trp ) . The single transformant was picked up and diluted 10 , 100 , or 1000 times in sterilized-distilled water . For the interaction assay , 1 μl of each dilution was plated on SD-Leu-Trp-His medium with 0 mM or 40 mM 3-amino-1 , 2 , 4 , -triazole ( 3AT ) . The full-length Spin6 cDNA was cloned into the vector pET28a for in vitro transcription/translation of SPIN6 protein using the TNT rabbit reticulocyte lysate translation system ( Promega ) . The full-length Spll11 cDNA and the Spl11 ARM domain fragment were cloned into the vector pGEX-6p-1 , and the GST:SPL11 fusion proteins were expressed and purified according to the manufacturer’s instructions ( Amersham Biosciences ) . GST pull-down was performed as previously described [24] . Bound SPIN6 was detected by the streptavidin–horseradish peroxidase chemiluminescent method using the Transcend Nonradioactive Detection System ( Promega ) . Agroinfiltration was performed as described [32] . Briefly , Agrobacterium strain GV3101 with corresponding constructs was incubated at 28°C with shaking ( 220 rpm ) for 18 h . Bacteria were collected by centrifugation at 4000 rpm for 10 min and were resuspended to a final OD600 of 1 . 5 in MES buffer ( 10 mM MgCl2 and 10 mM MES , pH 5 . 6 ) with 150 nM acetosyringone . After 3 h , the suspensions were infiltrated into N . benthamiana leaves . After 3 days , leaf samples were collected for protein extraction or microscopic observation . Full-length cDNAs of Spin6 , Spl11 , and OsRac1 were separately cloned into vectors pSPYNE ( R ) 173 and pSPYCE ( M ) for BiFC assay [33] . The constructs were expressed in N . benthamiana by the agroinfiltration method . A Leica confocal microscope was used for YFP fluorescence observation with excitation at 515 nm and emission at 525 nM; images were captured with a Leica Microsystems camera ( Leica , Heidelberg GmbH ) . For Co-IP , GFP:SPIN6 and Myc:OsRac1 protein was isolated from N . benthamiana leaves using a native buffer as previously described [34] Co-IP was performed as previously described [25] . Briefly , 1 ml of protein solution mixed with 15 μl of anti-GFP antibody ( Roche ) was gently shaken at 4°C for 4 h . The samples were then mixed with 10 ul of protein G agarose beads and incubated at 4°C with gentle shaking overnight . The samples were washed three times with 1xIP buffer ( Sigma-Aldrich ) , combined with 50 μl of 1xSDS loading buffer , and separated by SDS-PAGE gel for immunoblot analysis using anti-GFP and anti-Myc antibody . The plasmid SPL11:Myc , or SPL11m:Myc ( m ) was co-expressed with GFP:SPIN6 plasmid by agroinfiltration in N . benthamiana . The RFP plasmid was co-infiltrated as a control . Tissues were harvested 3 days after infiltration for protein extraction . For protein level analysis , total proteins were detected using anti-GFP , anti-Myc , and anti-RFP antibody . For MG132 treatment , 50 μM MG132 was infiltrated with DMSO at 18 h before tissue was sampled . The Spin6 GAP domain fragment was cloned into the GST fusion vector pGEX-6p-1 ( GE Healthcare ) . OsRac1 cDNA was cloned into the MBP vector pMAL-c2 ( New England BioLabs ) . The proteins were expressed and purified according to the manufacturer’s protocols for GAP activity assay . OsRac1 protein at 1 μM was loaded with 10 μM mant-GTP ( Molecular Probes , M12415 ) in the following buffer: 20 mM Tris/HCl pH 8 . 0 , 50 mM NaCl , and 1 mM EDTA . After 10 min at 25°C , the Mant-GTP loaded OsRac1 was used for the GAP activity assay by adding 5 μM SPIN6GAP or GST ( control ) proteins in reaction buffer ( 200 mM Tris-Cl pH 8 . 0 , 500 mM NaCl , 10 mM EDTA ) . Samples were immediately placed into a Glo-MAX luminometer ( Promega ) for fluorescence detection ( at 10 min intervals for 30 min ) at 25°C . Each reaction was repeated three times , and means and standard errors are presented . The Raichu intramolecular FRET sensor was used to analyze in vivo GAP activity of SPIN6 toward OsRac1 [21] . Ten to 12 hours after transfection of Raichu-OsRac1 into rice protoplasts , the cells were imaged using an Olympus IX-81 inverted microscope with a Yokogawa CSU22 confocal scanner equipped with the cooled charge-coupled device camera EM-CCD C9100–02 ( Hamamatsu Photonics ) . Raichu-OsRac1 was excited using a 440-nm diode laser ( iFLEX 2000 , Point Source ) . The CFP and YFP filters were 480 ± 15 nm and 535 ± 20 nm , respectively . Back- ground fluorescence was subtracted , fluorescence bleed- through was normalized , and FRET efficiency was calculated according to published procedures [35] . Sequences were collected using the BLAST program from the rice TIGR database ( http://rice . plantbiology . msu . edu/ ) and the Arabidopsis TAIR database ( http://www . arabidopsis . org/ ) . For protein domain analysis , the SMART program was used ( http://smart . embl-heidelberg . de/ ) . The ClustalW 2 . 0 program [36] was used for sequence alignment , and GeneDoc ( http://www . nrbsc . org/gfx/genedoc/ ) was used for conserved residue shading . For phylogenetic analysis , MEGA 3 . 1 [37] was used for building the neighbor-joining tree based on the bootstrap test ( replications = 1000 , random seeds = 24054 ) . | Rice diseases are the major threat for stable rice production and food security worldwide . Deep understanding of the disease resistance pathway in rice is essential for effective control of the diseases . Although rice contains many E3 ubiquitin ligases , the function of their substrates in immune responses is still not fully understood . We previously characterized U-box E3 ligase SPL11 in rice that is involved in the regulation of cell death , immune responses , and flowering . However , how SPL11 interacts with its substrates to control cell death and immunity is not clear . In this study , we found that the SPL11 interacts with SPIN6 , ubiquitinates , and degrades the protein via the 26S proteasome pathway . Both the Spin6 RNAi and mutant plants show enhanced resistance to rice pathogens and activate defense gene expression and ROS generation . Importantly , we found that SPIN6 is the RhoGAP of the small GTPase OsRac1 , which is a key component in rice immunity . Our study provides further insights into the relationship between SPIN6 and its interacting proteins SPL11 and OsRac1 , and its function in the control of cell death and immunity in rice . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The RhoGAP SPIN6 Associates with SPL11 and OsRac1 and Negatively Regulates Programmed Cell Death and Innate Immunity in Rice |
Among hereditary colorectal cancer predisposing syndromes , Lynch syndrome ( LS ) caused by mutations in DNA mismatch repair genes MLH1 , MSH2 , MSH6 or PMS2 is the most common . Patients with LS have an increased risk of early onset colon and endometrial cancer , but also other tumors that generally have an earlier onset compared to the general population . However , age at first primary cancer varies within families and genetic anticipation , i . e . decreasing age at onset in successive generations , has been suggested in LS . Anticipation is a well-known phenomenon in e . g neurodegenerative diseases and several reports have studied anticipation in heritable cancer . The purpose of this study is to determine whether anticipation can be shown in a nationwide cohort of Swedish LS families referred to the regional departments of clinical genetics in Lund , Stockholm , Linköping , Uppsala and Umeå between the years 1990–2013 . We analyzed a homogenous group of mutation carriers , utilizing information from both affected and non-affected family members . In total , 239 families with a mismatch repair gene mutation ( 96 MLH1 families , 90 MSH2 families including one family with an EPCAM–MSH2 deletion , 39 MSH6 families , 12 PMS2 families , and 2 MLH1+PMS2 families ) comprising 1028 at-risk carriers were identified among the Swedish LS families , of which 1003 mutation carriers had available follow-up information and could be included in the study . Using a normal random effects model ( NREM ) we estimate a 2 . 1 year decrease in age of diagnosis per generation . An alternative analysis using a mixed-effects Cox proportional hazards model ( COX-R ) estimates a hazard ratio of exp ( 0 . 171 ) , or about 1 . 19 , for age of diagnosis between consecutive generations . LS-associated gene-specific anticipation effects are evident for MSH2 ( 2 . 6 years/generation for NREM and hazard ratio of 1 . 33 for COX-R ) and PMS2 ( 7 . 3 years/generation and hazard ratio of 1 . 86 ) . The estimated anticipation effects for MLH1 and MSH6 are smaller .
Lynch syndrome ( LS ) is an autosomal dominant inherited syndrome that increases the risk of cancer , primarily in the colon , the rectum and the endometrial lining of the uterus , and to a lesser degree also in the stomach , the ovary , the hepatobiliary tract , the urinary tract , the small bowel and the brain [1 , 2] . LS is one of the most common heritable cancer syndromes , accounting for up to 4% of the total colorectal cancer burden in Europe , where patients have up to 70% lifetime risk of developing colorectal or endometrial cancer at an early age [1] . LS was formerly known as hereditary non-polyposis colorectal cancer ( HNPCC ) , but when clinical criteria evolved to take into account not only colorectal cancer to identify families with LS [3 , 4] the name Lynch Syndrome became generally accepted [5] . Today the diagnosis LS is restricted to families with a known pathogenic germline mutation in one of the mismatch repair ( MMR ) genes MLH1 , MSH2 , MSH6 and PMS2 irrespective of family history [6 , 7] . The MMR system corrects indels or mismatches in the DNA , and is evolutionary conserved from bacteria to human [8] . In human the recognition of nucleotide mismatches is mediated by the protein heterodimers MSH2/MSH6 or MSH2/MSH3 , while the removal and resynthesis of nucleotides is mediated by MLH1/PMS2 [9] . LS is a heterogeneous disease with regard to tumor spectrum and age at onset [10] . Part of this phenotypic variation has been linked to specific MMR gene mutation . For instance , MLH1 mutation carriers are suggested to have a higher risk for colorectal cancer ( CRC ) and earlier age of onset , compared to MSH2 and MSH6 mutation carriers [11–15] . In general , MSH6 mutation carriers tend to have a later age of onset and lower penetrance for LS associated tumors , apart from endometrial cancer , compared to MLH1 and MSH2 mutation carriers [16–20] . An older age of onset and a lower overall risk for CRC has also been suggested for PMS2 mutation carriers [21 , 22] . However , LS display phenotypic variation in age of onset also within families and between families with the same mutation [23–25] . This variation is attributed to individual genetic differences modifying the effect of an inherited MMR mutation [26–31] . Another factor proposed to influence age at onset is genetic anticipation , defined as progressive earlier onset and severity of disease in successive generations within a family . This phenomenon is closely related to the disease mechanisms in several genetic disorders , e . g the neurodegenerative diseases Fragile X syndrome , Myotonic dystrophy type 1 and Huntington disease , in which trinucleotide repeat expansion directly influence expressivity and penetrance of disease [32] . Anticipation has also been observed in hereditary cancer for example familial melanoma , Li-Fraumeni syndrome , breast , ovarian and pancreatic cancer , and recently in the renal cell cancer syndromes von Hippel-Lindau and HLRCC ( hereditary leiomyomatosis and renal cell cancer ) [33–39] . In LS , a progressive decrease of age at CRC onset was proposed already in 1925 when the syndrome was first described [40 , 41] . However , it is complicated to estimate genetic anticipation and there are contradictory reports regarding its existence in LS , though the majority indicate anticipation [42–52] . Previous studies have applied various statistical methodologies , compiled different mutations and included subjects with LS associated mutations as well as subjects with only a clinical diagnosis . In light of these studies , we analyzed affected and unaffected mutation carriers in LS families throughout Sweden , to investigate signs of anticipation using two regression models with adjustment for potential confounders , including gene-specific effects .
In Sweden , families with suspected LS are referred to the regional department of Clinical genetics in Umeå , Uppsala , Stockholm , Linköping , Göteborg or Lund , for counceling and genetic testing . Out of this population-based cohort , families identified with a LS-associated MMR mutation that received genetic counseling in Lund , Stockholm , Linköping , Uppsala or Umeå between January 1990 and December 2013 were enrolled in this study . This project was approved in accordance with the Swedish legislation of ethical permission ( 2003:469 ) . All patients provided oral or written informed consent for genetic diagnostics as part of their routine clinical care . This anonymized genetic information may be used for research without further consent sought from the patients if approved by an ethical review board . Accordingly , this study was approved by the Regional ethical review board in Stockholm ( dnr 2014/1320-31 ) . Patient and family cancer history was reported at the time of genetic counseling and cancer diagnoses were further confirmed from medical records or pathology reports . A total of 239 families with proven pathogenic MMR variants described in [53] ( 96 MLH1 families , 90 MSH2 families including one EPCAM-deletion family , 39 MSH6 families , 12 PMS2 families , and 2 MLH1+PMS2 families ) , comprising 1029 mutation carriers , were identified in the cohort . One individual whose parents were both mutation carriers was excluded . Additionally , the sex of 11 carriers was unknown , and the age at diagnosis for an additional 14 was missing . We excluded these 25 individuals , leaving 1003 at-risk carriers with available follow-up information and sufficient pathological and medical information to be included in the study . We grouped the EPCAM-deletion family within the MSH2 families , as it is reported that a partly deleted EPCAM gene ( located upstream of MSH2 ) cause LS through reducing the expression of MSH2 in EPCAM-expressing tissues [54] . For statistical modeling purposes , we counted two families with mutations in both MLH1 and PMS2 as having mutations in PMS2 only ( unreported auxiliary analyses that excluded these families altogether or counted them as MLH1 showed that our findings are not sensitive to this decision ) . The follow-up period was defined as the time from birth until age at onset , and for individuals who were diagnosed with multiple Lynch-related cancers , age of onset was recorded as the time of first diagnosis . Our first analytic approach was the normal random effects model ( NREM ) proposed by Larsen et al . [45] , which has been used previously to test for anticipation in LS [43] and BRCA-mutation related cancers [55] . Let ni denote the number of carriers in the ith family , i = 1 , 2 , … , 239 , and let j = 1 , 2 , … , ni index the jth individual in family i . The NREM is given by Tij=μi+γZij+βXij+εij , ( 1 ) where Tij is the age of diagnosis in years for the jth member of family i ( “person ij” ) , μi is the family-specific random intercept representing a typical age of diagnosis in the ith family , Zij is person ij’s generation ( coded with respect to oldest observed generation in each family , as described in [41] and γ , the parameter of interest , is the mean change in age of diagnosis between consecutive generations , i . e . the anticipation effect . In the NREM , anticipation is indicated if γ < 0 . Collectively the Xij term represents any other covariate ( s ) of interest for person ij , the effect of which is given by β . The final term εij is the residual error , assumed to be independently and normally distributed with mean zero and variance σ2 . For each person who was not diagnosed with a Lynch-associated cancer during the follow-up period , the likelihood contribution is given by the normal survivor function , that is , the probability of being cancer-free at the age of last follow-up . We assume that the censoring mechanism is independent of the time to cancer diagnosis . Our second analytic approach , which is also a regression strategy , extends the Cox proportional hazards model that was used in [56] to test for anticipation in lymphoproliferative tumors . Person ij’s hazard for cancer diagnosis at age t is modeled as: λ ( t|Zij , Xij ) =λ0 ( t ) exp ( μ˜i+γ˜Zij+β˜Xij ) . ( 2 ) The function λ0 ( t ) is the overall baseline hazard function . In Daugherty et al . , the baseline hazard was assumed to be identical for all families , that is , μ˜i was not included in the model and within-family correlations were accounted for by robust sandwich-type covariance estimates . Here we add a random family-level effect μ˜i , similar to NREM , which makes the less restrictive assumption that all families’ baseline hazards are proportional to , rather than equal to , one another . We call this Cox model with family-level random effect COX-R . The remaining parameters are analogous to NREM . Specifically , γ˜ gives the generational effect of anticipation as a log-hazard ratio , with γ˜>0 indicating anticipation , and β˜ is the log-hazard ratio ( s ) for all other covariates . In addition to adjusting for sex , we also included mutational status in NREM and COX-R . In Eqs ( 1 ) and ( 2 ) , let person ij’s length-4 vector of covariates be given by Xij = {1[sexij = male] , 1[genei = MSH2] , 1[genei = MSH6] , 1[genei = PMS2]} , where 1[y] is the indicator function , equal to 1 if y is true , sexij is the sex of person ij and genei is the mutational status of family i . MLH1 serves as the reference category . We also investigated whether there were gene-specific effects of anticipation by substituting Zij in Eqs ( 1 ) and ( 2 ) with the four dimensional covariate vector . All analysis was done in the R software package R Core Team [57] . Code for maximizing the integrated partial likelihood of model ( 2 ) , marginalized over the random effects μ˜i , is provided in the R package COXME [58] .
Table 1 presents the clinical characteristics of our data and Fig 1 plots the Kaplan-Meier estimate of the time to first Lynch-associated cancer diagnosis , to give an overview of the age at onset in our cohort . During the follow-up period , 719 carriers were diagnosed with at least one Lynch-associated cancer and 171 were diagnosed with multiple Lynch-associated cancers . Overall , the median age of first diagnosis was 51 years ( 95% CI: 50–53 ) , but this varied with mutational status , being 49 years in both MLH1 and MSH2 patients and 58 and 67 years , respectively , for MSH6 and PMS2 patients . Based on this , in addition to adjusting for sex , we also included mutational status in NREM and COX-R analyses . Table 2 gives the estimates , standard errors , and Wald-type p-values for the anticipation effects only , and Table 3 provides all parameter estimates . As shown in Table 2 , the estimates of γ ( NREM ) and γ˜ ( COX-R ) are -2 . 1 ( p = 0 . 0001 ) and 0 . 171 ( p = 0 . 0013 ) , respectively . Both suggest the presence of anticipation: a 2 . 1 year decrease in the age of diagnosis per generation and a hazard ratio of exp ( 0 . 171 ) = 1 . 19 between consecutive generations . Because mutational status appears to confound the age of diagnosis , we also investigated whether there were gene-specific effects of anticipation , yielding one estimated anticipation effect for each analyzed MMR-gene . These are given in the bottom rows of Table 2 . In NREM , anticipation is estimated to be -1 . 8 ( p = 0 . 044 ) , -2 . 6 ( p = 0 . 003 ) , -1 . 1 ( p = 0 . 366 ) , and -7 . 3 ( p = 0 . 014 ) years per generation respectively , for MLH1 , MSH2 , MSH6 , and PMS2 . In COX-R , the corresponding log-hazard ratios are 0 . 127 ( p = 0 . 133 ) , 0 . 284 ( p = 0 . 001 ) , -0 . 005 ( p = 0 . 965 ) , and 0 . 618 ( p = 0 . 052 ) , representing hazard ratios of 1 . 13 , 1 . 33 , 0 . 99 , and 1 . 86 , respectively . In both models , the confidence intervals ( CIs ) for the anticipation effects of MSH2 and PMS2 lie far from their null values ( Table 2 ) , whereas there is greater uncertainty with regard to any possible effect of anticipation in MLH1 and MSH6 .
We investigated signs of anticipation in LS through the analysis of a large , Swedish population-based cohort and regression analyses suggest that anticipation exists in these families . The NREM analysis suggests that the age of diagnosis in families is decreasing by about 2 years per generation , and the COX-R analysis suggests a multiplicative increase in the rate of diagnosis of about 1 . 19 between generations . These regression analyses carry at least two important advantages over hypothesis testing approaches that compare the age of diagnosis between all parent-child pairs . First , they make use of the partial follow-up time from all at-risk carriers who have not yet been diagnosed; these individuals would otherwise be excluded from analysis . Second , the model-based structure allows for straightforward incorporation of genetic effects or other possible confounders . The underlying causal mutation evidently plays a role in the extent of anticipation , as our estimates varied between MMR genes . Among the MMR genes , the ordering of estimated anticipation effects was PMS2 , MSH2 , MLH1 , and MSH6 , with the largest effect in PMS2 ( 7 . 3 years/generation [NREM] or a hazard ratio of exp ( 0 . 618 ) = 1 . 86 [COX-R] ) . Although the small number of PMS2 families yielded correspondingly large uncertainty , these effects were still highly significant therefore this uncertainty does not invalidate the findings . For MSH2 , the estimated effect of anticipation was 2 . 6 years/generation or a hazard ratio of exp ( 0 . 284 ) = 1 . 33 . The results are comparable to those reported in several earlier studies ( for a review , see [41] ) . In an analysis of Lynch families from the Danish HNPCC Registry [45] , an anticipation effect of about three years/generation was reported but no differences between mutational status was found . A version of the same data was considered again in Boonstra , et al . [43] , who fit variants of both regression models considered here , reporting effects of 3 . 3 years/generation and hazard ratios of exp ( 0 . 22 ) = 1 . 25 . Neither model in that study adjusted for mutational status . Also , the Cox model did not include family-level random effects , as we do here; our approach is arguably a more accurate , although still simplified , reproduction of the true underlying hazard process . A later report analyzed the same Danish HNPCC Registry data with Bayesian modeling techniques [59] , allowing anticipation to be random between families . They estimated population-level gene-specific effects of anticipation , as performed here , for MLH1 , MSH2 , and MSH6 . They found respective anticipation effects of 2 . 8 , 2 . 5 , and 1 . 0 years , consistent with our findings . Several other studies based on anecdotal observation or analyses of affected parent-child pairs have found effects of anticipation varying between 5 . 5 and 10 years [44 , 47 , 49 , 50] . A notable exception from previous studies is Tsai , et al . [46] who found no evidence for anticipation in 475 parent-child pairs from the Johns Hopkins Hereditary Colorectal Cancer Registry; in part this may be explained by differences in eligibility as only 14 of the 475 parent-child pairs analyzed had verified disease-predispoing germline MMR gene mutations . The underlying mechanism for anticipation in heritable cancer is still unknown . However , it has been proposed that anticipation is caused by a progressive accumulation of germline mutations , due to the reduced DNA mismatch repair ability in mutation carriers [51] . Accordingly , haploid/monoallelic mutations in the MMR system affect the mutation load in the carrier prior to loss of the second allele , and accumulated alterations in the germ cells is transferred to the offspring [41] . Interestingly , there is an overrepresentation of mononucleotide repeats within and around the human MMR genes compared to other genomic regions , with an overrepresentation in the PMS2 gene [60 , 61] . It has been suggested that MMR proteins maintain the length of such microsatellites present within their own nucleotide sequences by an evolutionary mechanism operating by gene-protein interactions [60] . With the above arguments a deficient MMR system would propagate errors through generations and this would be most significant for mutations in the PMS2 gene , which is in accordance with our results . In addition , it has been shown that PMS2-deficient mice eggs forms embryos with an increased mononucleotide mutation rate , indicating that MMR mutations might affect germline mutation rate in a heterozygous state [62] . This also points to our results that PMS2 mutations carriers would display the most anticipation if the mutation load is inherited by the next generation . Noteworthy , PMS2 and MSH2 are not part of the same protein complex involved in recognition , excision and resynthesis of mismatched nucleotides [63] , nor does the MSH2 gene contain the same magnitude of mononucleotide repeats as PMS2 [60] . This together suggests a different underlying mechanism generating anticipation in MSH2 mutation carriers . For example , it is shown that MMR deficiency affect telomere shortening in human fibroblasts , and that this might influence heterozygous carriers of a MSH2 mutation in particular [64] . Moreover , in a recent study telomere shortening correlated significantly with age at onset in the MSH2 carriers , whereas the MLH1 carriers displayed longer telomeres and delayed age at onset [65] . Nevertheless , MMR mutation carriers with LS-associated cancer may have specific telomere-length dynamics but telomere shortening does not alone explain anticipation , as reported by Segui et al [66] , indicating that gene-specific dynamics and different mechanisms are involved . Despite a general concurrence with earlier studies , several caveats accompany our findings . Evidently , our study and previously published evidence that performed survival analysis for genetic anticipation in LS suggests that if genetic anticipation does exist , the effect is modest [42 , 43 , 45] . This makes anticipation a difficult problem statistically and challenges some of the clinical utility of our findings . At the population level , anticipation may well also be due to reasons other than genetic . For example , cohort effects arising from changes in treatment , diagnostic or environmental factors can also result in a decline in age at diagnosis . These effects should be visible both within family trees and in the entire population ( which is a mix of mutation carriers from different generations ) . This is in contrast to genetic anticipation , which would only be seen within each unique family tree . Voskuil , et al . found that the hazard ratio corresponding to generation decreased considerably in magnitude after adjusting for birth cohort [42] , although their final estimated hazard ratios for the effect of generation were still very close to our estimate of 1 . 2 . Statistically , birth cohort and generation are typically highly correlated , which can cause the resulting parameter estimates to be unstable . Boonstra , et al . [59] attempted to disentangle these effects by independently estimating secular trends in age of colorectal cancer diagnosis from a cancer registry of all colorectal cancers , and adjusting the Danish HNPCC Registry data for this estimated trend before analysis . Still , the results indicated as reported earlier in this section , population-level gene-specific effects of anticipation of 2 . 8 , 2 . 5 , and 1 . 0 years , respectively , for MLH1 , MSH2 , and MSH6 . Our estimated effects of anticipation decrease by about 0 . 7 years when we directly apply the secular trends estimated in Boonstra , et al . [54] . Furthermore , it has been argued that anticipation may be falsely detected due to fecundity bias [48] . Through repeated simulations of parent-child pairs in which no anticipation exists ( in truth ) but the fertility rate was positively correlated with age of diagnosis , Stupart , et al . demonstrated in a particular scenario that an apparent anticipation effect of about 1 . 8 years can manifest . Noteworthy , the greatest reduction in fertility was predominantly among those diagnosed before age 29 , affecting the fertility of the cohort as a whole . In our cohort , the Kaplan-Meier estimated proportion of patients free of diagnosis at age 29 was 96 . 5% , which suggests that fecundity bias due to these patients is likely to be small . Taken together , our findings are in line with those of previous studies . That being said , the study of genetic anticipation is both complex and statistically challenging . The ideal setting in the continuing assessment of fine variations in LS phenotype , such as anticipation , would be prospective , population-based datasets , together with state-of-the-art statistical methods . Still , a number of promising findings have been reported previously , yet often the statistical methods or small sample sizes have been limiting . We believe that the analyses performed in our study properly consider familial , genetic , and clinical parameters and therefore give a representative measurement of anticipation in Lynch Syndrome . | Genetic anticipation is a phenomenon where symptoms of a hereditary disease appear at an earlier age and/or are more severe in successive generations . In genetic disorders such as Fragile X syndrome , Myotonic dystrophy type 1 and Huntington disease , anticipation is caused by the expansion of unstable trinucleotide repeats during meiosis . Anticipation is also reported to occur in some hereditary cancers though the underlying mechanism behind this observation is unknown . Several studies have investigated anticipation in Lynch syndrome , the most common hereditary colorectal cancer syndrome , yet there is a debate concerning whether anticipation occurs and what underlying mechanism there is . The objective of this project is to study if anticipation is part of the clinical picture in Swedish families with LS , with the long term goal to enable better prediction of age at onset in family members . Our results suggest that anticipation occurs in families with mutation in MSH2 and PMS2 , while the evidence is equivocal for MLH1 and MSH6 . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"cancer",
"detection",
"and",
"diagnosis",
"medicine",
"and",
"health",
"sciences",
"genetic",
"diseases",
"cancer",
"risk",
"factors",
"cancers",
"and",
"neoplasms",
"oncology",
"mutation",
"hereditary",
"nonpolyposis",
"colorectal",
"cancer",
"mismatch",
"repair",
"dna",
"autosomal",
"dominant",
"diseases",
"clinical",
"genetics",
"genetic",
"causes",
"of",
"cancer",
"biochemistry",
"diagnostic",
"medicine",
"colorectal",
"cancer",
"nucleic",
"acids",
"heredity",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"repair",
"human",
"genetics"
] | 2017 | Genetic anticipation in Swedish Lynch syndrome families |
Many insects navigate by integrating the distances and directions travelled on an outward path , allowing direct return to the starting point . Fundamental to the reliability of this process is the use of a neural compass based on external celestial cues . Here we examine how such compass information could be reliably computed by the insect brain , given realistic constraints on the sky polarisation pattern and the insect eye sensor array . By processing the degree of polarisation in different directions for different parts of the sky , our model can directly estimate the solar azimuth and also infer the confidence of the estimate . We introduce a method to correct for tilting of the sensor array , as might be caused by travel over uneven terrain . We also show that the confidence can be used to approximate the change in sun position over time , allowing the compass to remain fixed with respect to ‘true north’ during long excursions . We demonstrate that the compass is robust to disturbances and can be effectively used as input to an existing neural model of insect path integration . We discuss the plausibility of our model to be mapped to known neural circuits , and to be implemented for robot navigation .
Orientation cues are required for spatial behaviours from following a straight line [1] to migrating across continents [2] ( for a theoretical proof see [3] ) . Idiothetic cues such as those generated in the mammalian vestibular system are useful for short time periods but are inherently problematic because of accumulating errors . To avoid this limitation , many animals , in particular insects , have developed an array of sensory systems to detect allothetic directional cues in their environments: magnetic ( butterflies [4] , moths [5] , ants [6] ) , wind ( moths [7] , ants [8] ) , and visual [solar compass— ( honeybees [9] , crickets [10] , locusts [11] , butterflies [12] , ants [13] , dung beetles [14] ) ; star compass— ( dung beetle [15] ) ] . The benefit of such accurate external compass systems is exemplified in the behaviour of desert ants , who utilise the sky polarisation pattern [16] . In a habitat with few if any landmarks , these ants can integrate the distance and directions travelled on a tortuous search path of up to a kilometre in length and make a direct return home when food is found [17] . Our main aim in this paper is to study the potential accuracy with which an insect can estimate its allocentric direction from the sky polarisation pattern , given realistic constraints on the environmental cues , the sensory system , and the various sources of disturbances . The primary directional cue used for path integration by central place foraging insects such as desert ants [18] is the sky ( sometimes called the celestial compass ) . The position of the sun ( or moon ) in the sky—as well as providing a direct directional reference point—determines the properties of light across the skydome including intensity and chromatic gradients , and a specific pattern of polarisation . Linear polarisation of light is the alignment of orientation of oscillation of the electromagnetic wave to a single plane . As light from the sun passes through earth’s atmosphere it undergoes a scattering process [19–22] producing differing levels of polarisation across the sky-dome relative to the position of the sun . From the point of view of an earth based observer , as the angular distance from the sun increases from 0° to 90° , the degree of linear polarisation in skylight increases , with the principal axis of polarisation perpendicular to the observer-sun axis , forming concentric rings around the sun . Angular distances above 90° have decreasing degree of linear polarisation [23] . The insect celestial compass has been studied extensively in the honeybee Apis mellifera [9 , 24] , the cricket Gryllus campestris [10 , 25– 27] , the locust Schistocerca gregaria [11 , 28] , the monarch butterfly Danaus plexippus [12] , the dung beetle Scarabaeus lamarcki [14] and the desert ant Cataglyphis bicolor [13] . Insects perceive polarised light through a specially adapted region of their upper eye known as the dorsal rim area ( DRA ) . For the ommatidia in this area , the light sensing elements ( microvilli ) do not twist relative to each other , resulting in units that are sensitive to specific polarisation angles . Ommatidia in the DRA are connected to polarisation sensitive ( POL ) neurons in the medulla of the insect optic lobe which follow a sinusoidal activation profile under a rotating linear polarisation input [10] . The maximum and minimum activation is separated by 90° , consistent with an antagonistic input from at least two polarisation-sensitive channels with orthogonal e-vector tuning orientation ( the e-vector is the electric vector component of the light’s electromagnetic energy and is orthogonal to the direction of propagation ) . The identity and spike-rate of each POL neuron thus encodes information about the angle and degree of polarisation respectively for the specific region of sky from which the associated ommatidia samples . An array of such sensing elements arranged appropriately may hence be sufficient to decode the sun position , without using any additional sky cues [29] . From the optic lobe the pathway for polarised light processing has been traced through several neuropils in the insect brain . Key processing stages for polarised light are the dorsal rim lamina and medulla , specific layers of the lobula , the anterior optic tubercle , and the lateral complex with giant synapses; before reaching a highly structured midline neuropil known as the central complex ( CX ) [28 , 30] . A variety of neuron types within the CX region have been shown to have polarisation dependent responses , including CX inputs in the form of three types of tangential neurons [28] ( TL1 , TL2 and TL3 ) which synapse with columnar neurons [11 , 31 , 32] in the ellipsoid body/lower division of the central body . Most strikingly , intracellular recordings from neurons in the CX protocerebral bridge have revealed an orderly polarisation tuning preference across the eight columns , described as an internal compass [33] . More recently , the same structure has been observed through neurogenetic imaging in Drosophila to act as a ring attractor encoding the heading direction of the insect relative to a prominent visual cue [34] . However , there remains an inconsistency between these observations , as the polarisation tuning appears to range from 0° to 180° with each successive column tuned ∼ 22 . 5° degrees apart [33] , whereas the fly’s compass covers at least 270° , changing by around ∼ 35° per column [34] and might be assumed to form a 360° representation of space [32] . In a recent model , we used anatomical constraints for processing within the CX to explain how compass information in the protocerebral bridge could be combined with speed information to carry out path integration , and subsequently steer home [35] . This model assumed a 360° compass across the 8 tangential cells ( TB1 ) of the protocerebral bridge ( PB ) could be derived from sky polarisation cues . In this paper , we first determine whether in principle such a signal can be recovered from a simulated array of POL-neurons stimulated by a realistic sky polarisation pattern . We further investigate whether this signal can deal with , or be plausibly corrected for , potential disturbances such as partially obscured sky , tilting of the sensor array , and the movement of the sun with passing time . We evaluate the potential accuracy of compass information both in absolute terms , and in the context of path integration . Finally we show how the discrepancy in biological data for the protocerebral bridge tuning pattern [33 , 34] might be resolved , by testing our model with artificial polarisation patterns .
This study investigates how navigating insects can transform solar light into an earth-based compass signal that is sufficiently stable and accurate to drive precise path integration behaviour . Fig 1 provides an overview of the modelling pipeline . We start with a physical model of skylight , which is used as input to a biomimetic sensor array based on the desert ant eye . We then take a more direct computational approach to generate compass output from the insect eye input , by defining a hypothetical neural architecture that will reconstruct the sun position from this input , with additional mechanisms to correct for tilt and for passing time . As the precise neural connectivity underlying these transformations in the insect is unknown , this is a proof of principle that can provide hypotheses for future investigation of this circuit ( see discussion ) . We then use the output of the compass as input to an existing biologically constrained model of path integration in the central complex , and test it in a closed loop agent simulation . The properties of our model are drawn from a variety of insects that are shown to have a celestial compass , as detailed in Table 1 , but with a specific focus towards the desert ant . To test our neural model , we need to simulate the incoming light using a skylight dome model . Previous computational studies of the insect POL-system have often copied the typical stimulus input from experimental studies , i . e . , a rotating linear polariser [45] . However , the topology of the ommatidia and the neural processing of the compass system in the insect brain have evolved under real sky conditions , hence using a more realistic input can be critical to understanding the function . Specifically , as we will show , the real sky pattern breaks the symmetry conditions that inherently prevent 360° directions being recovered from a simple linear polarisation cue . We use the skylight dome model described in [44] , which gives a very realistic luminance and linear polarisation information pattern ( a sample of its output is illustrated in Fig 2 ) . This model is the most accurate description of the skylight distribution currently available , and for a detailed description we refer the reader to the original work [44] which we follow directly; a brief description is also given in S1 Appendix . outlining the objective function used in our evaluation . Given the position of the sun and a set of points in the sky , this model generates the luminance , degree ( DOP ) and angle of polarisation ( AOP ) for those points . Tuning by geo-referenced input parameters allows realistic sky patterns to be estimated for specific locations . Therefore , plugging into the model the location from our own desert ant fieldwork site ( Seville , Spain ) allows us to run simulated experiments for desert ants . This way , we can study the response of their POL-sensitive neurons using near natural stimuli . We built a compass model to transform the responses of the POL-neurons into the desired activation patterns of the TB1-neurons used for path integration . Alhough the anatomical pathway is known [51] , the neurobiological processes on this pathway are as yet uncertain , so we have taken an information processing approach: given biologically realistic POL-neuron responses gathered from the skylight simulation we examine whether this input provides enough information for a biomimetic central complex model to drive steering . Fig 4 shows an overview of the model . The connection of POL-neurons to SOL-neurons in the solar layer implements a sum-of-sinusoids model to recover an estimate of the solar azimuth . The gating function adjusts the connection weights to compensate for tilting of the sensor array . The true compass layer uses the confidence of the estimate to predict and compensate for the changing sun position over time . We describe each of these steps in more detail below . The TCL-neuron output described above provides the required compass input that was assumed to be available in a previous anatomically constrained model of path integration in the insect central complex [35] . In that model , a set of 8 TB1 neurons with preferred directions {k ⋅ 45° | k = 1 , … , 8} were activated with a sinusoidal relationship to the heading of the agent . We thus use an exact copy of this CX model , replacing its idealised input with our polarisation-derived compass signal to test its efficacy and robustness .
We first evaluate our compass model in conditions where it always points towards the zenith . The average error in the absence of disturbance is J = 0 . 28° ± 0 . 1620° for N = 1000 sun positions homogeneously distributed on the sky-dome , and the average confidence was τs = 0 . 91 , which is quite high ( see Fig 8B and 8C to compare with regular confidence levels ) . The solar elevation strongly affects the polarisation pattern in the sky , and as a result the accuracy of the compass predictions . Fig 8B reports the error measured for different solar elevations , and different levels of disturbance . Note we measure the elevation as the angular distance from the horizon ( thus the zenith corresponds to an elevation of 90° ) . The blue line , which is hardly visible and lying on the bottom of the figure , is the error for samples without disturbance . The dashed line is the average confidence reported across all the reported disturbance levels . It is not hard to notice that , apart from the error , the confidence is also affected by the solar elevation . In the model , we use this latter effect to our advantage , to estimate the elevation from the confidence . Fig 9A–9C show how the different disturbance levels affect the estimation of the solar elevation , θ s ′ ( black dots—real , red dots—predicted ) , using the confidence , τs . Fig 9D shows the predicted against the real solar azimuth change rate . Fig 8C gives a summary of the effect of the disturbance on our model’s predictions . We notice that as the disturbance grows so does the error of the predictions , but the confidence drops . This suggests we should not trust the predictions of our model when the disturbance of the sensory input is more than 85% , but for lower disturbance levels the compass still gives predictions with less than 30° error , which can be sufficient for the path integration task ( see later results ) . Navigating ants are subject to large changes in their head pitch angle , particularly when carrying objects such as food or nest mates [52] . Here we assess how this might impact the accuracy of their celestial compass . As we described in the methods , we filter the connections between the POL- and SOL-neurons using a gating function . With this function deactivated , and thus all ommatidia providing input to the solar layer , the performance of the model drops significantly . Fig 10A , 10B and 10C demonstrates the increased error of the predictions for different sun positions as the sensor is tilted for δ ≈ 0° , 30° and 60° respectively , and Table 2 summarises the respective average error along with the overall error . Activating the gating function , the influence of each ommatidium to the responses of the solar layer becomes a function of the tilting parameters , producing much more robust results ( Fig 10D–10F ) . More specifically , the overall average error drops to J = 10 . 47° ± 0 . 12° ( N = 8 , 500 ) . The default parameters for the gating function were selected using exhaustive global optimisation . More specifically , we fixed the design and network parameters and explore the different combinations of the parameters , θg and σg , in the gating function . As the number of parameters in the function is very small and their range is also constrained , exhaustive global optimisation was not a very costly process . Fig 10G illustrates that the current combination of sensor layout and compass model perform best for a gating function with a ring shape of θg = 40° radius and σg = 13° thickness . The radius of the ring could be interpreted as the dominant angle of focus or the most informative direction . Moreover , our optimal main focus angle is 40° from the zenith point differs to the 25° angle , suggested in [25] . The sensor layout and the number of neurons in the computational model were based on biological data , but it is of interest to examine the effect of varying these parameters . The performance error of the compass ( including tilted conditions ) for a range of layout parameters is illustrated in Fig 11A . The green line on this figure indicates the receptive field with the lowest error for the given number of units . The performance with respect to the receptive field seems to be independent from the number of units used . More specifically , the best performance on average was for ω = 55 . 99° ± 0 . 33° . The error observed on the slice set by the green line in Fig 11A , where ω ≈ 56° , is illustrated in Fig 11B . This figure shows that there is a sharp drop of the error up to n = 60 units , after which there is not a significant improvement . A slice on the other axis , for n = 60 , is illustrated in Fig 11C , which shows that the best design parameters for the sensor are ω ≈ 56° receptive field and n = 60 number of units . The average error reported for these parameters is J = 10 . 47° ± 0 . 12° . However , the lowest error reported was J = 9 . 55° ± 0 . 12° for n = 336 and ω = 56° . The number of SOL- and TCL-neurons were selected based on the CX model described in [35] . However , we explored different populations of neurons and compare the performance to the one of the proposed model . Our results showed that less than 8 SOL inteneurons increase the error to J = 47 . 49° ± 0 . 32° , while more interneurons do not change the performance . Similarly , as long as we have at least 4 TCL-neurons , the performance of the sensor does not change for any number of SOL-neurons . To demonstrate the performance of the sensor in a more realistic scenario , we integrated the compass and path integration [35] models , testing how the compass accuracy affects the foraging and homing paths . We create an environment with a simulated sky and let an agent navigate in it . We guide our agent to food-source , using 133 different routes from Spanish desert ants Cataglyphis velox [54] and let the agent return to the nest using its path integrator . In addition , we test the performance of the agent under different sky conditions , by adding disturbance to the polarisation pattern . Fig 12 summarises the results of the above experiment . The faded coloured lines in Fig 12A ( even terrain ) and Fig 12B ( uneven terrain , illustrated in Fig 12C ) are the outward paths and the bold lines are the inward paths . The colour of the line identifies the different disturbance level . We use similar evaluation methods to [35] to allow direct comparison . Fig 12D and 12E show the overall performance of the agent in the path integration task with respect to the tortuosity of the inward route , τ = L C , where L is the distance from the nest and C is the distance travelled . The results show that in most cases , the agent is moving in the correct direction until it reaches the nest and then does a systematic search for the nest . An exception is for η ≈ 97% ( see S3 Fig ) , where the agent continues moving in the same direction not realising how far it has travelled . Overall , for less than η ≤ 90% disturbance the agent seems to navigate without noticeable problems . However , for higher disturbance levels we see a drop in the performance of the navigation task , walking at least twice the distance of the nest from the feeder for η ≈ 97% . The performance of the agent is affected very little by the uneven terrain , which shows that the tilting of the agent is not a problem anymore . S2 Fig . summarises the results for different input disturbance levels and steepness of the terrain . S3 Fig . illustrates the corresponding detailed paths of all the agents . The terrain used here is drawn from a normal distribution , allowing the agent to tilt for a maximum of δ = 47° . The outward paths are consequently distorted by compass and distance errors while following the predefined sequence of directions and distances , but the homing paths still successfully guide the agent back to the nest , suggesting that any systematic bias in compass or distance information caused by uneven terrain is balanced out between the outward and inward routes . However , it is clear that the uneven terrain introduces an extra level of moment by moment disturbance in the heading direction . In addition , we tested the performance of the sensor in longer runs , which take more time and hence will test the operation of the sensor’s time compensation mechanism ( Fig 12F and 12G ) . We multiply the dimensions of the arena and the outward paths of the ants by a factor of 100 , transforming the arena to 1 km × 1 km and the total run of the agent to 1 hour and 16 minutes . In this time ( from 10:00 am to 11:16 am ) the sun position changes by 23 . 82° clockwise . Fig 12F and 12G show that including the time compensation mechanism the agent successfully returns to the nest , while without it the path integration mechanism leads it away from the nest due to the change of the sun position . For detailed paths of multiple ant routes see S4 Fig . We have noticed that the output of our compass model , the TCL-neurons , is not identical to the electrophysiological responses of the locusts’ TB1-neurons reported in [33] . However , this is not surprising , as the testing conditions of the two experiments were very different . More specifically , in the locust experiment , the animal was pinned in a vertical position and its DRA was exposed to uniform light passing through a rotating polariser . On the contrary , we expose our sensor to realistic sky-light facing upwards , assuming that the head of the hypothetical animal is aligned with the horizon . Therefore , we tried to simulate the former experimental environment and compare the responses of our TCL-neurons to the TB1-neurons recorded from the desert locust . We found that the response of the simulated compass neurons closely resembles the double preference angles recorded in locust TB1-neurons [33] when stimulated by a rotating linear polariser under a uniform light source ( Fig 13B and 13D ) . This contrasts dramatically with the response of the same simulated neurons when exposed to the natural skylight pattern ( see Fig 13F ) . Moreover , calculating the preferred directions from the response of the simulated neurons under the linear polariser , for each column ( Fig 13C ) , produces results rather comparable to the locust ( Fig 13A ) . Note that for the linear polariser , the preferred direction has an inherent 180 degree ambiguity: for our simulation data ( Fig 13C ) we resolve this by taking the stronger of the two peaks ( effectively a random choice as this difference results from noise ) ; for the locust data ( Fig 13A ) we use the direction chosen in the original paper . These data have been interpreted in [33] as supporting a [0 , ∼180°] ‘map’ of polarisation directions across the protocerebral bridge , increasing by ∼ 22 . 5° per column ( see fitted line , Fig 13A ) . Our results suggest this effect may be a consequence of the experimental procedure rather than revealing the true directional preference—relative to the sky pattern—of these neurons , which may instead resemble Fig 13E . The responses of Fig 13A/13B and Fig 13C/13D are similar but not identical . For example , despite the similar range of their expected , ϕ ¯ max , and undisturbed preference angles , ϕ max * , in Fig 13A and 13C respectively , i . e . [90° , 270°] , their exact values are not identical . Note that the expected values try to approximate the undisturbed values of the real recordings . However , the number of recordings from the locust brain is limited and shows substantial variability ( see the full set of simulation and locust responses in S5 Fig ) . As a consequence it does not seem productive to attempt to quantitatively replicate the details of this activity pattern with our model , but rather would be more interesting to test the response of these neurons to a more realistic sky pattern , which we predict should have a substantial qualitative effect on the observed activation patterns .
In our model , each neuron in the SOL layer integrates the signal from all sensory units . The relative azimuth of the sensor unit in the array to the preferred direction of the compass unit determines its weighting . As a result , the response across the compass units effectively represents the direction in the sky with the highest degree of polarisation , the cross-solar azimuth , from which the solar azimuth can be directly inferred . Our model thus counters the common assumption that a polarisation-based compass sensor must inherently have a 180° degree ambiguity and requires some additional signal to resolve the 360° directionality ( see e . g . [49 , 57] ) . Another consequence is that the best compass performance is not when the sun is on the horizon ( thus producing the maximum degree of polarisation , largely in one direction , in the zenith ) as has been sometimes assumed . In fact , for sun exactly on the horizon or exactly on the zenith , precise symmetry in the resulting polarisation pattern will result in ambiguity and low confidence in our model . The highest confidence occurs when the cross-solar azimuth falls within the receptive field of the sensor ( modulated by the gating function ) , which corresponds to a solar elevation θs ≈ 28° ( i . e . , nearer to , but not on , the horizon ) . Note however that for higher elevations , specifically , for 60° ≤ θs ≤ 90° the sun itself would fall in the receptive field of the sensor . This suggests a parallel processing system based on sky luminance could form a complementary mechanism for determining the solar azimuth that would be accurate for solar elevations where the polarisation one is not . A speculative pathway for this could originate in the two out of the eight photo-receptors in the ommatidia of the desert ants that are sensitive in a wider range of the spectrum [58] ( see Fig 3C ) , which could detect a sufficient light intensity gradient , and ( in a similar way to our POL to SOL processing ) form a novel skylight intensity compass . Alternatively or in parallel , the position of the sun is likely to also be detected by the non-DRA ommatidia of the compound eye , which are much better equipped for this task due to their smaller acceptance angle [38] . The two pathways could then be combined in the CX to form a complete insect celestial compass , consistent with the observation [58 , 59] that the insect’s compass appears to integrate multiple modalities of light [60 , 61] . Specifically , recent neurophysiological results show that all polarisation sensitive neuron types in the CX also show azimuth-dependent responses to an unpolarised UV or green light spot [57 , 62] . Our model represents a proposed mapping from POL to TB1 neurons in a computational form , i . e . , using a weight matrix derived from theoretical considerations rather than following details of the neural connectivity in the insect brain . Here we consider whether there is a plausible neural substrate for this computation . Tangential neurons ( TL ) of the lower central body ( CBL ) represent the actual input of the polarisation pathway to the CX , with at least three TL types which are all polarisation sensitive . As their name suggests , these neurons provide input tangentially across all columns in the CBL . As such , they could form the basis for the ‘fully connected’ mapping in our model between POL- and SOL-neurons , as CX columnar neurons innervating the same CBL region could potentially sample from all POL inputs . A plausible candidate would be the CL1 neurons: their receptive field is about 60° wide and their signal-to-noise ratio is relatively low [11] . There is evidence that CL1-neurons may be homologous to the E-PG neurons of the ellipsoid body ( EB ) in flies [34 , 63] , which represent landmark orientation and are used for visual navigation . This suggests the same neurons may get information from the visual field , such as the horizon line , which could provide the pitch and roll information that our model assumes will be integrated at this stage of processing to correct for tilting . However there is no direct evidence as yet to support the existence of our proposed gating function . A fascinating possibility is that this putative TL-CL1 mapping , which would need a rather well-tuned set of weights to extract the compass heading , could be a self-organising network , or at least could be calibrated by the experience of the animal , e . g . , if it makes coordinated rotations under the sky , as many insects have been observed to do [15 , 64] . Columnar ( CL1 ) neurons innervate the protocerebral bridge ( PB ) and are presumed to connect to TB1 neurons , hence this could form the physiological basis of our model’s connections from SOL- to TCL-neurons . In other insects , e . g . fruit flies , the equivalent neurons to CL1 have been shown to form part of a ring-attractor network to encode heading relative to a visual target [34] , which can also hold and update this information in the dark ( based on self movement ) . The PB has recurrent connections to the lower central body ( ellipsoid body in flies ) which could provide the feedback hypothesised in our model to compensate for time [65 , 66] . Alternatively , it has been noted [67] that there is a potential neural pathway from circadian pacemaker circuitry in the accessory medulla to the PB , which could provide an alternative way to adjust the compass with the time of day . As shown in our results ( see also [29] ) it may be difficult to interpret the real encoding principles of these neurons using isolated cues if they have evolved to be tuned to the combined input pattern of the real sky , and are potentially modulated by time and the tilting orientation of the animal . Specifically , we see that the robust [0 , ∼ 360°] representation of direction in our simulated TCL neurons appears to be a noisy [0 , ∼ 180°] representation when using a rotating polariser as the stimulus , resembling the data from [33] . We found that the resolution and receptive field of the sensor are optimal for n = 60 units resolution and ω = 56° receptive field . However , the DRA of Cataglyphis has more units ( ommatidia ) and broader receptive field in the frontocaudal axis ( ωa ≈ 120°; see Fig 3A ) . This asymmetric design of the DRA might be explained if we assume the ant’s head is tilted more often around the mediolateral axis; as they do not appear to significantly stabilise their head orientation while running up and down hills [68] or while carrying a load [52]; whereas there is some evidence that they do stabilise when their body is tilted around the frontocaudal axis [68 , 69] . Therefore more samples on the frontocaudal axis would increase the confidence of their compass when running . Other insect species have distinctive differences in the layout of their dorsal rim: in the precise number of ommatidia , their alignment pattern , their acceptance angle and their spectral sensitives ( see Table 3; for a review see [56 , 70] ) . These might suggest similar adaptations to the specific requirements of habitat , foraging time , task and motor control . In this study we used a somewhat generalised DRA , as it was specifically our intent to consider how we might construct an equivalent sensor for a robot , preferably at a low cost . The insect celestial compass has already inspired the design of a number of polarisation compass sensors , particularly as a robust alternative to magnetic compass sensing for robot applications [56] . The approach applied in the Sahabot [47 , 49 , 50] introduced a number of biomimetic aspects , including POL-OP units , but used only 3 , each one with a relatively small acceptance angle , all pointing towards the zenith , and oriented in angles with 60° difference . The dominant polarisation direction was recovered either by rotational scanning or by using a look up table , with additional light sensing used to decide between the solar and cross-solar directions . The output angular error reported for flat terrain experiments is 1 . 5° . Chu et al . [73] improved this design , using blue filters on photo-receptors with wider acceptance angles , ρ ≈ 53° , to obtain a minimum of 0 . 2° angular error . Ma et al . [74] and Xian el al . [75] followed the same line , optimising the DOP and AOP extraction using the least-squares method . More recently Dupeyroux et al . [76 , 77] used UV sensors with orthogonally aligned HNP’B polarisers that were rotated 360° every 42 seconds using a stepper motor . Calculating a compass direction from this method produced from 0 . 3° to 1 . 9° peak errors in clear and cloudy skies respectively . Similar robot implementations include those by [78 , 79] . Alternative approaches use a camera [80–84] or multi-camera system [79 , 80 , 85] , or specialised image sensors with different polarisation sensitivity for each pixel [56 , 86] . Good results are obtained by Stürzl et al . [84] , who built a single camera sensor with 3 lenses and 3 polarisers oriented in different angles . The sensor estimates the angle and degree of polarisation of the skylight , and fits a sky-model on the input stream to estimate the parameters , which are the solar azimuth and elevation . In addition , they also estimate a covariance matrix that shows the confidence of their prediction . Their approach also works in tilted environments by integrating inertial measurement unit ( IMU ) data . Finally , Yang et al . [87] follow up Sahabot’s work , use 2 POL-OP units oriented in different angles from the zenith , placed on a plane and use the scanning technique in order to get values from different angles . They show that their sensor can estimate the solar azimuth and elevation in clear sky conditions with MAE 0 . 2° and 0 . 4° respectively . Our model suggests an alternative sensor design , in which a larger number of POL-OP sensors are used to sample specific areas of the sky , but these do not form a complete image as in the camera-based systems described above . Such a sensor could be built from off-the-shelf components , e . g . , using pairs of UV photodiodes and linear polarisation filters to imitate dorsal rim ommatidia photo-receptor neurons . The components could be mounted on a dome , creating a similar DRA to ant eyes . As we have shown , the subsequently computation to recover heading direction is relatively low-cost and could easily be carried out on a robot-compatible microprocessor , which could also run our path integration model . We hope to build this sensor and test it on a robot in future work . As well as building a physical implementation , there are several other ways in which this model could be developed in the future . One consideration already discussed above would be to integrate parallel processing of luminance and spectral cues , which can provide complementary information to polarisation , and thus enhance the reliability with which the solar azimuth can be determined over a wider range of conditions . A second would be to examine whether a more direct mapping can be made between the computational processing we have proposed and the detailed neuroanatomy of the layers intervening between the POL neurons in the medulla and the compass neurons in the CX . Finally , we believe it is key that such a model remains grounded in understanding of the real task constraints the circuit needs to support , i . e . , the natural environment conditions under which insect path integration evolved and operates . | We propose a new hypothesis for how insects process polarised skylight to extract global orientation information that can be used for accurate path integration . Our model solves the problem of solar-antisolar meridian ambiguity by using a biologically constrained sensor array , and includes methods to deal with tilt and time , providing a complete insect celestial compass output . We analyse the performance of the model using a realistic sky simulation and various forms of disturbances , and compare the results to both engineering approaches and biological data . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"compasses",
"engineering",
"and",
"technology",
"locusts",
"social",
"sciences",
"neuroscience",
"animals",
"insect",
"pests",
"animal",
"behavior",
"deserts",
"eyes",
"pests",
"zoology",
"hymenoptera",
"animal",
"cells",
"ants",
"behavior",
"ecosystems",
"head",
"insects",
"agriculture",
"measurement",
"equipment",
"arthropoda",
"cellular",
"neuroscience",
"psychology",
"eukaryota",
"animal",
"migration",
"cell",
"biology",
"anatomy",
"ecology",
"equipment",
"animal",
"navigation",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"ocular",
"system",
"organisms",
"terrestrial",
"environments"
] | 2019 | From skylight input to behavioural output: A computational model of the insect polarised light compass |
MOB1 protein is a core component of the Hippo signaling pathway in animals where it is involved in controlling tissue growth and tumor suppression . Plant MOB1 proteins display high sequence homology to animal MOB1 proteins , but little is known regarding their role in plant growth and development . Herein we report the critical roles of Arabidopsis MOB1 ( AtMOB1A ) in auxin-mediated development in Arabidopsis . We found that loss-of-function mutations in AtMOB1A completely eliminated the formation of cotyledons when combined with mutations in PINOID ( PID ) , which encodes a Ser/Thr protein kinase that participates in auxin signaling and transport . We showed that atmob1a was fully rescued by its Drosophila counterpart , suggesting functional conservation . The atmob1a pid double mutants phenocopied several well-characterized mutant combinations that are defective in auxin biosynthesis or transport . Moreover , we demonstrated that atmob1a greatly enhanced several other known auxin mutants , suggesting that AtMOB1A plays a key role in auxin-mediated plant development . The atmob1a single mutant displayed defects in early embryogenesis and had shorter root and smaller flowers than wild type plants . AtMOB1A is uniformly expressed in embryos and suspensor cells during embryogenesis , consistent with its role in embryo development . AtMOB1A protein is localized to nucleus , cytoplasm , and associated to plasma membrane , suggesting that it plays roles in these subcellular localizations . Furthermore , we showed that disruption of AtMOB1A led to a reduced sensitivity to exogenous auxin . Our results demonstrated that AtMOB1A plays an important role in Arabidopsis development by promoting auxin signaling .
In recent years , the Hippo signaling pathway has emerged as a very important pathway for animal development [1] . This highly conserved pathway was initially identified in Drosophila as a key pathway controlling organ size , and later was shown to play a role in controlling cell fate and pattern formation in mammals [2–5] . The core part of the pathway is a phosphorylation cascade composed of four key components in mammals and Drosophila: a Ste20-like Ser/Thr protein kinase Mst1/2 [Hippo ( Hpo ) in Drosophila] [6 , 7] , an NDR-family protein kinase Lats1/2 [Warts ( Wts ) in Drosophila] [8 , 9] , and two kinase regulatory components , Sav and MOB1 ( Sav and Mats in Drosophila ) [10 , 11] ( S1 Fig ) . Mst1/2 phosphorylates MOB1 and Lats1/2 , and activates Lats1/2 . MOB1 can bind to Lats1/2 and potentiate its intrinsic kinase activity . The activated Lats1/2 in turn phosphorylates and inactivates a transcriptional co-activator YAP/TAZ ( Yorkie in Drosophila ) [12] . YAP/TAZ is an effector of the Hippo pathway . Phosphorylation of YAP/TAZ results in its cytoplasmic retention , largely by facilitating its interaction with 14-3-3 proteins . Dephosphorylation of YAP/TAZ promotes its nuclear localization where it interacts with transcription factors and regulates gene expression . Drosophila mutants of core components in this pathway , such as hpo , wts , mats , sav , showed larger organs . In mammals , Hippo signaling controls patterning and differentiation of airway epithelial progenitors , mammary gland differentiation , intestinal fate , cardiovascular , liver , pancreas , central nervous system , and lymphocyte development [2] . It also regulates stem cell self-renewal and cell polarity in animals [2 , 13 , 14] . Recently , it was reported that the Arabidopsis thaliana MOB1A gene is required for tissue patterning of the root tip [15] and the development of both sporophyte and gametophyte [16] . MOB1 proteins in plants and animals share high sequence homology [11] . It is tempting to hypothesize that the Hippo pathway may also function in plants . However , very little is known regarding how the hypothesized Hippo pathway may regulate plant growth and development . The plant hormone auxin plays critical roles in plant growth and development . Local auxin biosynthesis , polar transport , and auxin signaling all contribute to proper plant growth and development . The best characterized tryptophan-dependent auxin biosynthesis pathway is the indole-3-pyruvate pathway , in which tryptophan is converted into indole-3-pyruvate by TAA/TAR family of amino transferases . Indole-3-pyruvate is then converted into IAA by YUC family of flavin-containing monooxygenases [17–21] . Auxin biosynthesis is temporally and spatially regulated [22 , 23] . Auxin transport is carried out by auxin influx carriers AUX1/LAXs , auxin efflux carriers PINs , and ABCB transporters [24] . Both local auxin biosynthesis and polar transport are important for generating auxin gradients and maxima , which are perceived by auxin receptors . The best characterized auxin receptor is TIR1/AFBs and Aux/IAA co-receptor complexes [25 , 26] . Disruption of auxin biosynthesis , polar transport or signal transduction pathways leads to defects in almost every aspect of developmental processes , such as flower , embryo , root , and leaf development [22 , 27 , 28] . For example , auxin biosynthetic mutants yuc1/4/10/11 quadruple mutants are defective in embryogenesis , and auxin signaling mutants such as mp fail to develop normal hypocotyls and roots [23] . Auxin transport mutant pin1 develops pin-like inflorescences , which was also observed in auxin signaling mutant mp and npy mutants [29 , 30] . Although it has been well documented that auxin plays essential roles in plant development , little is actually understood regarding how auxin gradients are translated into guiding proper developmental events . In this paper , we provide evidence that links AtMOB1A , which is homologous to a key component of the animal Hippo pathway , to auxin-mediated plant organogenesis and development . We conducted a genetic screen for mutants that could enhance the phenotypes of pid , which is defective in auxin signaling and transport [31 , 32] . One of the pid enhancers , ncp1 ( no-cotyledon in pid 1 ) failed to develop cotyledons in pid background . We further showed that ncp1 single mutant displays strong developmental defects in early embryos , seedlings , and in adult plants . NCP1 encodes a protein with significant homology to the animal MOB1s , a core component of the Hippo pathway . We showed that NCP1/AtMOB1A probably has biochemical activities similar to those of animal MOB1 , because the Drosophila MOB1 ( Mats ) can fully rescue the developmental defects of ncp1/atmob1a . The atmob1a mutant showed synergistic genetic interactions with known auxin biosynthetic , transport , and signaling mutants , suggesting that AtMOB1A functions in parallel to auxin pathways or affecting some aspects of auxin biology . Furthermore , disruption of AtMOB1A led to a decrease in sensitivity to auxin treatments and down-regulation of auxin reporters including DR5-GFP , ProARF7:GUS , and ProARF19:GUS . Our findings demonstrate that AtMOB1A likely promotes auxin signaling , thus impacting various Arabidopsis developmental processes .
Genetic enhancement has been widely used to identify components in signaling and metabolic pathways . We previously identified npy1 as a genetic enhancer of yuc1 yuc4 , which are defective in auxin biosynthesis . NPY1 is a key component of a signaling pathway responsible for auxin-mediated organogenesis [29] . Previous studies have shown that several Arabidopsis auxin mutants/mutant combinations , including npy1 , yuc1 yuc4 , wag1 wag2 , pin1 , and wei8 tar2 , have no cotyledons when combined with pid , which encodes a protein kinase important for auxin signaling and transport [20 , 29 , 30 , 33] . Therefore , pid provides a sensitized background , and cotyledon formation serves as an easy phenotypic readout for us to genetically identify additional components in auxin-mediated plant development . We conducted a genetic screen for enhancers of pid and isolated a new mutant that lacked cotyledons . We name the mutant as ncp1 ( no-cotyledon in pid 1 ) . At seedling stage , ncp1 pid failed to develop cotyledons , but they appeared to have normal hypocotyls and roots ( Fig 1A and 1C ) . The no-cotyledon phenotype of ncp1 pid was highly penetrant: the majority ( 90% ) of the mutants completely lacked both cotyledons , while some plants occasionally developed one cotyledon ( Table 1 ) . Interestingly , ncp1 pid plants could develop true leaves , however , they were abnormal in morphology and vascular development ( S2 Fig ) . The ncp1 pid plants were able to transition from vegetative growth to reproductive development , but their inflorescences were all pin-like and failed to produce fertile flowers ( Fig 1B ) . The no-cotyledon phenotype in seedlings of ncp1 pid was caused by defects occurred during embryogenesis . In mature embryos , the cotyledon formation was abolished in ncp1 pid , while two cotyledons in WT and two or three cotyledons developed in pid ( Fig 1D ) . The observed no-cotyledon phenotype was dependent on the presence of the pid mutation . We genotyped 48 individual plants that showed the no-cotyledon phenotype and found out that they were all pid homozygous , suggesting that the phenotype was dependent on the presence of the pid mutation . We further analyzed the progenies from a single ncp1+/- pid+/- plant , 22 of 427 seedlings ( about 1/20 ) showed the no-cotyledon phenotype , indicating that the phenotype was caused by two un-linked recessive mutations , i . e . pid and ncp1 . We crossed ncp1+/- pid+/- to Arabidopsis Landsberg ecotype and allowed the F1 plants to self-fertilize to generate a mapping population . In the F2 mapping population , we isolated 1325 seedlings that failed to develop cotyledons from about 26 , 000 F2 individuals . We found that the no-cotyledon phenotype was linked to two genetic loci: one on the bottom arm of chromosome II and the other on chromosome V . The Chromosome II locus is pid , further supporting that the no-cotyledon phenotype was dependent on pid . We narrowed the mapping interval on Chromosome V down to about 140 kb , between the two genetic markers on K9E15 and MRA19 ( Fig 1E ) . We sequenced all of the open reading frames ( ORFs ) in the mapping interval and identified a G to A conversion at the splicing junction of the second intron and the third exon of the gene At5g45550 . Further analysis of At5g45550 cDNA from the ncp1 mutant plants revealed that the mutation caused a single base-pair shift of the splicing acceptor of the second intron and the deletion of the first G of the third exon . The mutation led to a frame shift after the Lys24 , and introduced a premature stop codon ( Fig 1F ) . Therefore , this mutant is likely a null allele . To further confirm that the identified mutation in At5g45550 was responsible for the observed no-cotyledon phenotype in pid background , we transformed a genomic fragment containing the coding region and its up- and down-stream regulatory sequences of At5g45550 into ncp1-/- pid+/- plants . All of the T1 transgenic plants ( 341 in total ) had two or three cotyledons . We genotyped the T1 plants and found that 86 of them were double mutants , indicating that wild type ( WT ) copy of At5g45550 complemented the phenotype ( Fig 1G ) . We also identified a T-DNA insertion allele of ncp1 ( GK_719G04 ) from the NASC stock center , and named it ncp1-2 . We generated double mutants ncp1-2 pid and ncp1-2 pid-714 ( SAIL_770_E05 ) . Both of the double mutants displayed the same no-cotyledon phenotype as ncp1-1 pid ( S3 Fig ) . Therefore , we conclude that At5g45550 is NCP1 and the identified mutations in At5g45550 are responsible for the no-cotyledon phenotype in pid backgrounds . We used ncp1-1 allele for further detailed analysis and genetic interaction studies in the paper . NCP1 was identified in the pid mutant background . We segregated out pid and investigated whether ncp1-1 mutation alone caused any developmental defects . At seedling stage , ncp1-1 had shorter root meristems zones when compared to WT . The root phenotypes of ncp1-1 were caused by decreased cell numbers in its root meristem ( S4E and S4F Fig ) . Compared to WT plants , ncp1-1 single mutant plants were slightly taller with shorter siliques and smaller flowers ( S4A–S4C Fig ) . The mutant was much less fertile . Our observed phenotypes of roots , siliques and flowers were consistent with previous findings from the analyses of the T-DNA allele of AtMOB1A , GK_719G04 ( ncp1-2 ) [15] . The shorter root phenotype and the decrease in cell numbers in the root meristems of ncp1 and ncp1 pid may be caused by defects in cell division . To test this hypothesis , we investigated cell division activities in ncp1 and ncp1 pid mutants . CycB1;1:GUS is a widely used marker for the G2/M phase of the cell cycle [34] . The GUS staining domains were dramatically decreased in both ncp1 and ncp1 pid mutants , indicating that cell division activities were decreased in these mutants . These findings could partially account for the observed short root phenotypes of ncp1 and ncp1 pid ( S4I and S4J Fig ) . Because the no-cotyledon phenotype of ncp1 pid was caused by defects occurred during embryogenesis , we also analyzed whether disruption of AtMOB1A alone is sufficient to affect embryogenesis . Another indication that AtMOB1A is important for embryogenesis is that about 66 . 6% of the embryos ( n = 785 ) were aborted in the ncp1-1 siliques ( S4D Fig ) . We carefully analyzed various stages of embryogenesis of the ncp1-1 mutants and discovered that AtMOB1A plays an important role in early embryogenesis . The cell division in some mutant embryos was disturbed as early as 8-cell embryo stage . The upmost suspensor cell divided longitudinally in some ncp1-1 embryos whereas the cell in WT divides horizontally ( Fig 2C ) . At 16-cell stage , both the pro-embryos and suspensor cells were abnormal in ncp1-1 ( Fig 2D ) . At globular stage , the upmost suspensor cell became the hypophysis and remained as a lens-shape cell in WT [35] . But in ncp1-1 mutant , it was no longer lens-shaped and was divided into 2 cells ( Fig 2E ) . The observed defects in the embryogenesis of ncp1-1 mutant would severely affect its embryo development , indicating that NCP1 is important for embryogenesis . The predicted NCP1 protein contains 215 amino acid residues . It shares high sequence homology ( 63% identity ) to the Drosophila MOB1 ( Mats ) ( S5 Fig ) . MOB1 was first identified in yeast as Mps One Binder 1 , an essential protein required for the completion of mitosis and maintenance of ploidy [36] . It has been shown that MOB1 is a key component of the Hippo signaling pathway [11] . In the Arabidopsis genome , there are four MOB1-like genes , At5g45550 , At4g19045 , At5g20430 , and At5g20440 . They have been renamed as AtMOB1A , AtMOB1B [15] , AtMOB1C , and AtMOB1D herein , respectively . MOB1 is highly conserved in plant species . For example , the MOB1As of Brassica rapa and Arabis alpina are almost identical to AtMOB1A: they differ from AtMOB1A in only one and three amino acid residues out of 215 , respectively . Other putative plant MOB1A proteins and AtMOB1A share more than 90% identities ( S5 Fig ) . It is clear from our phylogenetic analysis that animal MOB1 proteins and plant MOB1s belong to different clades ( S6 Fig ) . The MOB1 genes have duplicated in the most recent common ancestor of land plants ( Embryophyte ) during evolution , and evolved with frequent duplication or deletion in the derived lineages of land plants . Selaginella moellendorffii only has one MOB1 gene whereas Physcomitrella patens has two . Monocots and dicots often have two to four copies of MOB1 genes ( S6 Fig ) . To further demonstrate that NCP1 is functionally related to MOB1 homologs from other organisms , we put the Drosophila MOB1 ( Mats ) under the control of the NCP1 promoter and transformed the construct into ncp1-1 . We confirmed that all of the 88 transgenic ncp1-1 seedlings contained the Mats gene . The adult plants of ncp1-1 mutant showed severe defects in fertility , but the Mats transgenic ncp1-1 mutant plants were able to produce siliques like WT ( Fig 3 ) . Our results indicated that the Drosophila Mats gene complemented the defects caused by ncp1-1 mutation , and the function of MOB1/Mats is conserved from plants to Drosophila . To investigate the expression pattern of NCP1/AtMOB1A , we generated a construct containing the NCP1 genomic DNA including its regulatory and coding sequences , with the GFP gene inserted immediately before the stop codon . We transformed ncp1 and ncp1 pid+/- mutants with this construct and found that the construct complemented both ncp1 and ncp1 pid , indicating that the NCP1-GFP fusion protein was fully functional . AtMOB1A is uniformly expressed in embryonic and suspensor cells from one-cell to mature embryo stages . The expression patterns of AtMOB1A are consistent with its role in embryo development . AtMOB1A protein is localized to nucleus , cytoplasm and associated to plasma membrane ( Fig 4 ) . The observed nuclear localization was consistent with previously findings [16 , 37] . The ncp1-1 mutant was isolated as an enhancer of pid , which is a well-known auxin mutant . We further analyzed whether ncp1 could genetically interact with other known auxin mutants . We tested three groups of auxin mutants that are defective in either auxin biosynthesis , or transport , or signaling . It has been shown that YUC flavin-containing monooxygenases and TAA1/TAR tryptophan amino transferases define a main auxin biosynthetic pathway in Arabidopsis [19 , 20] . Both YUCs and TAAs play essential roles in all of the major developmental processes including embryogenesis and flower development in Arabidopsis [18 , 22 , 23] . When we disrupted NCP1 in yuc1 yuc4 background , the resulting triple mutants developed pin-like inflorescence whereas yuc1 yuc4 never form pins , demonstrating that ncp1 greatly enhanced the phenotypes of auxin biosynthetic mutants ( Fig 5A and 5C and 5D ) . We previously reported that NPY1 is involved in auxin-mediated organogenesis . The pid npy1 double mutants had no cotyledons , and npy1 yuc1 yuc4 triple mutants developed pin-like inflorescences . NPY1 is proposed to play a role in auxin transport and signaling [29 , 30] . When we introduced ncp1 into npy1 background , the double mutants produced pin-like structures whereas either single mutant did not form any pins ( Fig 5B and 5C and 5D ) . We next tested if ncp1 could synergistically interact with auxin signaling mutants . TIR1/AFBs are the best characterized auxin receptors responsible for regulating expression of auxin inducible genes . We crossed ncp1 to tir1-1 afb2-1 afb3-1 [27] and obtained various combinations of ncp1 and tir1 afb mutants from the F2 populations . The phenotypic analysis was performed in F4 generation . The single mutants of tir1-1 , afb2-1 , afb3-1 , and combinations of their double mutants did not display dramatic developmental defects under normal growth conditions [27] . Interestingly , the ncp1 tir1-1 double mutants showed severe reduction in fertility , which was caused mainly by the defects in gynoecium patterning . The defects were further enhanced in ncp1 tir1-1 afb2-1 afb3-1 and led to complete sterility ( Fig 6A and 6B ) . The adult plants of tir1-1 afb2-1 double mutants showed a reduction in rosette leaf size and inflorescence height , but their seedlings were similar to WT ( Fig 6C ) [27] . However , the ncp1 tir1-1 afb2-1 triple mutants exhibited strong developmental defects . Six of 34 ( 18% ) triple homozygous seedlings of ncp1 tir1-1 afb2-1 mutants had no roots , whereas the tir1-1 afb2-1 double mutants never displayed such phenotypes . The observed no-root phenotypes closely resembled those of bdl/iaa12 or mp/arf5 mutants . The tir1-1 afb2-1 afb3-1 and tir1-1 afb1-1 afb2-1 afb3-1 mutants also showed the mp-like rootless seedling phenotypes at frequency of 36% and 49% , respectively ( Fig 6C ) [27] . Our results indicated that ncp1 genetically interacts with auxin signaling pathway . In Arabidopsis , PID has three close homologs: WAG1 , WAG2 , and PID2 , which redundantly control cotyledon development [30] . The ncp1 pid double mutants phenocopied the pid wag1 wag2 pid2 quadruple mutants ( S7A and S7B Fig ) . We further tested if ncp1 could enhance pid wag1 wag2 pid2 phenotypes . The double or higher orders of mutant combinations between ncp1 and wag1 wag2 pid2 did not show obvious phenotypic enhancement , suggesting that PID played a more predominant role in regulating cotyledon development than WAG1 , WAG2 , and PID2 . The ncp1 pid wag1 wag2 pid2 quintuple mutants displayed no-cotyledon phenotype similar to that of pid wag1 wag2 pid2 . However , the quintuple mutants showed strong developmental defects in true leaves ( S7 Fig ) . In dark grown seedlings , the initiation of true leaves was delayed in the quintuple mutants , compared to ncp1 pid ( S7C Fig ) . In 14-day-old light grown seedlings , the quintuple mutants developed single or two leaves , and occasionally developed a pin-like true leaf ( S7D and S7E Fig ) . In 36-day-old plants , the quintuple mutants showed two types of phenotypes . The type I plants ( 44% , n = 61 ) developed one to three true leaves and a pin-like inflorescence , and were arrested at this developmental stage . The type II plants ( 56% , n = 61 ) could produce more than three true leaves , and continued to grow with the phenotypes similar to those of ncp1 pid ( S7F Fig ) . We showed that NCP1 genetically interacted with PID to control cotyledon development in Arabidopsis . In animals , MOB1 physically interacts with and activates NDR/LATS through recruitment to the plasma membrane [38 , 39] . Because both PID and NDR/LATS are AGC kinases , we hypothesized that AtMOB1A may use a mechanism analogous to that of animal MOB1 . PID may play a role equivalent to that of NDR/LATS . To test this hypothesis , we conducted both pull-down and Co-IP assays to determine whether AtMOB1A physically interacts with PID/WAGs . However , we did not detect direct physical interactions between NCP1/AtMOB1A and PID , or WAG1/2 in our experiments ( S8 Fig ) . These results suggested that there may not be direct interactions between NCP1 and PID/WAGs , or the interactions are transient and difficult to be detected under our assay conditions . There are at least 39 AGCs in Arabidopsis . The observed genetic synergism of AtMOB1A with PID may suggest that AtMOB1A is necessary for the function of other AGCs that have overlapping functions with PID/WAG1/WAG2 . To assess the role of NCP1 in auxin response , we introduced the auxin reporter DR5-GFP into ncp1 , pid , and ncp1 pid mutants background . At heart and torpedo stages of embryogenesis , strong DR5-GFP signals were observed at the cotyledon primordia and hypophysis in WT . In pid , the DR5-GFP signals remained similar to WT . In contrast , the GFP signals were significantly decreased at the cotyledon primordia in ncp1 single and ncp1 pid double mutants . It is worth noting that the auxin responses at hypophysis seemed not changed in ncp1 single and ncp1 pid double mutants ( S9A Fig ) . These observations suggested that NCP1 might be involved in auxin signaling . It is known that auxin is required for the initiation and growth of lateral root ( LR ) and root hairs , and exogenous auxin can stimulate these developmental processes [40] . To further investigate the roles of NCP1 in auxin responses , we examined the response of ncp1 mutant to exogenous auxin treatment . Four-day-old seedlings of WT , pid , ncp1 , and ncp1 pid germinated on 1/2 strength of Murashige and Skoog medium ( MS ) plates were transferred and grew on 1/2 MS plates containing 50 nM 2 , 4-D , a synthetic auxin . It is obvious that the lengths of root hairs and density of LR/LR primordium were dramatically increased in WT , pid and ncp1 , however the effects of exogenous auxin on ncp1 pid were much weaker ( S9B–S9F Fig ) . This suggested that ncp1 pid double mutants are partially resistant to auxin in terms of root hair and LR initiation and growth . The pericycle cells in ncp1 and ncp1 pid were similar to WT , suggesting that the LR defects in ncp1 pid was likely due to slow LR primordium growth and the failure to emerge from the epidermis of the primary root . It could also be a defect in pre-branch site formation , which is not morphologically distinct . ARF7 and ARF19 redundantly control LR development , and they are expressed in lateral and/or primary roots [41 , 42] . We analyzed the expression of ARF7 and ARF19 in seedlings of ncp1 pid mutants by using ProARF7:GUS and ProARF19:GUS reporter lines [41 , 42] . The expression levels of ProARF7:GUS and ProARF19:GUS were dramatically decreased in the LR primordium of ncp1 , pid , and ncp1 pid mutants , compared to WT . The expression levels of ProARF19:GUS were also reduced in primary roots of ncp1 , pid , and ncp1 pid mutants ( S10 Fig ) . These findings suggested that the LR defects in ncp1 pid were partially caused by down-regulation of ARF7 and ARF19 . PIN1 plays an important role during embryogenesis [28] . It is reported that PIN1-GFP is asymmetrically localized on plasma membrane [43 , 44] . We introduced the PIN1-GFP marker into ncp1 , pid , and ncp1 pid mutants , and carefully checked the subcellular localization of PIN1-GFP from transition to torpedo stage of embryogenesis . No obvious alteration of the subcellular localization of PIN1-GFP was observed in these stages . However , we found that the expression levels of PIN1-GFP were altered in ncp1 mutants compared to WT . At these stages , PIN1-GFP was mainly expressed at the cotyledon primordia and ground tissue , which formed a Y-shape pattern . At transition stage , the expression pattern of PIN1-GFP in ncp1 mutants was similar to that of WT . However , in ncp1 pid double mutants , PIN1-GFP was found to be mainly expressed at the epidermal cell layer of apical part of embryos and ground tissue , which were barely connected by weak PIN1-GFP-expressing cells ( S11 Fig ) . This result suggested that NCP1 plays a role in controlling the expression pattern of PIN1 . It was previously reported that the localization pattern of PIN1 appeared normal in roots of Mob1A RNAi seedlings [15] . The discrepancy between our findings and those of the previous study might be because of the tissue specificity .
The Hippo signaling pathway has been shown to play a critical role in organ size control and morphogenesis in animals , but it is still an open question whether the Hippo pathway exists in plants . Because MOB1 proteins share high sequence homology in animals and plants , it is tempting to hypothesize that the Hippo pathway may also exist and play a role in plant growth and development . Here we show that AtMOB1A is functionally conserved with the Drosophila protein because atmob1a was fully rescued by its Drosophila counterpart , suggesting that at least part of the Hippo pathway is functional in plants . NCP1/AtMOB1A synergistically interacts with key genes in auxin biosynthesis , transport , and signal transduction pathways to regulate Arabidopsis development . The observed synergistic genetic interactions and the decreased auxin responses in various ncp1 and auxin mutant combinations suggest that there is an intrinsic link between auxin pathway and the hypothesized Hippo pathway in plants . Our finding that the expression levels of ProARF7:GUS and ProARF19:GUS were dramatically decreased in ncp1 pid further supports the notion that AtMOB1A is important for auxin-mediated developmental processes . This work provides a genetic framework for the Hippo pathway in auxin-mediated plant development . It was reported that about 2% of the progeny of AtMob1A RNAi silenced plants were tetraploid [16] , which is a result of cell division defects . Auxin is also known to control plant development by regulating cell division and expansion . Therefore , AtMOB1A may be involved in auxin-controlled cell division . The mutants in animal Hippo pathway display defects in organ overgrowth [1] , due to a loss of control of cell proliferation . In ncp1 mutant , the length and the cell number of root meristem were decreased compared to WT ( S4 Fig ) . The different developmental outcomes between animals and plant mob1 mutants suggest that Hippo pathway/MOB1 protein may play different roles in plants and animals regarding cell proliferation . Recently , the Hippo pathway has been shown to control cell fate in animals . For example , the Hippo pathway activity is essential for the maintenance of the differentiated hepatocyte state . Acute inactivation of the Hippo signaling in vivo is sufficient to dedifferentiate adult hepatocytes into cells bearing progenitor characteristics [4] . In Arabidopsis , ncp1 yuc yuc4 and ncp1 npy1 mutants failed to develop flowers ( Fig 5 ) . The cotyledons were also eliminated in ncp1 pid , and the hypophysis was lost in ncp1 tir1-1 afb2-1 during embryogenesis ( Fig 2 and Fig 6 ) . The observed defects in organ and embryo development in these mutants indicated that the Hippo pathway also plays a critical role in determining cell fate in plants . It has been shown that the Hippo pathway is highly conserved in mammals and insects . A human MOB1 gene rescued the developmental defects of the Drosophila MOB1 mutant mats [11] . We show that the Drosophila Mats fully rescued developmental defects of the Arabidopsis ncp1 mutant ( Fig 3 ) , indicating that at least some of the components of the Hippo pathway are conserved between plants and animals . This functional conservation of MOB1 proteins is consistent with the high similarities of their amino acid sequences ( S5 Fig ) . It has been shown that MOB1 is a phospho-protein in animal systems . Phosphorylation of Thr12 and Thr35 of hMOB1 by MST1 or MST2 is required for the interaction of hMOB1 with NDR/LATS kinases in human [45 , 46] . Thr12 and Thr35 are absolutely conserved in MOB1s of plants and animals ( S5 Fig ) . Both AtMOB1A and AtMOB1B were identified as phospho-proteins in a proteomic study [47] , suggesting AtMOB1A/B is also phosphorylated by some kinase ( s ) . AtMOB1A may also interact with Arabidopsis NDR/LATS kinases . In line with this hypothesis , there are eight NDR-like kinase genes in Arabidopsis [48] , and they share high similarities with their human counterparts ( S12 Fig ) . It is well known that auxin promotes root hair and LR formation [40] . Gain-of-function mutant msg2 of Aux/IAA19 had severely reduced LR and LR formation was not normally induced by exogenous auxin [49] . Root hair and LR formation are also inhibited in arf7 arf19 double mutants [41 , 42] . pid did not show obvious defects in root development [31] . However , NCP1 and PID synergistically control LR formation and root hair growth in seedlings ( S9 Fig ) . ncp1 pid also displayed strong defects in LR development in response to exogenous auxin treatment ( S9 Fig ) . Expression levels of ProARF7:GUS and ProARF19:GUS were decreased in ncp1 pid . Moreover , ncp1 enhanced tir1-1 afb2-1 mutants’ phenotypes ( Fig 6 ) . These findings suggested that NCP1/AtMOB1A plays a positive role in promoting auxin signaling . In the Hippo pathway , MOB1 binds and activates the AGC kinase NDR/LATS1/2 [38 , 39] . In Arabidopsis , there are 39 AGC kinases [48] . Some of them have been demonstrated to be involved to auxin pathways , such as PID/WAGs and D6PKs [31 , 50 , 51] , which phosphorylate PIN1 at different phosphosites with different preference [52] . d6pk0123 quadruple mutants showed somewhat pin-like axillary shoots [51] . pid wag1 wag2 mutants phenocopied ncp1 pid [30] . Because NCP1/AtMOB1A is functionally conserved MOB1 in Arabidopsis , it is possible that PID/WAGs/D6PKs function as a plant counterpart of LATS1/2 . It would be interesting to test if PID/WAGs/D6PKs can rescue Drosophila lats mutant phenotypes . AtMOB1A may associate with PID/WAGs/D6PKs and regulates its kinase activity , which subsequently modifies activities of PIN1 . In human and Drosophila , MOB1 can activate LATS/NDRs when targeted to the plasma membrane [39 , 53] . AtMOB1A is localized to nucleus [16 , 37] and also associated with plasma membrane ( Fig 4 ) . PID , WAGs and D6PKs are also associated with plasma membrane [54 , 55] , making it possible for AtMOB1A to activate PID/WAGs/D6PKs . However , we did not detect direct physical interactions between AtMOB1A and PID/WAGs by using pull-down and Co-IP assays . The negative results do not rule out the possibility that AtMOB1A is in a complex with AGC kinases . On the other hand , ncp1 pid wag1 wag2 pid2 showed no-cotyledon phenotypes similar to those of pid wag1 wag2 pid2 . But the quintuple mutants displayed enhanced developmental defects in true leaves . These findings support the hypothesis that AtMOB1A may function with PID/WAGs . Alternatively , AtMOB1A and PID/WAGs/D6PKs may regulate transcription levels of auxin related genes . Indeed , we observed the alteration of expression pattern of PIN1-GFP and down-regulation of ARF7:GUS and ARF19:GUS in ncp1 pid double mutants ( S10 Fig and S11 Fig ) . This finding is consistent with the mechanism that the animal Hippo pathway functions through regulating expression of downstream genes via a common growth regulatory effector , the transcriptional co-activator YAP/TAZ [1] . Another possibility is that the Hippo pathway functions in parallel to auxin pathway , yet they crosstalk to control plant development . This would be similar to the crosstalk between Wnt/β-catenin pathway and Hippo pathway to regulate animal development and tumorigenesis . It has been shown that cytoplasmic TAZ of the Hippo pathway can bind to DVL of the Wnt/β-catenin pathway and negatively regulate the Wnt/β-catenin pathway [56] . In conclusion , we demonstrate that AtMOB1A , a key component of the Hippo pathway , plays critical roles in auxin-mediated development in Arabidopsis . AtMOB1A synergistically interacts with auxin biosynthesis , transport , and signaling pathways to regulate Arabidopsis development . MOB1 is a regulator of AGC kinases in animal systems . PID/WAGs , D6PKs are AGC kinases , suggesting that NCP1/AtMOB1A may also regulate kinase activities of PID/WAGs and D6PKs , and possibly other AGC kinases in Arabidopsis . The fact that auxin responses and expression of auxin related genes such as ARF7 and ARF19 were down-regulated in ncp1 pid mutants suggests that NCP1/AtMOB1A may promote auxin signaling . This provides another layer of regulation of plant development by auxin . Further identification of other components of the Hippo pathway in Arabidopsis will help elucidate the mechanisms .
Plants were grown under 16-h light/8-h dark cycle at 22℃ . The T-DNA insertion lines were obtained from NASC . The mutants used in this work were: pid ( SALK_049736 ) , pid-714 ( SAIL_770_E05 ) , ncp1-2 ( GK_719G04 ) . T-DNA insertion sites were determined by sequencing . Genotyping primers for pid ( SALK_049736 ) and pid-714 ( SAIL_770_E05 ) are: 5’-CCTCAGATTTCGCTTACGCAG-3’ , and 5’- GCGAGACGAGTGAATCGTCG-3’ , combined with JMLB1 and SAIL-LB1 , respectively . For genotyping ncp1-2 ( GK_719G04 ) , 5’-ATGGATTCGTGTGGCTTTC-3’ , 5’-TGTTTACAGCAAGCCATTC-3’ , and PGABI1: 5’-ATATTGACCATCATACTCATTGC-3’ were used . To genotype ncp1-1 , 5’-TGACCGTCTTCTTCCTAT-3’ and 5’-TGTTTACAGCAAGCCATTC-3’ were used and the PCR products were digested with MseI . npy1-2 , yuc1 , yuc4 , tir1-1 , afb2-1 , afb3-1 were previously described [22 , 27 , 29] . All T-DNA insertion lines were genotyped as described previously [57–59] . For complementation of ncp1 pid mutants , a genomic DNA fragment containing the coding region as well as up- and down-stream regulatory sequences of At5g45550 was amplified by PCR using the following primers: 5’-CCCCCCGGGGAAACGGTGACCAAAATGCT-3’ and 5’-GCTCTAGAAGACGAGGCTCCAACACG-3’ . The PCR product was digested with BamHI and XbaI and subcloned into pPZP211 vector [60] to generate pPZP211-NCP1gDNA . The plasmid was transformed into ncp1 pid+/- mutants via Agrobacterium strain GV3101 using floral dipping method [61] . The transgenic seedlings were selected on 1/2 MS plates containing 50 μg/mL kanamycin . For expression of the Drosophila Mats under the control of NCP1 promoter , the Mats cDNA was amplified with PCR using the primers: 5’-ACTCCCGGGATGGACTTCTTGTTCGGTTC-3’ , and 5’- GCTCTAGACTATATCTGCCGCTCATCCT-3’ . The NCP1 promoter was amplified with primers: 5’-ACTGTCGACCTGCCCAATCAGCAAGAA-3’ and 5’-ACTCCCGGGGGCGACAAAAAGCAAGCGAG-3’ . The PCR products were digested with SalI , XmaI and XbaI and subcloned into pCambia-1300 to generate pCambia-1300-NCP1p:Mats . For expression pattern and subcellular analysis of NCP1 , the pPZP211-NCP1gDNA construct was modified . The GFP gene was inserted immediate before the stop codon of NCP1 gene with restriction site of ApaI . SEM samples were prepared as described previously [62] , and analyzed using a HITACHI S-4800 FESEM microscope . For whole-mount analysis of vascular structures and embryos , samples were prepared as previously described [63] , and photographed under differential interference contrast ( DIC ) field or dark field on Leica DM 4500 and Leica S8AP0 microscopes . DR5-GFP and PIN1-GFP signals in embryos were viewed on Olympus FV1000MPE following the manufacturer’s instructions . Sequences were aligned using Clustal X version 1 . 81 [64] , then refined manually . Maximum Likelihood method was used to reconstruct the phylogenetic tree using Mega5 [65] . Topological robustness of the phylogenetic tree was assessed by bootstrapping with 1000 replicates [66] . For the pull-down assay , cDNA of PID and NCP1 was cloned into pGEX-4T-1 and pET30a vectors to generate the expression constructs . The His-tagged and GST-tagged proteins were expressed in E . coli strain BL21 . The subsequent protein purification and pull-down assay with Glutathione Sepharose 4B ( GE ) or His beads ( Bio-Rad Ni-NTA Agarose ) were carried out following the manufacturers’ manuals . The bound proteins were eluted and analyzed with anti-GST and anti-HIS antibodies ( CWBIO ) . To perform Co-IP assay of NCP1 and PID/WAGs , we constructed pEarleyGate104-35S:YFP-NCP1 , pSuper1300:PID-Myc , pSuper1300:WAG1-Myc , pSuper1300:WAG2-Myc . YFP-NCP1 and PID-Myc or WAGs-Myc constructs were transformed into tobacco ( Nicotiana Benthamiana ) by injection . Leaves were grounded into fine powder in liquid nitrogen . Proteins were extracted with the extraction buffer [100 mM HEPES ( pH 7 . 5 ) , 5 mM EDTA , 5 mM EGTA , 10 mM NaF , 5% Glycerol , 10 mM Na3VO4 , 10 mM DTT , 1 mM PMSF , 0 . 1% Triton X-100 , 10 μg/mL Aprotinin , 10 μg/mL Leupeptin , 10 μg/mL Antipain] . The protein extracts were spun twice for 30 min at 14 , 000 g at 4℃ . The supernatant was incubated for 3 hr with anti-Myc-tag mAb-agarose ( MBL ) in IP buffer [20 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1 mM Na3VO4 , 1 mM NaF , 10 mM glycerophosphate , 0 . 1% Triton X-100 , 1 μg/mL Aprotinin , 1 μg/mL Leupeptin , 1 μg/mL Antipain] . The agarose was washed for three times with 1 ml of PBS . Proteins were then released and collected by boiling in 2×SDS loading buffer for 5 min . IP products were detected by SDS-PAGE and immunoblot analysis using anti-Myc or anti-GFP antibodies ( CWBIO ) . These experiments were repeated at least three times . | MOB1 protein is a key component of the Hippo signaling pathway in animals , and it plays critical roles in organ size control . The plant hormone auxin regulates many aspects of plant growth and development including organogenesis . In this work , we showed that AtMOB1A , which is highly homologous to animal MOB1 proteins , plays an important role in plant organogenesis . Furthermore , we demonstrated that AtMOB1A synergistically interacts with auxin biosynthesis , transport , and signaling pathways to regulate Arabidopsis development . We further showed that AtMOB1A likely controls plant development by promoting auxin signaling . This work identified a new player in auxin-mediated plant development and lays a foundation for further dissection of the mechanisms by which auxin regulates organogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"invertebrates",
"plant",
"anatomy",
"plant",
"growth",
"and",
"development",
"plant",
"embryo",
"anatomy",
"animals",
"hormones",
"animal",
"signaling",
"and",
"communication",
"organisms",
"animal",
"models",
"developmental",
"biology",
"plant",
"science",
"model",
"organisms",
"drosophila",
"melanogaster",
"animal",
"behavior",
"plant",
"hormones",
"seedlings",
"embryos",
"plants",
"zoology",
"drosophila",
"research",
"and",
"analysis",
"methods",
"embryology",
"plant",
"embryogenesis",
"plant",
"development",
"behavior",
"insects",
"arthropoda",
"biochemistry",
"plant",
"biochemistry",
"phenotypes",
"embryogenesis",
"genetics",
"biology",
"and",
"life",
"sciences",
"auxins",
"cotyledons",
"(botany)",
"fruit",
"and",
"seed",
"anatomy"
] | 2016 | NCP1/AtMOB1A Plays Key Roles in Auxin-Mediated Arabidopsis Development |
Homologous recombination is an important mechanism for the repair of DNA damage in mitotically dividing cells . Mitotic crossovers between homologues with heterozygous alleles can produce two homozygous daughter cells ( loss of heterozygosity ) , whereas crossovers between repeated genes on non-homologous chromosomes can result in translocations . Using a genetic system that allows selection of daughter cells that contain the reciprocal products of mitotic crossing over , we mapped crossovers and gene conversion events at a resolution of about 4 kb in a 120-kb region of chromosome V of Saccharomyces cerevisiae . The gene conversion tracts associated with mitotic crossovers are much longer ( averaging about 12 kb ) than the conversion tracts associated with meiotic recombination and are non-randomly distributed along the chromosome . In addition , about 40% of the conversion events have patterns of marker segregation that are most simply explained as reflecting the repair of a chromosome that was broken in G1 of the cell cycle .
Although mitotic recombination between homologous chromosomes was first described in 1936 [1] , our understanding of the mechanism of spontaneous mitotic recombination is still limited for two related reasons . First , spontaneous mitotic recombination events are very infrequent compared to meiotic exchanges . In S . cerevisiae , mitotic crossovers and conversions are about 104 to 105-fold less frequent than meiotic events [2] , [3] and usually require a selective system for their detection . Second , these systems , in general , do not allow selection of both daughter cells that contain the recombinant chromosomes generated in the mother cell . Reciprocal crossovers ( RCOs ) between homologous chromosomes that have a heterozygous marker can lead to daughter cells that are homozygous for the marker ( loss of heterozygosity , LOH ) . One selective system in S . cerevisiae to detect such events uses the heterozygous drug-resistance marker can1 ( Figure 1A ) . Since diploids heterozygous for this marker are sensitive to the arginine analogue canavanine , a derivative that is homozygous for the mutant allele arising from crossing over can be selected on medium containing canavanine . The daughter cell homozygous for the wild-type CAN1 allele , however , cannot be selected . A canavanine-resistant diploid can also be derived from a heterozygous diploid by break-induced DNA replication ( BIR ) [4] . As shown in Figure 1B , a double-strand DNA break ( DSB ) on the CAN1-containing chromosome is repaired by copying the DNA from the can1-containing chromosome . Since the only selectable daughter cell in this system is identical for both RCO and BIR , these two mechanisms cannot be distinguished by this system . Two recent studies have examined the relative contributions of RCO and BIR to LOH in yeast . Using a non-selective approach , McMurray and Gottschling [5] showed that most LOH events in “young” cells ( cells that have not undergone many mitotic divisions ) represent RCOs , whereas LOH events in “old” cells often involve BIR . Using a selective approach that will be described further below , we found that most spontaneous LOH events are RCOs and recombination events induced by hydroxyurea are both RCO and BIR [6] . In mitosis , as in meiosis , gene conversion events are observed and these events are often associated with crossovers [3] . Conversion events are the non-reciprocal transfer of information between homologous DNA sequences and , in meiosis , most conversions reflect heteroduplex formation , followed by mismatch repair [7] . Most studies of mitotic conversion employ strains that are heteroallelic for an auxotrophic marker and heterozygous for a centromere-distal marker ( Figure 2 ) . Although a reciprocal crossover between the heteroalleles could produce a prototroph , Roman [8] showed that most prototrophs were a consequence of a gene conversion event . It should be noted that use of heteroalleles for the detection of gene conversion is rather restrictive . If gene conversion is a consequence of heteroduplex formation followed by mismatch repair , in order to obtain a wild-type allele by conversion , the heteroduplex must include only one of the two alleles or the repair of the heteroduplex containing both alleles must be “patchy” . As described below , we found that the mitotic conversion tracts associated with RCO in our system are usually very long and continuous . In numerous studies of the type diagrammed in Figure 2 , heteroallelic gene conversion is associated with LOH of a centromere-distal heterozygous marker . The degree of association varies between about 10% and 50% [2] . Based on the expected patterns of segregation following an RCO , one would expect that only half of the RCOs would be detectable by producing cells that have undergone LOH ( Figures 1 and 2 ) . Chua and Jinks-Robertson [9] showed that this expectation is met for S . cerevisiae , although in Drosophila , the crossover chromatids usually segregate into different daughter cells [10] . Stern [1] argued that mitotic crossovers occur in G2 ( as shown in Figures 1 and 2 ) because a mitotic crossover between unreplicated chromosomes would not result in LOH for heterozygous markers ( assuming that the chromosomes undergo an equational division ) . In S . cerevisiae , however , two studies demonstrated that mitotic gene conversion could be induced in G1 cells by ultraviolet light or gamma rays [11] , [12] . From his analysis of crossovers associated with heteroallelic gene conversion events , Esposito [13] suggested that spontaneous mitotic exchanges also occur in G1 . He argued that Holliday junction intermediates formed in G1 were replicated rather than resolved by junction-cleaving enzymes , generating G2-like crossovers . In the analysis described below , we present evidence that at least 40% of spontaneous RCOs are initiated in G1 .
We previously described a genetic system ( Figure 3 ) allowing for the selection of both daughter cells containing the reciprocal products of mitotic crossovers in the 120 kb CEN5-CAN1 interval on chromosome V [6] . One homologue has the can1-100 allele , an ochre mutation . On the other homologue , the CAN1 gene has been replaced with SUP4-o , a gene encoding an ochre-suppressing tRNA [6] . In addition , the diploid is homozygous for ade2-1 , also an ochre mutation . In the absence of a suppressor , strains with the ade2-1 mutation require adenine , form red colonies because of the accumulation of a red precursor to adenine , and are canavanine-resistant . The starting diploid strain is canavanine-sensitive ( CanS ) and forms white colonies . If an RCO occurs between CEN5 and the can1-100/SUP4-o markers as the cells are plated on canavanine , a red/white sectored CanR colony will be formed . In our first use of this system , the two homologues were derived from isogenic haploids , resulting in a diploid that had no polymorphisms . In the current study , using standard methods [14] , we constructed a diploid by crossing the haploid strains W303A and YJM789 . These strains have about 0 . 5% sequence divergence and , therefore , about 60 , 000 single-nucleotide differences [15]; S288c and W303A are closely related in sequence [16] . By comparisons of the genomic sequences , we identified 34 polymorphisms between W303A and YJM789 in the CEN5-CAN1 interval and used those polymorphisms to map crossovers and associated gene conversion tracts as described below . The diploids derived from crossing W303a- and YJM789-derived strains were PSL100 and PSL101 . These strains are identical except one strain ( PSL100 ) is homozygous for the ura3 mutation and the other ( PSL101 ) is heterozygous ura3/URA3; these strains yielded very similar results . Each red/white sectored CanR colony reflects an independent RCO ( Figure 3 ) . We isolated genomic DNA from cells purified from the red and white sectors and analyzed the segregation of the polymorphisms by PCR followed by restriction enzyme treatment ( details in Materials and Methods ) . For example , one polymorphism distinguishing W303A and YJM789 is located at SGD coordinate 60 , 163 on chromosome V . A Hpy188III site that is present at this position in the W303A genome is absent in the YJM789 genome . We designed primers flanking this site ( Table S2 ) that result in a PCR product of about 520 bp . Thus , if we amplify genomic DNA from a diploid that is homozygous for the W303A form of the polymorphism , treat the amplified product with Hyp188III , and analyze the products by agarose gel electrophoresis , we observe two fragments of about 250 and 270 bp . A strain homozygous for the YJM789 form of the polymorphism produces a single fragment of 520 bp , and a heterozygous diploid produces three fragments of 250 , 270 , and 520 bp . The patterns of marker segregation that were expected are shown in Figure 4 . For a RCO unassociated with gene conversion ( Figure 4A ) , we expect that markers centromere-proximal to the exchange will be heterozygous in both the red and white sectors . Centromere-distal to the exchange the sectors should be homozygous , the red sector homozygous for the W303A markers and the white sector homozygous for the YJM789 markers . If there is a conversion associated with the RCO ( Figure 4B ) , there will also be a region in which a marker is heterozygous in one sector but homozygous in the other . Such a segregation pattern is analogous to a 3∶1 meiotic segregation event . Before mapping the crossovers and associated conversion events , we determined the rate of RCOs . In our previous study with a diploid ( MAB6 ) that was constructed from a cross of two W303A-related haploids and had no polymorphisms between CEN5 and CAN1 [6] , we observed CanR red/white sectors at a rate of 2±0 . 6×10−5/division ( ±95% confidence limits ) ; this analysis was done in cells cultured at 30°C . Since the background growth of CanS cells on the canavanine-containing solid medium in the W303A/YJM789 diploid used in the present study is strong at 30° , we performed all experiments at 22° . At this temperature , the rate of CanR red/white sectors in MAB6 was reduced to 2 . 9±0 . 4×10−6/division . The rate of CanR red/white sectors in PSL101 ( the diploid with the hybrid W303A/YJM789 background ) was 3 . 3±0 . 2×10−6/division , indicating that the numerous sequence polymorphisms do not significantly affect the rate of RCOs . Since only half of the segregation events in cells with an RCO result in loss of heterozygosity [9] , the calculated rate of RCO in PSL101 ( about 7×10−6 ) is twice the rate of sector formation . We also examined the rates of CanR red/white sectors in PG311 and MD457 , MATa/MATαΔ and spo11/spo11 derivatives of PSL101 , respectively . The rates of sectors were 1 . 1±0 . 5×10−6/division ( PG311 ) and 0 . 8±0 . 1×10−6/division ( MD457 ) . Since we previously found no significant effect of heterozygosity at the MAT locus on RCOs [6] and since Spo11p is not expressed in vegetative cells [17] , the significance of the three-fold reduction in the rate of RCOs relative to PSL101 is unclear . As will be described below , the patterns of segregation of polymorphisms in MD457 and PG311 were very similar to those observed in PSL101 . We mapped crossovers and conversions in 74 CanR red/white sectored colonies derived from PSL100 and PSL101 . The locations of the mapped events are shown in Figure 5 . Green Xs indicate crossovers unassociated with gene conversion and the horizontal lines indicate the extent of gene conversion tracts associated with crossovers ( red and black lines indicating markers derived from the W303A- and YJM789-derived homologues , respectively ) . Several generalizations can be made based on our analysis . First , most ( 59 of 74; about 80% ) of the RCOs are associated with adjacent conversion tracts; the conversion tract is adjacent to the crossover in 58 of the 59 tracts . For most conversion events ( exceptions to be discussed below ) , we cannot determine whether the crossover occurred within the tract or at one of the two ends of the tract . Second , most ( 54 of 59 ) of the tracts are exclusively red or exclusively black , indicating that only one homologue was the donor in each conversion event . Third , the red and black conversion tracts are not usually interrupted by markers that do not undergo conversion , demonstrating that regions of DNA from one homologue are usually non-reciprocally transferred as a single entity to the other homologue . Fourth , since the numbers of red and black conversion tracts ( 26 and 28 , respectively ) are approximately equal , the two homologues are equally capable of donating information during a conversion event . Fifth , although about 20% of the crossovers have no detectable conversion tracts , it is likely that most or all of such crossovers are associated with conversion events that could be detected with a denser array of markers . In addition to the “normal” ( 3∶1 ) gene conversion events shown as thin lines in Figure 5 , we detected an unexpected type of conversion . In this class ( which we term “4∶0” conversion ) , the same form of the polymorphism was homozygous in the red and white sectors . These conversion tracts are shown as thick lines in Figure 5 . Of the 59 conversion events observed , 35 were 3∶1 conversions , 7 were 4∶0 conversions , and 17 were hybrid 3∶1 , 4∶0 tracts . The 4∶0 tracts and hybrid tracts are unlikely to reflect two independent events , since the frequency of these tracts is similar to that observed for the 3∶1 tracts . In addition , about 70% of the 4∶0 tracts are contiguous with a 3∶1 tract and , in 15 of the 17 hybrid tracts , the 4∶0 segment of the tract is derived from the same chromosome as the 3∶1 tract ( Figure 5 ) . Our favored interpretation of the 4∶0 conversion events ( outlined in detail in the Discussion ) is that they are a consequence of a double repair event of a chromosome that was broken in G1 and replicated to yield two broken chromatids . There were 48 3∶1 or hybrid conversion tracts that involved sequences donated exclusively from W303A or YJM789 . In Figure 4B , we show the red chromatid ( representing W303A sequences ) donating sequences to the black chromatid during the conversion event . For this type of event , we expect that the red sector ( homozygous for can1-100 ) will be homozygous for the converted marker ( s ) and the white sector ( homozygous for SUP4-o ) will be heterozygous for these marker ( s ) . This expected pattern was observed in 42 of the 48 conversion events with a 3∶1 or hybrid 3∶1/4∶0 tract . In three of the 48 events , the patterns of markers in the sector were in the opposite direction ( defined as the “unexpected” pattern ) and , in three events , the patterns suggested a crossover within the 3∶1 conversion tract . These unusual patterns of marker segregation may reflect repair of a G1-associated DSB and are discussed further in the Supporting Information ( Text S1 , Figures S1 and S2 ) . For both meiotic and induced mitotic gene conversion events , the chromosome with the DNA lesion that initiates the exchange ( for example , a double-strand break ) is the recipient of genetic information [3] . Our data do not address this issue for spontaneous mitotic events . The analysis described above can determine whether the strain is heterozygous or homozygous for markers but does not reveal the coupling of heterozygous markers . Our expectation was that in sectors with heterozygous markers , the original coupling of these markers was maintained , one chromosome containing the W303A-derived markers and the other the YJM789-derived markers . This expectation was checked for the red and white sectors of nine sectored colonies . Strains derived from each sector were sporulated and we analyzed the segregation of multiple heterozygous markers in the four spores . For the heterozygous markers , we found that two of the spores had markers derived from W303A and two had markers from YJM789 , indicating that heterozygous markers usually had the same coupling relationship as in the chromosomes before the mitotic exchange . We classified 47 of the 59 conversion tracts in our study as “simple” using the following criteria: 1 ) the tract is continuous and the converted sequences are derived from only one of the two homologues , 2 ) the crossover is adjacent to the conversion tract , and 3 ) the 3∶1 conversion tract has the expected association ( as defined above ) with the sector . We included 3∶1 , 4∶0 , and hybrid tracts in our analysis . Most of these tracts spanned more than one marker . For each conversion event , we estimated the tract size by averaging the maximum tract size ( the distance between markers that flanked the conversion tract ) and the minimum tract size ( the distance between markers that were included within the tract ) ; for conversion events that included one site , the minimum tract size was taken to be one bp . The tract size averaged for the 47 events was 11 . 7±1 . 6 kb ( 95% confidence limits ) ; the median track size was 7 . 6 kb . We also calculated the average tract lengths separately for 3∶1 events ( 12 . 6±2 . 4 kb ) , 4∶0 events ( 6 . 8±0 . 8 kb ) , and hybrid events ( 11 . 4±1 . 2 kb ) . These tracts are considerably longer than those observed in meiotic cells that average about 1–4 kb [18]–[21] . The sizes of all conversion tracts for PSL100/PSL101 and the other strains used in this study are in tables in the Supporting Information section ( Tables S3 , S4 , S5 , and S6 ) . As discussed above , the mitotic crossovers that had no detectable conversion event are likely to have had a conversion tract that was restricted to the region between the assayed markers . If we assume that these postulated conversion events had tract sizes that were half of the distance between the markers in the interval containing the crossover , then the average mitotic conversion tract for PSL100/PSL101 was 9 . 4 kb rather than 11 . 7 kb , still considerably longer than meiotic conversion tracts estimated in other studies . In summary , our analysis of mitotic crossovers indicated two unusual features of the gene conversion tracts associated with the RCO: the tracts were often very long , and about 40% of the tracts were not consistent with the simplest model of a G2-initiated recombination event . To ensure that the unusual gene conversion events described above were not a consequence of a sub-set of cells that underwent meiotic levels of recombination , followed by mitotic patterns of chromosome disjunction , we examined mitotic recombination in MD457 ( a spo11/spo11 derivative of PSL101 ) and PG311 ( a MATa/MATαΔ derivative of PSL101 ) . These strains are incapable of meiotic recombination . The positions of RCOs and their associated conversion tracts ( 14 from MD457 and 15 from PG311 ) are shown in Figure 6 . The types of conversion events are similar to each other and to those observed in PSL100/PSL101 . The gene conversion tracts were very long in the two strains , and we observed 3∶1 , 4∶0 , and hybrid 3∶1/4∶0 tracts in approximately the same proportions as in PSL101 . The average conversion tract sizes ( average of all three types ) were 26 . 2±5 . 1 kb for MD457 and 12 . 8±2 . 3 kb for PG311; the median track sizes for MD457 and PG311 were 20 . 1 kb and 6 . 1 kb , respectively . The average conversion tract size for MD457 is somewhat misleading because one very large tract ( 103 kb ) had a substantial effect on the average . The average tract size for the other tracts in MD457 was 19 . 2 kb . These results argue that the very long conversion tracts and 4∶0 and 3∶1/4∶0 classes of events observed in PSL101 do not reflect an aberrant type of meiotic recombination . Using methods similar to those used to map mitotic crossovers and conversions , we also examined the patterns of meiotic exchanges in 21 tetrads derived from PS101 . By examining the segregation of the centromere-linked trp1 marker and the can1-100/SUP4-o markers , we identified tetrads that had at least one crossover in the 120 kb CEN5-CAN1 interval . The positions of crossovers and the lengths of associated gene conversion tracts in these tetrads are shown in Figure 6C . Eleven of the conversion tracts were associated with crossovers and three were not . Of the eleven tracts associated with crossovers , eight included only one marker and three included two . None of the conversion sites spanned more than two markers . In striking contrast , of the 47 “simple” conversion events associated with mitotic crossovers in the same strain , as described above , 12 included only one marker , 12 spanned two markers , and 23 involved more than two markers . This difference in the sizes of meiotic and mitotic tracts is very significant ( p value of 0 . 001 by Fisher exact test ) . In addition , using the same methods to estimate conversion tract length that we used for mitotic tracts , we calculated the average meiotic conversion tract length in PSL101 as 4 . 7±0 . 6 kb , significantly ( p<0 . 05 ) less than that observed in mitosis . If we assume that the crossovers with no detectable conversions had tracts that were half of the size of the interval between the markers containing the crossovers , the average conversion tract was 3 . 2 kb . In summary , these results demonstrate that the long mitotic conversion tracts in PSL101 and related strains are not an artifact generated by the high level of polymorphisms in PSL101 and related diploids , but reflect differences in the mechanisms of meiotic and mitotic recombination . As expected from many previous studies [3] , [7] , most of the meiotic conversion events are 3∶1 events ( three spores with one form of the polymorphism , one with the alternative form ) , but one tetrad had a conversion tract with a “4∶0” segment adjacent to a 3∶1 segment , similar to some of the mitotic conversion tracts described previously . Meiotic conversion events with 4∶0 segregation have been seen previously at meiotic recombination hotspots [22] and occur at the frequency expected for two independent conversion events . In 21 tetrads , we observed 26 crossovers; about 40% ( 11 ) were associated with conversion tracts and 60% ( 15 ) were not . This association between meiotic crossovers and conversion is significantly less ( p value≤0 . 001 by Fisher exact test ) than observed for mitotic crossovers and conversion in PSL100/101 where 59 of 74 crossovers were associated with a conversion tract . A simple interpretation of this result is that the longer conversion tracts associated with mitotic crossovers make it more likely that an associated conversion event will be detectable in mitotic cells than in meiotic cells .
Meiotic recombination events in S . cerevisiae are distributed non-randomly . Certain chromosomal domains have low levels of exchange ( for example , near the centromeres and telomeres ) and there are intergenic regions with very elevated rates of recombination ( hotspots ) correlated with high levels of local meiosis-specific double-strand DNA breaks [23] , [24] . Although no high-resolution mitotic recombination maps have been constructed previously , several DNA sequence motifs or conditions have been associated with elevated rates of mitotic recombination in yeast including: elevated rates of transcription , replication fork pausing/stalling , and DNA sequences capable of forming secondary structures such as poly CCG or inverted repeats [25] . Most of the assays of the recombination-stimulating sequences involve recombination between direct or inverted repeats rather than recombination between homologous chromosomes . From the patterns of the spontaneous recombination events shown in Figures 5 and 6 , it is clear that crossovers and conversions are initiated at many sites within the CEN5-CAN1 interval , although there appear to be more conversion tracts near CAN1 than near the centromere . This impression is conveyed more clearly in Figure 7A . In this figure , we show the number of times each marker was involved in a conversion event in the strains PSL100/101 , MD457 , and PG311 . If we divide the region into four intervals of approximately the same size and sum the number of events/marker over all markers in each quadrant , we find 124 ( Quadrant 1 , markers 35 to 55 ) , 112 ( Quadrant 2 , markers 56–87 ) , 99 ( Quadrant 3 , markers 92–117 ) , and 43 ( Quadrant 4 , markers 119–151 ) events in each quandrant , moving from CAN1 to CEN5 . This distribution of events is very significantly different ( p = <0 . 0001 by chi-square test ) from random . In addition , the number of events in Intervals 1 and 4 are significantly greater and less , respectively , than that expected from a random distribution . We confirmed this conclusion using two other types of analysis . First , we determined the number of conversion tracts within each quadrant . Only tracts that did not span two different quadrants were included . We found 28 , 16 , 18 , and 4 tracts within the Quadrants 1–4 , respectively . This distribution was significantly different from random ( p = 0 . 0005 ) . One difficulty in localizing a mitotic recombination hotspot is that the conversion tracts are long and heterogeneous in length . In meiosis , although the initiating DNA lesion stimulates gene conversion tracts bidirectionally , individual gene conversion tracts are propagated unidirectionally from the initiating DNA lesion [26] . In an analysis of HO-induced mitotic gene conversion tracts [27] , about 80% of the tracts were bidirectional from the DSB site , although the length of DNA transferred was often much greater on one side of the DSB site than the other . If we assume that individual spontaneous conversion events are propagated predominately in a single direction from the initiating lesion , one of the endpoints of the conversion tract will be near the initiating DNA lesion . Thus , we determined the number of conversion tracts that ended in each of the 35 intervals defined by the polymorphic markers; we also included in this analysis the crossovers within each interval . When these events were summed within each quadrant , we found 65 , 54 , 38 , and 31 events , respectively , in Intervals 1–4 . This distribution of events is significantly ( p = 0 . 0006 ) different from random . In Figure 7B , we show the number of events ( termini of conversion tracts and crossovers ) within each of the 35 intervals , normalized for the size of the interval . A peak between markers 43 ( SGD coordinates 43078 ) and 44 ( SGD coordinates 44403 ) is evident . The observed number of events ( 8 ) in this 1 . 3 kb interval is significantly ( p<0 . 0001 by chi-square analysis ) in excess of that expected based on a random distribution of 188 events in the 119 kb CAN1-CEN5 interval . The interval between markers 43 and 44 includes part of the PCM1 gene and the SOM1-PCM1 intergenic region . As discussed above , elevated levels of mitotic recombination have been associated with certain types of DNA structures ( inverted repeats ) , microsatellite sequences , or high levels of transcription . There are no obvious structure/sequence elements in the 1 . 3 kb region , and SOM1 and PCM1 are not among the most abundant transcripts in the yeast genome [28] . We also compared the level of mitotic recombination for each marker ( measured as in Figure 7A ) with the level of gene expression of the ORF closest to the marker [28] by a linear regression analysis; no significant correlation was observed ( r2 = 0 . 004; p = 0 . 74 ) . An understanding of the nature of mitotic recombination hotspots will probably require identification and analysis of many hotspots . Several other points should be made concerning the distribution of mitotic events . First , the frequency of gene conversion events near the CAN1 gene is somewhat underestimated , since a conversion event extending through the can1-100/SUP4-o markers would not result in a CanR red/white sectored colony . Second , in our previous study of mitotic recombination [6] , we did not observe a reduction of exchange in the 35 kb URA3-CEN5 interval . In this previous study , however , our estimate of crossovers was based on a relatively small number of events and was insensitive to a small degree of suppression . From our current study , it is possible that mitotic recombination , like meiotic recombination , is reduced close ( within 20 kb ) to the centromere . This conclusion , however , is tentative until studies of mitotic recombination have been extended to multiple chromosomes . In addition , although mitotic recombination is reduced near CEN5 , gene conversion events can extend through the centromere [29] . In summary , our analysis of the distribution of mitotic recombination events demonstrates that these events can be initiated at many locations in the CAN1-CEN5 interval , although we have preliminary evidence of one mitotic recombination hotspot . By a variety of microarray-based procedures , we and others have measured the distribution of meiosis-specific DSBs throughout the yeast genome [30]–[34] . We compared the number of mitotic conversion events involving each polymorphic site ( Figure 7 ) with the meiotic recombination activity of the nearest ORF ( derived from Table S2 ) [32] by a linear correlation and regression analysis . No significant correlation was observed ( r2 = 0 . 021; p = 0 . 41 by two-tailed test ) . Since meiotic recombinogenic lesions are generated by Spo11p which is not expressed in mitotic cells , this result is not unexpected . Before comparing mitotic and meiotic conversion events , we will briefly compare previous studies of mitotic conversions in yeast with our study . In our study , only mitotic conversion tracts associated with crossovers were examined . In a number of studies [35] , it was shown that mitotic conversion tracts associated with crossovers are longer than conversion tracts unassociated with crossovers . Most previous studies of mitotic conversion and crossovers were done using systems in which the length of the conversion was constrained in one of two ways . First , in studies involving inverted or direct repeats , the sizes of the conversion tracts are limited by the size of the repeats . Second , in experiments involving selection of a prototroph from a heteroallelic diploid , the system is biased against long continuous conversion tracts , the type of tract that is most common in our study . Nickoloff et al . [27] analyzed gene conversion events between homologous chromosomes in which an HO-induced DSB within the URA3 gene was the initiating lesion . The diploid strain was also heterozygous for markers flanking the HO cleavage site , approximately two kb to one side and 1 kb to the other . Most of the tracts were continuous , and 60% extended outside of the markers on one side or the other; 30% were beyond all of the markers , a minimal distance of 3 . 4 kb . In an analysis of 51 spontaneous mitotic conversion events unassociated with crossovers , Judd and Petes [19] found 49 that were greater than two kb , and 19 of these 49 were greater than four kb ( end points extending beyond the markers ) . 50 of the 51 tracts in this study were continuous . Using a different approach , Golin and Esposito [36] examined co-conversion of heteroalleles located about 30 kb apart on chromosome VII . Although the rate of co-conversion events was 50-fold less than the rates of conversion at one locus or the other , these co-events were 1000-fold more frequent than expected for independent events , arguing the possibility of rare very long mitotic conversion tracts . Although very long conversion tracts could reflect BIR [4] , co-conversion of two pairs of heteroalleles is unlikely to be a consequence of BIR . With the exception of the current study , there is only one analysis of meiotic and mitotic conversion events in the same genomic region of the same strain [19] . Of ten meiotic conversion tracts , eight had two defined endpoints ( compared to 11 of 51 mitotic events ) . The average size of these eight tracts was 2 . 1 kb , clearly shorter than the mitotic tracts . In two other meiotic studies using similar methods , average conversion tract lengths of 3 . 4 kb [18] and 1 . 5 kb [20] were observed . Because Borts and Haber [20] calculated the minimal tract lengths rather than the average of the minimal and maximal lengths , these two estimates are not significantly different . The most accurate estimates of meiotic conversion tracts can be obtained in strains with the maximum density of markers with the caveat that the markers themselves could influence the pattern of gene conversion [37] . In a genetic background very similar to one used in our study , Mancera et al . [21] used high-density microarrays to map meiotic crossovers and gene conversions with markers that had a median spacing of about 80 bp . In analyzing several thousand conversion events , Mancera et al . found an average tract length of 2 . 0 kb for conversions associated with crossovers and 1 . 8 kb for conversions unassociated with crossovers . In summary , our analysis , as well as those of others , demonstrates that meiotic conversion tracts are considerably shorter than mitotic conversion tracts . We will discuss three related aspects of the mechanism of mitotic recombination: 1 ) the timing of the initiating DNA lesion in the cell cycle , 2 ) the nature of the initiating DNA lesion , and 3 ) mechanisms of generating long continuous mitotic conversion tracts . Our analysis of spontaneous mitotic crossing-over in a 120 kb CEN5-CAN1 interval of yeast chromosome V demonstrates that most crossovers are associated with long continuous gene conversion tracts . Crossovers and conversions occur throughout the whole interval , although these events are reduced in frequency near the centromere and there is one modest hotspot for conversion located near CAN1 . About 40% of the recombination events have properties indicative of a DSB on one homologue in G1 , replication of the broken chromosome , and subsequent repair of the two broken chromatids .
Most of our analysis was done with two very closely related diploid strains PSL100 and PSL101; the only difference between these strains is that PSL100 is homozygous for the ura3-1 mutation and PSL101 is heterozygous ura3-1/URA3 . Isogenic diploids that were hemizygous for the mating type locus ( PG311 ) or lacked SPO11 ( MD457 ) were also analyzed . These diploids are identical except for changes introduced by transformation . Their constructions are described in Supp . Information and Table S1 . All diploids were homozygous for ade2-1 , heterozygous for can1-100 , and heterozygous for an insertion of SUP4-o at a position on chromosome V allelic to can1-100 . As explained in Results , reciprocal crossovers between CEN5 and CAN1 can be selected in strains of this genotype . In addition , each diploid was derived by crossing two sequence-diverged haploids ( isogenic derivatives of W303A and YJM789 ) , resulting in a diploid with many single-nucleotide polymorphisms [15] . The homologue with the can1-100 gene had the markers contributed by W303A and the one with the SUP4-o marker had the markers contributed by YJM789 . As described below , we used these markers to construct a high-resolution genetic map of the CEN5-CAN1 region . Standard yeast procedures were used for mating , sporulation , and tetrad dissection [14] . Rich growth medium ( yeast extract , peptone , dextrose; YPD ) and omission media were also made following standard recipes [14] except the medium contained 10 micrograms/ml of adenine . The solid medium used to select mitotic crossovers lacked arginine ( SD-arg ) and contained 120 micrograms/ml canavanine . The diploid strains PSL100 , PSL101 , MD457 , and PG311 were used to analyze mitotic crossovers . These strains were streaked for single colonies on YPD and incubated at 30°C . for 2 days . Individual colonies ( about 20/experiment ) were resuspended in 400 microliters of water . Each sample was diluted ( usually by a factor of 105 ) and plated onto solid medium lacking arginine in order to measure the number of cells per colony; colonies on the control plates were counted after the plates were incubated two days at 30° . 100 microliters of the undiluted samples were plated onto SD-arg medium containing canavanine . These plates were incubated at room temperature for four days , followed by one day of storage at 4° to minimize the background growth of canavanine-sensitive cells and accentuate the red color of colonies that lack the SUP4-o gene . We then counted the number of red/white sectored colonies , only counting colonies in which the smallest sector was at least one-eighth of the size of the total colony . Each sector was purified on solid YPD medium for the subsequent analysis described below . We isolated yeast DNA from purified red ( can1-100/can1-100 ) and white ( SUP4-o/SUP4-o ) sectors by standard procedures [14] . The numbers of sectored colonies analyzed for PSL100/101 , MD457 , and PG311 were 74 , 14 , and 14 , respectively . As described above , the diploids used in our study were heterozygous for many markers . By comparing the W303A sequence ( http://www . sanger . ac . uk/gbrowse/gbrowse/cere_dmc/ ) and the YJM789 sequence [15] , we identified 34 polymorphisms that changed restriction enzyme recognition sites that were located between CEN5 ( SGD coordinate of 152 , 000 ) and can-100/SUP4-o ( SGD coordinate of about 32 , 000 ) . The positions of these polymorphisms ( SGD coordinates ) are shown in Table S2 . For each polymorphism analyzed for individual sectors , we PCR-amplified the genomic DNA using the primers that flanked the polymorphism ( Table S2 ) and treated the resulting DNA fragment with the relevant restriction enzyme . The products were analyzed by standard agarose gel electrophoresis . This analysis allowed us to determine whether the strain representing the red or white portion of the sectored colony was homozygous for the YJM789 polymorphism , homozygous for the W303A polymorphism , or heterozygous for the polymorphism . Additional details of our analysis are given in Supp . Information . The meiotic segregation of markers in the diploid PSL101 was examined in 21 tetrads . All four spores of each tetrad were examined . All 34 markers were analyzed in six of the tetrads; the analysis of the remaining 15 was done by the same approach used for most of the mitotic sectors . In each tetrad , the crossovers and gene conversion events were mapped to the highest degree of resolution possible with the 34 markers . Statistical analyses ( Fisher exact test , Chi-square tests , and linear correlation analysis ) were done using the VassarStats Website ( http://faculty . vassar . edu/lowry/VassarStats . html ) . | Most higher organisms have two copies of several different types of chromosomes . For example , the human female has 23 pairs of chromosomes . Although the chromosome pairs have very similar sequences , they are not identical . Members of a chromosome pair can swap segments from one chromosome to the other; these exchanges are called “recombination . ” Most previous studies of recombination have been done in cells undergoing meiosis , the process that leads to the formation of eggs and sperm ( gametes ) . Recombination , however , can also occur in cells that are dividing mitotically . In our study , we examine the properties of mitotic recombination in yeast . We show that mitotic recombination differs from meiotic recombination in two important ways . First , the sizes of the chromosome segments that are non-reciprocally transferred during mitotic recombination are much larger than those transferred during meiotic exchange . Second , in meiosis , most recombination events involve the repair of a single chromosome break , whereas in mitosis , about half of the recombination events appear to involve the repair of two chromosome breaks . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/genome",
"projects",
"genetics",
"and",
"genomics/microbial",
"evolution",
"and",
"genomics",
"genetics",
"and",
"genomics/cancer",
"genetics",
"genetics",
"and",
"genomics/chromosome",
"biology"
] | 2009 | A Fine-Structure Map of Spontaneous Mitotic Crossovers in the Yeast Saccharomyces cerevisiae |
Detailed studies of individual genes have shown that gene expression divergence often results from adaptive evolution of regulatory sequence . Genome-wide analyses , however , have yet to unite patterns of gene expression with polymorphism and divergence to infer population genetic mechanisms underlying expression evolution . Here , we combined genomic expression data—analyzed in a phylogenetic context—with whole genome light-shotgun sequence data from six Drosophila simulans lines and reference sequences from D . melanogaster and D . yakuba . These data allowed us to use molecular population genetics to test for neutral versus adaptive gene expression divergence on a genomic scale . We identified recent and recurrent adaptive evolution along the D . simulans lineage by contrasting sequence polymorphism within D . simulans to divergence from D . melanogaster and D . yakuba . Genes that evolved higher levels of expression in D . simulans have experienced adaptive evolution of the associated 3′ flanking and amino acid sequence . Concomitantly , these genes are also decelerating in their rates of protein evolution , which is in agreement with the finding that highly expressed genes evolve slowly . Interestingly , adaptive evolution in 5′ cis-regulatory regions did not correspond strongly with expression evolution . Our results provide a genomic view of the intimate link between selection acting on a phenotype and associated genic evolution .
Changes in gene expression are governed primarily by the evolution of cis-acting elements and trans-acting factors . Several single-gene studies have combined data on expression , protein abundance , function , and sequence evolution to make powerful statements about the role of adaptive evolution in effecting phenotypic change [1 , 2] . These case studies of single genes focused on well-described pathways that were known , a priori , to have remarkable expression differences . As such , they may provide a biased view of the population genetic mechanisms controlling gene expression evolution . Thus , the question remains as to which forces , neutral or adaptive , predominate on a genomic level to bring about changes in gene expression . Recent studies have tried to discern the causes of genome-wide expression evolution solely from patterns of gene expression variation within and among species [3–5] . Patterns of constant expression levels across several species combined with significantly elevated or reduced expression in a single species have been taken as evidence of lineage-specific adaptive evolution [3 , 4] . Alternatively , low levels of within-population variation in expression compared to divergence in expression among species has also been taken as evidence of adaptive evolution [5–7] . As these studies are based strictly on phenotypic data—expression variation—they are indirect indicators of the underlying genetic and population genetic phenomena . For example , elevated lineage-specific expression divergence can be explained equally well by directional selection or by reduced functional constraint . These studies highlight the importance of direct tests of the mechanisms of evolution . For example , Good et al . [8] used statistical inferences of adaptive protein evolution along with expression evolution to investigate the connection between the two . Their highly conservative test suggested that no significant connection existed . In an attempt to unite population genetic inference with expression data , Khaitovich et al . [9] found a positive correlation between linkage disequilibrium and expression divergence in genes expressed in the human brain . This result is consistent with recent adaptive evolution of cis-acting regulatory elements associated with brain-expressed genes , but could also be due to selection on protein function . A global understanding of the population genetic processes acting on expression phenotypes requires both genomic expression data and genomic sequence variation and divergence data . Combining these data allows for the use of molecular population genetic tests to identify the underlying evolutionary mechanism . To this end , we combined expression data from three closely related species , D . simulans , D . melanogaster , and D . yakuba [6 , 10] , with population genomic sequence data from D . simulans [11] , and genome sequence data from D . melanogaster [12] and D . yakuba [11] . These data allow us to polarize both expression and sequence evolution to particular lineages . Additionally , we used the sequence data to mask expression probes ( which were developed using the D . melanogaster reference ) with sequence mismatches in D . simulans and D . yakuba . This approach has the critical advantage that it does not confound expression divergence with sequence evolution across lineages . DNA polymorphism and divergence data allow one to directly test for both recent and recurrent directional selection on genes and noncoding regions associated with rapid changes in expression . If expression evolution were due to recent directional selection on cis-acting elements , we predict a reduction in the DNA heterozygosity to divergence ratio in flanking regions of genes showing expression evolution relative to genomic averages [13] . Alternatively , if recurrent directional selection has acted on cis-regulatory sequences controlling expression levels , one might observe excess fixations at regulatory sites relative to nearby “neutrally” evolving sites [14] . Finally , if gene expression diverges primarily due to trans-acting factors or neutral processes at cis-acting sites , one would expect no evidence of directional selection on noncoding sequences near genes showing expression divergence . Here , we use population genomic and gene expression data from Drosophila to address the following questions: Is expression evolution associated with adaptive evolution of cis regions ? Are genes with modified expression patterns also evolving modified protein function under directional selection ? Are genes that change expression over short time scales clustered into distinct functional groups ?
We reanalyzed previously collected expression data from adult male D . melanogaster , D . simulans , and D . yakuba from the Drosophila v1 Affymetrix GeneChip Array [6 , 10] . Sequence divergence of probe targets in D . simulans and D . yakuba could confound expression analysis [15] , so mismatched probes were masked before analysis . After masking procedures , 4 , 427 probe sets remained , with an average of 3 . 81 ( SE ± 1 . 01 ) probes per set . We defined genes that are increasing and decreasing in expression in D . simulans as those in the 5% tails of expression divergence from the D . melanogaster–D . simulans ancestor ( see Materials and Methods ) . Cis-regulatory element evolution directly affects transcription and mRNA half-life ( see [16 , 17] ) . Cis-acting elements , such as core promoters , that regulate transcription are predominantly located in 5′ regions and those that control mRNA stability and degradation are primarily located in 3′ regions [16 , 17] , although there is considerable variation among genes . We tested for evidence of an association between recent and recurrent directional selection in 5′ and 3′ flanking regions ( which include UTRs and putative regulatory regions ) and significant changes in expression levels . Reductions in polymorphism relative to divergence indicate the action of recent directional selection [13] . Flanking regions with polymorphism to divergence ratios in the lowest 5% tail of the distribution were taken as having evidence of recent selective sweeps . Figure 1 depicts mean levels of polymorphism and divergence in 5′ and 3′ noncoding sequence . Flanking regions and UTRs have lower levels of polymorphism and divergence than silent sites , which is in agreement with previous findings that noncoding regions are under greater constraint than silent sites [13] . Genes with increased expression levels show more variability in levels of polymorphism and divergence over different features , but no strong pattern emerges . There is no evidence of hitchhiking effects in either 5′ or 3′ UTR or flanking regions in association with changes in expression ( Figure 2; Table S1 ) . Using an extension of the McDonald-Kreitman test [14] for noncoding sites , we compared flanking polymorphic and fixed sites to synonymous sites of the corresponding gene to infer the action of recurrent directional selection . Genes with significant expression evolution show more evidence of recurrent directional selection in 3′ UTRs and 3′ flanking regions than expected by chance ( Figure 2; Table S1 ) . Genes with increases in expression drive this relationship . Although genes with reduced expression have more 3′ UTR and flanking region divergence than genes with no change in expression , the tests provide no strong evidence of recurrent adaptation associated with reduced gene expression ( Figure 2; Table S1 ) . The 5′ regulatory regions of genes with increased expression show the same trend , but again the result is not statistically significant ( Figure 2; Table S1 ) . Thus , recurrent adaptive evolution of 3′ cis-regulatory regions likely plays a critical role in adaptive expression increases . The 3′ regulatory regions are bound by elements , such as microRNAs , that can stabilize or destabilize mRNA ( see [18] ) . Given the linkage between adaptive evolution of 3′ regulatory regions and expression evolution , we hypothesized that microRNAs may be coevolving with their target genes . We retrieved information on known microRNAs and their targets in D . melanogaster from miRBase [19 , 20] . We found that those microRNAs that regulate a greater number of genes with changes in expression have faster , but not significantly faster , rates of evolution ( Spearman's ρ = 0 . 2065 , p = 0 . 1073 ) . Rapid evolution of microRNAs and adaptive expression divergence associated with 3′ regions strongly motivate in-depth investigation of the 3′ flanking regions to uncover the functional mechanisms for transcriptional regulation of genes with significant expression evolution . Increases in gene expression were more often associated with adaptive evolution than decreases in expression ( Figure 2 ) . This observation does not appear to be due to a bias in analysis of the data because expression changes are normally distributed and there is no correlation between estimated ancestral divergence and change in expression ( see Materials and Methods ) . However , continually increasing expression levels cannot persist over long evolutionary time scales . In fact , expression levels are typically under strong stabilizing selection ( [5] , and see Materials and Methods ) . A speculative hypothesis for this observation relies on relaxation of codon bias . Begun et al . [11] documented an accumulation of fixations for unpreferred codons in D . simulans . If these unpreferred codons are slightly deleterious and reduce translational efficiency , regulatory regions may be under directional selection to compensate for this phenomenon by making more transcript available for translation . As seen in previous research [6 , 8] , genes with greater absolute levels of expression divergence evolve faster at the protein level ( mean dN ± SE 0 . 0046 ± 0 . 0003 and 0 . 0034 ± 0 . 0001 , for genes changing in expression and not changing , respectively; Wilcoxon: p < 0 . 0001; Table S2 ) . Genes with rapid expression evolution are also represented by fewer expression probes per set ( mean number of probes ± SE 2 . 98 ± 0 . 076 versus 3 . 90 ± 0 . 033; Wilcoxon: p < 0 . 0001 ) . A rapid rate of sequence evolution would lead to more probe mismatch , which explains the observed pattern . This also renders our expression divergence analysis conservative , as our power to detect a significant expression difference is reduced for the most rapidly evolving genes . Interestingly , even though genes with significant increases in expression in D . simulans have higher average dN , they show decelerating dN in D . simulans relative to D . melanogaster and D . yakuba ( resampling test: p = 0 . 023; method for relative rates described in Begun et al . [11] ) . The same is not true of genes with decreasing expression ( p = 0 . 861 ) . While higher average rates of amino acid evolution in genes with expression divergence could have been indicative of relaxed purifying selection , the deceleration in dN certainly speaks against that hypothesis . Previous work showed that high levels of expression correlate with lower rates of protein evolution [21–23] , which may reflect selection for translational robustness [23] or translational accuracy [22] . The deceleration in protein evolution of genes with increases in expression is consistent with the idea of stronger translational selection on highly expressed genes , but overall , we see only a weak relationship between expression level and protein divergence ( Spearman's ρ = −0 . 1821 , p < 0 . 0001 ) . Genes adaptively evolving modified expression patterns may also be adaptively evolving modified protein function . We estimated the proportion of genes in each expression class—increasing , decreasing , and no change—with evidence for recurrent directional selection using the McDonald-Kreitman test [14] . For all genes in this analysis , the proportion undergoing recurrent adaptive evolution was similar to the genome-wide estimate [11] . The prevalence of recurrent adaptive evolution was not significantly different for genes showing expression evolution versus those showing no expression evolution ( p = 0 . 4438; Figure 2 and Table S1 ) . We also tested for evidence of recent directional selection as measured by a reduction in the ratio of silent polymorphism to silent divergence [13] . Coding regions with ratios in the lowest 5% tail of the distribution were taken to have evidence for recent selective sweeps . A higher proportion of genes showing expression evolution have significantly reduced ratios of silent site polymorphism to divergence , which is consistent with recent selective sweeps ( p = 0 . 0445; Figure 2 and Table S1 ) . Genes with increased expression levels explain more of this relationship than genes with decreased expression ( increase p = 0 . 0328 , decrease p = 0 . 2530 ) , although both sets have greater reductions of silent polymorphism to divergence ratios than genes that are not changing in expression . The targets of these putative hitchhiking events may have been nearby regulatory regions in an intron or upstream or downstream of the protein coding region . Alternatively , one possible explanation for the association between upregulation and recent selection on coding regions is codon bias . Gene expression is positively correlated with codon bias [22] . Given this association , hitchhiking effects of preferred codons might increase with increasing levels of expression due to stronger selection for translational accuracy [22] . While there is a higher ratio of preferred to unpreferred polymorphisms and fixations in genes evolving increases in expression versus those that show no expression evolution , the difference is not statistically significant ( Fisher's Exact Test: p ≫ 0 . 05 for both tests; Table 1 ) . There may be a time lag between expression evolution and the fine-tuning of translation via codon bias . Thus , our data might mean that genes with the most extreme expression differences have recently increased expression . Alternatively , the hitchhiking events may result from adaptive evolution acting on one or a few amino acids or on nearby regulatory regions . We used gene ontology information from Flybase and from the generic Gene Ontology Slim set of terms to determine whether certain functional classes of genes were more likely to evolve expression differences . Six ontology terms are significantly enriched for genes both with significant increases and decreases in expression ( Table S3 ) . Two of those terms , chymotrypsin and trypsin activity , have completely overlapping genes and are part of a larger category , serine-type endopeptidase activity . These genes have many functions , including reproduction , digestion , and immunity [24] . Three other categories , courtship behavior , negative regulation of transcription , and sex determination appear to be unrelated on the surface , but closer inspection of the genes in these categories reveals that all are involved in regulation of transcription or chromatin remodeling . These functions frequently evinced adaptive protein evolution in the genome-wide analysis of adaptive evolution in D . simulans [11] . This suggests that there may be a connection between adaptive protein evolution and expression divergence for some biological functions . Because adaptive evolution of 3′ cis-regulatory regions may be driving expression divergence , at least for genes with increased expression , we examined the classes of genes associated with genes that have both evidence for adaptive 3′ evolution and significant expression divergence ( Tables S4 and S5 ) . We also investigated ontology terms associated with genes showing evidence of hitchhiking events and significant expression divergence ( Table S6 ) . Generally , genes with adaptive 3′ or protein evolution are found in the cytoplasm or are integral to the membrane . Their molecular functions are predominantly protein binding , nucleic acid binding , and translation related . The most common biological processes are related to response to stimuli , RNA regulation ( binding , splicing , degradation ) , and metabolism . In this study , we link adaptive sequence evolution to phenotypic change on a genome-wide scale . Several recent studies have illustrated the importance of adaptive evolution acting on noncoding DNA [11 , 25 , 26] , and our data reinforce this point . More critically , we show that adaptive evolution of cis-acting elements in 3′ regions is clearly associated with and may be driving lineage-specific increases in expression that lead to phenotypic differences among species . Recent work suggests that genes with certain 5′ promoter elements show an increased interspecies variability in expression in yeast as well as Drosophila [27] . In contrast , our data implies that 3′ regulatory regions are playing a more critical role in adaptive expression divergence . Functional genomic investigation of these 3′ cis-regulatory regions is clearly warranted . The question now becomes , how and why do genes involved in important processes such as chromatin remodeling change their expression patterns through 3′ cis-acting regulatory adaptive evolution ?
We reanalyzed expression data from 3-d-old virgin adult males of one isogenic line of D . melanogaster , ten isogenic lines of D . simulans , and one isogenic line of D . yakuba [6 , 10] . Three replicate chips for each line were used . All data were collected at the same location under standard conditions using the Affymetrix GeneChip Arrays ( Drosophila 1 . 0 ) , which contain 13 , 966 features representing the genome of D . melanogaster . Because the D . melanogaster gene annotation has been updated since the array was developed , we compared probe sequences to the D . melanogaster genome to determine which genes were targeted with each probe set . The probes representing features on the Affymetrix GeneChip Arrays are constructed for D . melanogaster and are not expected to perfectly match other species . Prior research suggests that such imperfect matches cause incorrect measures of expression due to poor hybridization [10 , 15 , 28] . To account for the confounding effect of probe sequence divergence among species on gene expression measures , only probes that were identical matches to the genome sequences of D . melanogaster , D . simulans , and D . yakuba were included in analyses . Probes showing any divergence among the probe sequence on the array and the genome sequences of the three species were masked . Probe sets with fewer than two probes remaining after masking ( out of the original 14 ) were removed before downstream analyses . Finally , probe sets that bound to overlapping genes or homologous sequence of multiple genes were also removed , as the signal could not be attributed to a single gene . After probe-masking procedures , all chips were normalized and expression intensities were calculated using gcrma from the affy package available in Bioconductor [29 , 30] . The mean of the log2 expression intensity for each probe set was then calculated for each species . Probe sets for which the log2 mean intensity of at least one species was not greater than three were considered absent . Of the original 195 , 944 probes from 13 , 996 probe sets , 16 , 850 probes representing 4 , 427 probe sets remained after masking and removing probe sets with no detectable expression in either D . melanogaster or D . simulans ( all expression data are in Table S7 ) . The distribution of expression intensities was highly similar between species ( Figure S1 ) and probe set intensities were highly correlated between species ( Spearman's ρ = 0 . 92 between D . simulans and D . melanogaster and ρ = 0 . 89 between D . simulans and D . yakuba ) . However , probe sets with fewer probes have higher coefficients of variation in D . simulans and in D . melanogaster ( Kruskal-Wallis tests: p < 0 . 0001 for all four tests ) . We tested whether probe sets with fewer probes gave reliable estimates of mean expression intensity . We randomly sampled four probes from probe sets that had all 14 probes remaining after masking . The mean expression intensity of the sample was highly correlated with the mean intensity estimated from all 14 probes ( Spearman's ρ = 0 . 869 ) . The mean expression level varied by +/− 7% , and the variance in expression among replicates increased by 22% . Ancestral expression states were reconstructed using AncML v 1 . 0 [31] using the average of normalized log2 expression values for each species . Expression divergence was calculated as follows: where Esim is the expression level of D . simulans and EAncmel-sim is the estimated expression level of the D . simulans/melanogaster ancestor . Figure S2 depicts the distribution of expression change along the D . simulans lineage . The distribution is not significantly different from normally distributed . Additionally , there is no correlation between change in expression along the D . simulans branch and the expression level of the inferred ancestor ( Figure S3 ) . The conical nature of Figure S3 reflects the negative correlation between expression level and expression divergence over short evolutionary time scales . We defined genes that are increasing and decreasing in expression in D . simulans as those in the 5% tails of expression divergence from the D . melanogaster–D . simulans ancestor . We calculated confidence intervals ( CI ) around the expression values for D . simulans and determined whether the D . melanogaster expression estimate fell within the D . simulans CI . Intraspecific expression divergence values in the tails are not normally distributed , so we calculated CIs in R using bias correction and acceleration [32] . One probe set ( of 221 ) with increasing expression and four probe sets ( of 221 ) with decreasing expression along the D . simulans lineage had mean intensities in D . melanogaster within the 95% CIs of D . simulans . Drosophila simulans and D . yakuba syntenic assemblies are described in Begun et al . [11] and information on the D . yakuba genome project can be found at http://genome . wustl . edu . From light-shotgun sequencing of six lines of D . simulans , a total of 109 Mbp of euchromatic sequence were covered by at least one of the six lines . Each line had 43%–90% coverage of that 109 Mbp with an average of 3 . 6 alleles per site . However , coverage of genic regions was somewhat higher at 3 . 9 alleles per site . Genes and Affymetrix probes were localized using the Flybase v . 4 . 2 annotation ( http://flybase . org/annot ) . Genes included were from two categories . The first set maintained the gene model of D . melanogaster meaning that , in D . simulans , they have canonical translation initiation codons ( or that matched the D . melanogaster noncanonical codon ) , canonical splice junctions at the same position as D . melanogaster ( or noncanonical splice junctions that were identical to the D . melanogaster nucleotides at splice sites ) , no premature termination , and a canonical termination codon . The second set was less conservative in that the gene could have a different gene model with respect to only one of the aforementioned criteria ( i . e . , either a noncanonical translation initiation codon at the D . melanogaster initiation site , or noncanonical splice junctions , or lack a termination codon at the D . melanogaster termination ) . Additionally , genes with premature terminations in the last exon were included . There were very few genes with imperfect models in any of the expression groups ( 10/212 with increased expression , 14/210 with decreased expression , and 173/3 , 814 with no change in expression ) . Only gold collection UTRs ( i . e . , those with completely sequenced cDNAs ) were used in analyses ( http://www . fruitfly . org/EST/gold_collection . shtml ) . Flanking regions consisted of sequence 1 , 000 bases upstream and downstream of any annotated UTR sequence for each gene ( or initiation/termination codons for genes without annotated UTRs ) . Flanking sequence was truncated if the coding sequence of a neighboring gene was within the 1 , 000 bases . We also investigated 300 bases upstream of the 5′ UTR ( see Table S1 ) , which would target core promoter regions , and recovered the same results as with 1 , 000 bases upstream . Some statistical tests were performed using JMP IN v5 . 1 ( SAS Institute ) . PERL scripts for calculations of estimated nucleotide diversity ( π ) , McDonald-Kreitman tests , and resampling tests were written by and can be obtained from AKH . Nucleotide diversity was estimated as in Begun et al . [11] for each genomic feature ( exon , intron , UTRs , flanking ) that had a minimum number of nucleotides represented [i . e . , n ( n − 1 ) × s ≥ 100 , where n = average number of alleles sampled and s = number of sites] . The measure of nucleotide diversity , π , is the coverage-weighted average expected heterozygosity of nucleotide variants and is therefore an unbiased estimate of polymorphism . For coding regions , the numbers of silent and replacement sites were counted using the method of Nei and Gojobori [33] . The pathway between two codons was calculated as the average number of silent and replacement changes from all possible paths between the pair . Estimates of π on the X chromosome were corrected for sample size [π w = π × ( 4/3 ) ] under the assumption that males and females have equal population sizes . Lineage-specific divergence was estimated by maximum likelihood using PAML v3 . 14 [34] and was reported as a weighted average over each D . simulans line with greater than 50 aligned sites in the segment being analyzed . PAML was run in batch mode using a BioPerl wrapper [35] . For noncoding regions , we used baseml with HKY as the model of evolution to account for transition/transversion bias and unequal base frequencies [36] , and for coding regions we used codeml with codon frequencies estimated from the data . For all genes , 0 . 001 was added to heterozygosity and divergence values so that we could calculate ratios for genes with entries of zero . We did not analyze genes with zero values for both heterozygosity and divergence . Even after correction for smaller effective population sizes , heterozygosity at silent sites is significantly lower on the X chromosome than on autosomes ( Kruskal-Wallis test: p < 0 . 0001 , Tukey's HSD shows X is different from all autosomes ) , so we defined significantly low heterozygosity/divergence ratios separately for the X and autosomes . For each feature , genes in the lowest 5% tail of silent site heterozygosity/divergence ratios were defined as being significantly low and therefore showing evidence of a recent selective sweep . Those ratios defined as having evidence of recent selective sweeps were at least 10-fold lower than the mean ratio for all features . D . simulans–specific accelerations/decelerations in protein evolution were calculated as described in Begun et al . [11] . Polarized MK tests minimized the numbers of nonsynonymous substitutions and required that D . melanogaster and D . yakuba share the same codon to ensure that fixations and polymorphisms were attributable to evolution along the D . simulans lineage . We used a derivative of the McDonald-Kreitman test [14] to evaluate evidence for recurrent directional selection in noncoding regions . Polymorphic and fixed sites of noncoding DNA were compared to polymorphic and fixed silent sites of the gene . Again , we only analyzed sites where D . melanogaster and D . yakuba shared the same nucleotide . With very few polymorphisms and fixations there is little power to detect the action of directional selection . Therefore , we imposed a minimum row and column count for tests to be included in downstream analyses . We required that each row and column in the 2 × 2 table have a sum of at least five observations . We also removed any tests that had a significant test result but that had a neutrality index value greater than one , ( which indicates excess amino acid/noncoding polymorphism not directional selection [37] ) in order to calculate the proportion of genes that are experiencing recurrent directional selection . All data for D . simulans heterozygosity , lineage-specific divergence and MK tests are listed in Table S8 . Substitutions to preferred and unpreferred codons were estimated by a parsimony method developed by Y . -P . Poh [11] . For each category of interest ( e . g . , increasing or decreasing expression levels ) , we calculated the proportion of genes with a significant test result ( for MK tests , p ≤ 0 . 05 , for heterozygosity/divergence ratios were considered significant if they fell in the 5% tail ) . We then tested whether this proportion was significantly greater than the random expectation using resampling tests . We randomly drew n p-values from the set of all genes where n is the number of genes in the category . We repeated this procedure 10 , 000 times to get the empirical distribution of proportion genes with significant tests . We obtained cellular component , molecular function , and biological process ontology terms from the Flybase gene ontology terms ( http://flybase . org/genes/lk/function ) in combination with the generic Gene Ontology Slim set of ontology terms ( http://geneontology . org/GO . slims . shtml#avail ) . The proportion of genes with significant expression evolution was calculated for each ontology term . We determined whether each ontology term had a higher proportion of genes with significant D . simulans expression divergence than would be expected from the empirical distribution . We derived the empirical distribution for each ontology term by drawing the same number of genes as was in the term from all genes with expression data . We then calculated the proportion in the resampled dataset with significant expression evolution . We used 10 , 000 resampled data sets to derive the empirical distribution for each term . | Changes in patterns of gene expression likely contribute greatly to phenotypic differences among closely related organisms . However , the evolutionary mechanisms , such as Darwinian selection and random genetic drift , which are underlying differences in patterns of expression , are only now being understood on a genomic level . We combine measurements of gene expression and whole-genome sequence data to investigate the relationship between the forces driving sequence evolution and expression divergence among closely related fruit flies . We find that Darwinian selection acting on regions that may control gene expression is associated with increases in gene expression levels . Investigation of the functional consequences of adaptive evolution on regulating gene expression is clearly warranted . The genetic tools available in Drosophila make functional experiments possible and will shed light on how closely related species have responded to reproductive , pathogenic , and environmental pressures . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology",
"drosophila",
"genetics",
"and",
"genomics"
] | 2007 | Adaptive Gene Expression Divergence Inferred from Population Genomics |
Biomarkers that drift differentially with age between normal and premalignant tissues , such as Barrett’s esophagus ( BE ) , have the potential to improve the assessment of a patient’s cancer risk by providing quantitative information about how long a patient has lived with the precursor ( i . e . , dwell time ) . In the case of BE , which is a metaplastic precursor to esophageal adenocarcinoma ( EAC ) , such biomarkers would be particularly useful because EAC risk may change with BE dwell time and it is generally not known how long a patient has lived with BE when a patient is first diagnosed with this condition . In this study we first describe a statistical analysis of DNA methylation data ( both cross-sectional and longitudinal ) derived from tissue samples from 50 BE patients to identify and validate a set of 67 CpG dinucleotides in 51 CpG islands that undergo age-related methylomic drift . Next , we describe how this information can be used to estimate a patient’s BE dwell time . We introduce a Bayesian model that incorporates longitudinal methylomic drift rates , patient age , and methylation data from individually paired BE and normal squamous tissue samples to estimate patient-specific BE onset times . Our application of the model to 30 sporadic BE patients’ methylomic profiles first exposes a wide heterogeneity in patient-specific BE onset times . Furthermore , independent application of this method to a cohort of 22 familial BE ( FBE ) patients reveals significantly earlier mean BE onset times . Our analysis supports the conjecture that differential methylomic drift occurs in BE ( relative to normal squamous tissue ) and hence allows quantitative estimation of the time that a BE patient has lived with BE .
There is great interest in the molecular characterization of precancerous fields and lesions ( e . g . , colorectal adenomas or ductal carcinoma in situ ( DCIS ) in the breast ) to quantify their neoplastic potential , although it is generally not known how long such lesions ( or fields ) have sojourned in a patient when they are discovered . This point is of particular importance in the case of Barrett’s esophagus ( BE ) , a variable-length metaplastic precursor of esophageal adenocarcinoma ( EAC ) that has been shown to undergo a stepwise progression to cancer involving multiple rate-limiting events [1–3] . In spite of a generally low EAC progression risk of about 0 . 2–0 . 5% per year across BE patients [4] , the progression risk is believed to be highly variable and dependent on age , gender , histopathological grade , and personal risk factors such as severity of gastroesophageal reflux disease ( GERD ) , body mass index ( BMI ) , and smoking status [5] . However , since the total number of BE patients who progress to EAC is generally low for most epidemiological studies ( mostly due to limited follow-up ) , inter-individual variability in progression risk is difficult to specify other than by gross factors . Furthermore , the clinical assessment of the BE tissue is known to be fraught with uncertainty as only a small portion of the tissue is biopsied for pathology . Thus , there is a pressing need to develop more accurate markers ( and risk stratifications ) that identify BE that is more likely to progress to EAC in a person’s lifetime versus BE that is indolent or has low neoplastic potential . Inter-individual variability in the EAC progression risk may depend on the duration of how long a patient has lived with BE ( BE dwell time ) . In a large population-based study in Northern Ireland , Bhat et al . [6] found a significant increase of the annual progression risk with patient age ( 2-fold from age <50 to age 60–69 ) suggesting that the BE-to-EAC progression risk is not constant but rather increases with the age of the BE tissue due to the stepwise accumulation of genetic and epigenetic alterations that drive premalignant and malignant progressions in BE [1 , 2 , 7] . Thus , a longer dwell time for BE may increase the risk for neoplasia and cancer in an exponential manner consistent with the exponential increases observed in the age-specific incidence of EAC in the general population [8 , 9] . Also , in an environment of chronic inflammation analogous to that which is caused by GERD within BE , patients with ulcerative colitis have a higher colon cancer risk that increases with earlier age of onset and disease duration [10 , 11] . These risk factors unfortunately cannot be identified clinically in the case of BE because BE is asymptomatic . Yet , the use of mathematical modeling to quantifiy the waiting ( or dwell ) time of premalignant stages during carcinogenesis until the occurrence of cancer has been of considerable interest [12] . Recently identified age-related changes in DNA-methylation have led to the notion of a biological tissue age which , although highly correlated with chronological age , may differ significantly from it [13 , 14] . It is generally believed that epigenetic drift ( i . e . , neutral changes in DNA methylation levels ) is responsible for this process [15] . In this study we examine array-based methylation patterns of CpG-dinucleotides across the genome to determine whether CpGs that drift differentially between BE and normal tissue can be used to infer the relative biological age of a patient’s BE tissue . Specifically , we identify CpGs that undergo such ‘methylomic drift’ based on array data from formalin fixed paraffin embedded ( FFPE ) tissue samples from two groups of BE patients: one group of 10 patients each with 2 or more tissue samples that were obtained at least 5 years apart ( data set D1 ) . These samples provide longitudinal information at the individual level . A second group of 30 patients ranging in age from 21 to 88 ( data set D2 ) had matched tissue samples obtained from Barrett’s esophagus and adjacent normal esophagus squamous epithelium ( SQ ) , providing cross-sectional information as well as differential drift information between SQ and BE tissue . The combined statistical analyses of these two data sets , as described in Materials and Methods , suggest that numerous hypomethylated CpG sites undergo significant differential methylomic drift in BE versus SQ . Significantly , the observed patient-specific drift differentials appear relatively uniform across the set of identified 67 CpGs , giving rise to high correlations in the methylation differentials ( against the mean drift ) between CpGs . Thus , a hallmark of methylomic drift is that the associated methylation differentials between markers ( across patients ) are highly correlated , as are all clocks that keep time . We also validated the computed methylomic drift rates for the 67 selected CpGs in an independent data set of 10 additional BE patients ( data set DV ) each with samples at two time points . To infer patient-specific BE onset times from the measured methylation levels of identified CpGs that drift differentially between BE and SQ tissues , we use a Bayesian model that accounts for ( CpG-specific ) random effects in drift rates , measurement error , and a patient-specific BE onset time . Furthermore , to gain insights into how the age of BE onset may influence EAC risk , we used a recently developed mathematical model for EAC incidence to compute standardized lifetime risks for the individuals in data set D2 given their predicted BE onset times [8 , 16] . Additionally , we applied this methodology to methylation array data from 22 familial BE ( FBE ) patients ( data set D3 ) . The quantitative predictions of both BE onset times and inferred EAC risks for BE patients without neoplasia ( D2 ) and familial BE ( D3 ) suggest that BE onset is a useful event-marker of cancer risk . In the following we describe the data and methodologies that support this conclusion .
The human tissues used for the analyses presented here were obtained from 72 patients with confirmed Barrett’s esophagus ( BE ) . Written informed consent was obtained , signed by all participants , and conformed to institutional ethics requirements . IRB approval ( protocol numbers 1989 , 8137 ) was given by the ethical review board of the Fred Hutchinson Cancer Research Center . We examined levels of DNA methylation at over 450 , 000 CpG sites in tissue samples from four groups of BE patients ( see S1 Table for detailed patient information ) . The first data set ( D1 ) is unique and consists of serial samples from 10 BE patients , ages 33–70 years at index biopsy ( mean age = 51 . 2 ) , with 2 or more tissue biopsies each that were collected at least 5 years apart to comprise a total of 29 samples . D1 patient data for two particular CpGs that show longitudinal drift for each of these 10 patients’ serial sample sets are shown in Fig 1 . The second , cross-sectional data set ( D2 ) includes matched BE and normal squamous esophageal epithelium ( SQ ) tissue samples from 30 BE patients ages 21–88 years ( mean age = 63 . 4 ) comprising a total of 60 tissue samples . While the D1 data provide some information on methylomic drift in BE tissue for each patient , the aggregated cross-sectional data also provide population-level information on the mean drift rate across all patients and ages . Although methylomic drift may depend on various factors , here we will focus on the influence of BE dwell time , which may be highly variable from patient to patient , even for patients of similar age . Fig 2 shows the probability densities of BE onset for two representative D2 patients’ ages at time of biopsy ( a1 = 21 , a2 = 80 ) , and the theoretical consequence their ages will have on the statistical inference of their BE onset ages . The inter-individual heterogeneity in BE onset times will thus affect the methylation level data around the mean population drift . An illustration for a single CpG site j for the BE samples from D2 is shown in the insert of Fig 2 . Note , for the cross-sectional group ( D2 ) , the matched BE and SQ samples originate from biopsies collected during the same endoscopic exam . The third serial data set ( DV ) consists of 10 BE patients from Cleveland Clinic Foundation , ages 54–77 years at index biopsy ( mean age = 51 . 2 ) , with 2 serial tissue biopsies each , comprising a total of 20 BE samples . The fourth data set ( D3 ) includes BE tissue samples from 22 familial BE ( FBE ) patients ages 39–84 years ( mean age = 62 . 8 ) with one sample per patient . Familial Barrett’s esophagus ( FBE ) was defined as having a first- or second-degree relative with long-segment BE , adenocarcinoma of the esophagus , or adenocarcinoma of the gastroesophageal junction whose diagnosis was confirmed by review of endoscopy and histology reports [22] . The data also include gender and age when the tissue biopsy was collected for each patient ( see S1 Table ) . Two concepts have so far emerged that relate alterations in DNA methylation to biological tissue age . The first is based on the discovery of sets of clock-CpGs that undergo age-dependent changes in methylation that in combination correlate strongly with chronological age [13 , 14 , 23] . The second concept relates to subtle changes in methylation levels due to epigenetic drift as a result of a semi-conserved replication process of DNA-methylation patterns [24–27] . Significantly , some CpG-islands that show very low ( hypo- ) methylation levels early in life are known to undergo gradual methylation over time , presumably as a result of sporadic de novo methylation events during DNA replication , a process commonly understood as epigenetic or methylomic drift [15 , 24 , 28–31] . Therefore , to narrow the number of CpG candidates that may serve as markers for differential tissue aging in the emerging metaplastic tissue of BE patients , we first identified CpGs that show significant longitudinal drift among the patients of our longitudinal study D1 , as described below . The following steps summarize our discovery pipeline in more detail . Here we show how information about methylomic drift characteristic of BE and differential between BE tissue and normal squamous ( SQ ) tissue can be combined with individual-level methylation data at a given age to predict when a patient developed BE assuming there is a single time point of origin for BE . Our model ( described below ) employs Bayesian inference to derive dates of BE onset via initial differential drift away from squamous methylation values , and in this way our method can be considered somewhat analagous to dating divergence times in phylogenies with a relaxed molecular clock [32] . In the following we assume that methylomic drift is essentially linear with age ( at the logit scale ) , although there is also evidence that age-associated variation in methylation levels may be better modeled by a function of logarithmic age for younger individuals [23] . However , this approach has the flexibility to accommodate non-linear drift . For patient i , i = 1 , … , N , the data consist of measurements yBEi , j ( ti ) for BE clock CpGj ( j = 1 , … , 67 ) at observation time ( age ) ti = ai . We consider the following linear drift model for the conditional expected methylation values of variable YBEi , j ( ti ) , taken from patient i at time ti for each clock CpG , given the onset of BE occurred at time si ≤ ti , E [ Y B E i , j ( t i ) ] = α S Q j + b S Q j s i + b i , j ( t i - s i ) , ( 1 ) for j = 1 , … , 67 . Thus , given the following parameters—the onset of BE at time TBE = si , the rate ( bSQj ) and intercept ( αSQj ) of the SQ population regression lines obtained from individuals with matched samples in data set D2 , and the patient-specific , CpG-specific BE drift rate bi , j—we observe 67 independent measurements for N independent individuals . Furthermore , we used the linear regression slopes and intercepts provided by the ANCOVA procedure using the normal squamous sample group in D2 to impute αSQj and bSQj in D3 for each BE clock CpG , as implemented in the model shown in Eq ( 1 ) . For this data set , we did not have matched SQ samples but because the methylation values in normal squamous tissue show little variation for our selection of BE clock CpGs , we assumed that the normal squamous tissues behave similarly for non-familial and familial patients . We show that this approach for imputing SQ M-values for non-matched samples is robust in a sensitivity analysis given in Results . Allowing for patient-specific drift rates for the BE clock CpGs , we explicitly model the inter-individual differences in BE drift rates between ‘slow’ and ‘fast’ aging BE tissues relative to the standard clock , which are measured from means and standard deviations of the serial samples . Again , the observation from a single patient i , for i = 1 , … , N , observed at time ti , is of the form y i = { y B E i , j , j = 1 , ⋯ , 67 } . ( 2 ) In the Bayesian BE clock framework defined by Eq ( 1 ) , the likelihood contribution from a single patient observed at time ti is given by ∏j=167f ( yBEi , j ) =∏j=167fN ( yBEi , j;μBEi , j=αSQj+bSQjsi+bi , j ( ti−si ) , σBEi ) , ( 3 ) where fN is the normal density function . For the Bayesian model we further assume uniform priors ps ( si ) for the BE onset times si ( due to the fact that the distribution of BE onset times in the general population is essentially unknown ) , conjugate gamma priors pσ ( σBEi ) for the standard deviation σBEi of methylation measurement values using shape and scale parameters fitted to the distribution of non-drifting CpG measurements , and normal prior distributions pb ( bi , j ) for the drift rates bi , j , j = 1 , … , 67 , which were derived from the longitudinal data sets with empirical mean and standard deviation ( see S1 Text for full expressions of prior distributions ) . In order to ultimately simulate the BE onset times s1 , … , sN from the corresponding patient-specific posterior distributions , let us define the vector Ψi = ( si , bi , 1 , … , bi , 67 , σBEi ) for patient i . Samples of Ψi under its posterior distribution for patient i will be obtained using Markov Chain Monte Carlo ( MCMC ) . The posterior distribution of Ψi given the observation yi comprised of patient-specific data of the form in Eq ( 2 ) , for i = 1 , … , N , is given by π ( Ψ i | y i ) ∝ likelihood · prior ( 4 ) = ∏ j = 1 67 f N ( y B E i , j ; μ B E i , j , σ B E i ) · p s ( s i ) · p b ( b i , j ) · p σ ( σ B E i ) . ( 5 ) To estimate the model parameters of this Bayesian BE clock model we used MCMC with Gibbs sampling [33] . All the full conditionals are known distributions . Specifically , for each individual i , i = 1 , … , N , we estimated the posterior means , medians , and other quantiles of the BE onset time si , patient-specific , CpG-specific drift rates bi , j , j = 1 , … , 67 , and patient-specific standard deviation of measurements parameter , σBEi . All MCMC simulations were run for 100K cycles and allowing 1K cycles for burn-in . The Bayesian BE clock model requires specification of a prior distribution pb ( bi , j ) for the drift rates bj , j = 1 , … , 67 of the BE clock . In the preselection pipeline described above ( Step 1 ) , we obtained mean drift rates ( slopes ) and standard deviations for each arrayed CpG in the longitudinal study D1 . To illustrate the degree of variability and uncertainty in the estimated drift rates we show normal distributions with those means and standard deviations individually ( in Fig 5 , light dashed green curves ) and aggregated as a single normal distribution ( solid green curve ) . To validate the methylomic drift associated with these 67 BE clock CpGs in an independent longitudinal data set ( denoted as DV ) , we used the procedure described in Step 1 to evaluate the drift rates ( regression slopes ) for each of the 67 CpGs . The results are shown in Fig 5 , analogous normal distributions for each of the 67 CpGs in the clock set individually ( light dashed purple curves ) and in aggregate ( solid purple curve ) for the validation set DV . S3 Fig shows a scatterplot of mean drift rates between data sets D1 and DV . As expected , overall we observe slightly decreased means and increased variances in the drift rates of the clock CpGs in the validation set DV , a phenomenon commonly referred to as “winner’s curse” , reflecting the typical overestimation of effect sizes in discovery samples ( see Fig 5 ) . Ultimately , there was minimal effect of this bias conferred on posterior parameter estimates ( see S1 Text ) . In Results , we will apply the Bayesian BE clock model to estimate model parameters for 2 patient data sets independently—cross-sectional ( D2 ) and FBE ( D3 ) . To formally assess differences between different patient groups , we use Bayes factors to statistically test if the BE onset ages estimated for one group si , i = 1 , . . , Nk , lead to BE dwell times that are significantly different from those of a second patient set with estimated BE onset ages s i ′ , i = 1 , . . , Nl , for k , l ∈ {2 , 3} . For two specified data sets Dk , Dl , we compare the average fraction of life until age at biopsy ( ai ) during which the patient harbored BE . This quantity is given for two data sets by the following variables , γ k = 1 N k ∑ i = 1 N k a i - s i a i , γ l = 1 N l ∑ i = 1 N l a i ′ - s i ′ a i ′ . ( 6 ) Thus , we are interested in testing hypotheses H0: γk > γl versus H1: γk ≤ γl . For this test , we consider data y ∼ = { y 1 , … , y N k , y 1 ′ , … , y N l ′ } comprised of patient-specific observations of the form in Eq ( 2 ) and compute the Bayes factor B 01 = Pr [ y ∼ | H 0 ] Pr [ y ∼ | H 1 ] = Pr [ H 0 | y ∼ ] / Pr [ H 0 ] Pr [ H 1 | y ∼ ] / Pr [ H 1 ] = Pr [ H 0 | y ∼ ] / Pr [ H 0 ] ( 1 - Pr [ H 0 | y ∼ ] ) / ( 1 - Pr [ H 0 ] ) ( 7 ) to quantify the evidence in favor of the null hypothesis H0 and against the alternative H1 [34] . To compute Pr[H0|y∼] , we apply the ergodic theorem and approximate the posterior probability by the fraction of MCMC samples satisfying γk > γl . The prior Pr[H0] is computed similarly except we sample onset times si for the two groups of patients being compared directly from the uniform prior distributions si ∼ Uniform ( 0 , ai ) . The methods outlined in this section are implemented by the Bayesian BE clock model . All necessary tools to employ this model via the Gibbs sampler are available in documented R code at https://github . com/yosoykit/BE_Clock_Model .
First , we used the Bayesian BE clock model to obtain posterior estimates of parameters for data set D2 ( size N2 = 30 patients ) with the BE clock set of 67 CpGs . See Materials and Methods for modeling details and CpG selection . Fig 6 depicts the wide inter-individual variability in the predicted BE onset ages among the 30 patients in D2 , with interquartile and 95% credible intervals ( CIs ) denoted by box and whisker , respectively , for each Markov Chain Monte Carlo ( MCMC ) parameter estimate of BE onset age si , i = 1 , . . , N2 . For these 30 patients , median MCMC estimates for BE onset ages ranged from 2 . 0 to 59 . 0 years of age , with a median of 33 . 6 years of age . The model also estimates CpG specific drift rates bi , j , j = 1 , . . , 67 for the BE clock set and a measurement standard deviation parameter , σBEi for each individual i ( see Materials and Methods for details ) . The BE onset age estimates for the patients in D2 were obtained using prior pb ( bi , j ) derived from data set DV ( purple curve in Fig 5 ) . We provide MCMC results when using this prior because 1 ) the estimates of BE onset times si , i = 1 , … , N , using the DV prior are very similar to those when using the D1 prior , and 2 ) the DV prior introduces no bias ( i . e . , more realistic overall population drift distribution ) because it was not used for the BE clock CpG marker set selection . To quantify the aggregation of BE and EAC in families , Chak et al . performed a study with 411 patients with BE and/or its associated cancers , and found that familial BE ( FBE ) can be determined in 7 . 3% of patients , comprising 9 . 5% of EAC cases [22] . One hypothesis is that FBE patients have a stronger predisposition to develop BE compared to non-familial individuals , possibly due to inherited susceptability gene ( s ) . We estimated the Bayesian BE clock model parameters for the independent data set D3 ( size N3 = 22 patients ) with FBE , with age range 39–84 at time of biopsy ( mean age = 62 . 7 ) . Fig 7 depicts the posterior median BE onset ages estimated for the 22 patients in D3 , with interquartile and 95% credible intervals denoted by box and whisker , respectively . For these 22 patients , median MCMC estimates for BE onset ranged from 0 to 46 . 4 years of age , with a median of 26 . 1 years of age . The youngest FBE patient is shown to have onset at birth due to the incongruence of the standard clock drift rate distribution with his methylation values for the molecular clock set and thus we were unable to obtain positive posterior estimates of his onset age . Because a younger age of disease onset is often considered a surrogate marker for a genetic or environmental predisposition , we tested the hypothesis that the FBE patients of data set D3 had been living with their BE for longer than the general BE patients in data set D2 , which in our notation translates to H0: γ3 > γ2 ( see Materials and Methods for details ) . The Bayes factor ( see Eq ( 7 ) ) was conservatively estimated to be 100K . This result provides decisive support in favor of the hypothesis that the FBE patients indeed harbored BE ( relative to their ages when biopsies were removed for analysis ) longer than the general BE population harbored BE ( see left panel of Fig 8 for violin plot depicting this result ) . With the BE onset predictions provided in the previous results , we are in a position to associate a patient-specific risk of developing EAC before a certain age . We computed the cumulative risk of developing EAC for each patient before age 88 ( age of the oldest patient in our data sets ) by using tissue age biomarker data to inform the modeling of the neoplastic progression to EAC . Such an integrated perspective for cancer risk management has recently been suggested by Li and colleagues [35] . To this end , we employ a mathematical model for EAC progression , termed the multistage clonal expansion for EAC ( MSCE-EAC ) model , that was previously calibrated to EAC incidence in the US by birth cohort , to obtain EAC risk estimates for each patient assuming that all patients share similar risk factors ( e . g . , unknown dysplasia status at time of biopsy ) for EAC progression [8 , 16] . Specifically , for each BE patient who has not been diagnosed with EAC by age a , given estimated BE onset time TBE = s , we computed the following risk Pr [ T E A C < 88 | T B E = s , T E A C > a ] = S M S C E ( a - s ) - S M S C E ( 88 - s ) S M S C E ( a - s ) , ( 8 ) where SMSCE is the EAC survival probability for the multistage clonal expansion ( MSCE ) model after BE initiation ( see S1 Text for a derivation and S1 Fig for a model schematic ) [8 , 16 , 36] . Alternatively , we may use summary ( constant ) risk estimates of progressing from non-dysplastic BE to EAC using published annual risk estimates across individuals of different age and different BE onsets . Note , however , for general s < a our mathematical EAC model implies the following inequality , Pr [ T E A C < 88 | T B E = s , T E A C > a ] ≠ Pr [ T E A C < 88 | T B E < a < T E A C ] , ( 9 ) which demonstrates that a patient’s BE onset adds information to refine blanket risk stratifications that do not consider this information . As a demonstration , we used this model to compute the patient-specific risk of developing EAC by age 88 assuming a standardized 1950 birth cohort , allowing for gender-specific model parameters , by inputting the BE onset age estimate s for each patient into Eq ( 8 ) . See S1 Table for the MCMC BE onset median estimates ( with 95% credible intervals ) of the 2 BE data set groups . Fig 8 shows the distributions of median MCMC estimated BE onsets for the 2 patient data sets ( green violin plots ) and their age-at-biopsy distributions ( grey boxplots ) , alongside the corresponding EAC risk estimates for these onset ages . Of the two patient groups , the FBE patients in data set D3 have a significantly higher predicted median EAC risk estimate of 0 . 47 compared to the sporadic BE population with a median risk of 0 . 11 . Because EAC risk is predicted by our model to increase monotonically with BE dwell time for patients of the same age , the correlation between estimated BE onset age and predicted EAC risk by age 88 is very high across patients ( corr = . 92 for data set D2 , corr = . 97 for data set D3 , see S5 Fig ) .
A fundamental problem in predicting the risk of esophageal adenocarcinoma ( EAC ) in patients with BE continues to be the difficulty in assessing the neoplastic potential of BE , which is considered the premalignant field in which EAC arises . Several lines of evidence and theoretical considerations support the notion that both BE segment length and the duration of how long BE has been present in a patient ( i . e . , BE dwell time ) are important determinants of EAC risk in addition to environmental and genetic risk factors [16 , 37 , 38] . While endoscopic surveillance with systematic biopsy sampling is the standard clinical care to screen BE patients for dysplasia and early cancer , most BE patients never develop esophageal cancer in their lifetimes . Priority has therefore been given to novel approaches to identify the molecular signatures of EAC progression and biomarkers in an attempt to more precisely define EAC risk at an individual level . However , because chronological age is recognized as one of the strongest predictors of cancer risk , renewed attention has been given to exploring the roles of biological tissue-age and cellular senescence in the progression to cancer [39] . Unfortunately , a clinical determination of when a patient first developed BE is presently not possible because BE is mainly asymptomatic ( over 90% of EAC cases do not present with a prior history of BE [40] ) . For this reason we made an attempt to validate our BE onset predictions indirectly through two lines of evidence . First , we validated the longitudinal drift rates with an independent data set ( DV ) . Although the drift rates for the BE clock set were generally lower in the validation set DV compared with the rates seen in set D1 ( which we attribute to selection bias in D1 ) , we found very similar estimates of the BE onsets using either drift-rate prior distribution in our Bayesian model . Secondly , we considered previous efforts to identify tissue-based indicators that accurately reflect the biological age of a tissue using regularized regression techniques by directly regressing age on the levels of methylation at a large number of CpGs to identify subsets of CpGs that are predictors of chronological age [13 , 14] . Although we cannot use these techniques in this context because the BE onset times are unknown , we find that our predictions are at least broadly consistent with the straightforward application of these clock models to estimate absolute tissue-age differences between BE and SQ tissue . Specifically , using the published elastic net coefficients by Horvath [14] and by Hannum et al . [13] we computed the predicted biological age of the BE tissue and subtracted the predicted biological age of the normal squamous ( SQ ) esophageal tissue to arrive at crude estimates of the BE dwell time for the 30 patients in D2 ( the cross-sectional cohort of patients ) . By subtracting these estimates from the chronological ages of the patients we obtained corresponding BE onset times that correlated well with our predictions ( r = 0 . 77 for the Horvath 110 clock-CpG model , r = 0 . 84 for the 89 clock-CpG model by Hannum et al . ) . Finally , we tested our clock model using methylation array data from 22 familial BE patients ( set D3 ) . Patients from both groups D2 and D3 have similar age distribution ( see Fig 8 and S1 Table ) . However , compared to the onset ages estimated for the patients in data set D2 , the familial group show increased BE dwell times; Bayes factor testing for the FBE study suggests that the inferred BE onset times , although heterogeneous ( Fig 8 ) , tend to occur significantly earlier in life for FBE patients compared to nonfamilial BE cases implying a possible heritable predisposition to develop BE metaplasia . Given that the predictions of BE onsets among FBE cases are significantly earlier than the predictions for the sporadic cases , it is perhaps surprising that the age distribution for the familial cases is not dissimilar to the age distribution for the sporadic cases ( see grey boxplots in Fig 8 ) . One possible explanation is that , next to symptomatic reflux , heartburn and other common risk factors , family history may not have been an indicator for referral to endoscopy as familiarity of this disease was only discovered in the past couple decades [22] . Therefore , if reflux frequency and other indicators for referral are similar for familial and non-familial patients , we expect the mean ages of BE diagnosis to be similar between the two groups . Specifically , we found the median estimates of BE onset age for the FBE patients to be 7 . 4 years earlier on average than the sporadic BE cases in study D2 . This result is consistent with the result of a large study by Chak et al . that concluded that multiplex FBE families ( multiplex being defined as having at least 2 confirmed FBE cases among family members ) develop EAC at an earlier age compared with nonfamilial EAC cases [38] . Similar to the conclusions drawn by these authors , our result suggests that FBE patients may need earlier and possibly more frequent endoscopic screening for neoplastic lesions in BE tissue before EAC develops . Given the theoretical implications of our proposed model of BE initiation and progression to EAC , we propose that once a patient’s BE onset has been estimated from his/her methylomic drift profile , his/her risk of developing EAC can be estimated more precisely . We have used a previously validated multistage clonal expansion model for EAC incidence which explicitly considers the uncertainty of the timing of BE onset in the general population and describes , conditional on when BE develops , the stochastic process of neoplastic progression from metaplastic to dysplastic tissue to cancer [8 , 16] . These theoretical predictions show a strong dependence of EAC risk on the BE dwell time ( see Fig 8 ) . Importantly , we found that the lifetime risks for the individuals in study D2 vary widely , with an interquartile range of 0 . 01 to 0 . 44 . It is important to recognize that these EAC risk predictions do not consider the effects of interventions and therefore may be overestimates . Although this predicted variability in risk stands unconfirmed , our median risk prediction of 0 . 11 for the D2 patients ( see Fig 8 ) is consistent with empirical estimates of the EAC lifetime risk in BE patients found in the range 0 . 07–0 . 13 [41] . Therefore , the finding that the lifetime risks for the individuals in study D2 vary widely with an interquartile range of 0 . 01 to 0 . 44 translates into relative EAC risks ( for the 4th quartile relative to 1st quartile ) of > 40 , assuming an otherwise homogenous population . For comparison , we found positive correlations between our D2 EAC risk predictions based on BE onset and D2 EAC risk estimates using previously reported risk factors based on gender ( corr = 0 . 57 , p = . 001 ) , histopathological grade ( corr = 0 . 53 , p = . 003 ) , and chronological age ( corr = 0 . 49 , p = . 006 ) [6] . However , each of those risk factor estimates led to much lower relative EAC risks of <3 . This suggests that BE onset , as determined by methylomic drift , can be considered a potential biomarker for EAC risk , although further validation via properly powered prospective studies or case-control studies in BE patients are needed to confirm this . Such studies may provide the requisite data to further test how well BE tissue-age performs in identifying individuals that likely progress to HGD or EAC in their lifetime so that endoscopic surveillance and available interventions can be utilized more effectively . | Barrett’s Esophagus ( BE ) is a metaplastic precursor to esophageal adenocarcinoma ( EAC ) . When a patient is diagnosed with BE , it is generally not known how long he/she has had this condition because BE is asymptomatic . While the question of how long a premalignant tissue or lesion has been resident in an organ ( dwell time ) may not be of importance for cases where curative interventions are readily available ( such as adenomas in the colon ) , for BE , curative interventions are either costly or carry patient risks . Knowledge of a precursor’s dwell time may therefore be advantageous in determining the cancer risk due to the stepwise accumulation of critical mutations in the precursor . In this study , we create a molecular clock model that infers patient-specific BE onsets from DNA methylation data . We show that there is considerable variation in the predicted BE onset times which translates , using mathematical modeling of EAC , into large variation in individual EAC risks . We make the case that , notwithstanding other known risk factors such as chronological age , gender , reflux status , etc . , knowledge of biological tissue age can provide valuable patient-specific risk information when a patient is first diagnosed with BE . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"biopsy",
"carcinomas",
"cancers",
"and",
"neoplasms",
"surgical",
"and",
"invasive",
"medical",
"procedures",
"health",
"care",
"oncology",
"systems",
"science",
"mathematics",
"endoscopy",
"probability",
"distribution",
"gastroenterology",
"and",
"hepatology",
"adenocarcinomas",
"epigenetics",
"dna",
"dna",
"methylation",
"chromatin",
"computer",
"and",
"information",
"sciences",
"chromosome",
"biology",
"treatment",
"guidelines",
"gene",
"expression",
"chromatin",
"modification",
"dna",
"modification",
"probability",
"theory",
"biochemistry",
"health",
"care",
"policy",
"barrett's",
"esophagus",
"cell",
"biology",
"nucleic",
"acids",
"normal",
"distribution",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"dwell",
"time"
] | 2016 | A Molecular Clock Infers Heterogeneous Tissue Age Among Patients with Barrett’s Esophagus |
Advances in the computational identification of functional noncoding polymorphisms will aid in cataloging novel determinants of health and identifying genetic variants that explain human evolution . To date , however , the development and evaluation of such techniques has been limited by the availability of known regulatory polymorphisms . We have attempted to address this by assembling , from the literature , a computationally tractable set of regulatory polymorphisms within the ORegAnno database ( http://www . oreganno . org ) . We have further used 104 regulatory single-nucleotide polymorphisms from this set and 951 polymorphisms of unknown function , from 2-kb and 152-bp noncoding upstream regions of genes , to investigate the discriminatory potential of 23 properties related to gene regulation and population genetics . Among the most important properties detected in this region are distance to transcription start site , local repetitive content , sequence conservation , minor and derived allele frequencies , and presence of a CpG island . We further used the entire set of properties to evaluate their collective performance in detecting regulatory polymorphisms . Using a 10-fold cross-validation approach , we were able to achieve a sensitivity and specificity of 0 . 82 and 0 . 71 , respectively , and we show that this performance is strongly influenced by the distance to the transcription start site .
Our ability to identify the molecular mechanisms responsible for specific genetic traits within our population will be enhanced by our imminent ability to decipher each individual's genome . This is evident from recent advances in sequencing and genotyping technologies , which allow an increasing number of variants to be sampled for association and linkage ( reviewed in [1–3] ) and contribute a growing number of sources of variation and their frequencies to public databases each year . As new variants are identified , each becomes a molecular window into our past , present , and future—each aids in tracing our genetic heritage and in charting the footsteps of our common evolution , and possesses the potential to predict disease or drug susceptibilities , ideally acting as an early-warning system in preventative medical practice ( reviewed in [4 , 5] ) . However , our ability to catalog genotypes has far outstripped our ability to implicate them in phenotypes . Currently , more than 6 million unique single-nucleotide polymorphisms ( SNPs ) are included in version 126 of dbSNP [6]; of these SNPs , only a very small fraction have been associated with a phenotype using genetic association or linkage analysis . This is because association studies are costly , time-consuming , and dependent on the frequency of the genotype in the sampled population . Furthermore , many SNPs are not necessarily expected to have a function . To select candidates for functional validation , computational methods have been developed to identify SNPs that alter the protein-coding structure of genes [7–16] . These types of computational methods tend to prioritize putative functional SNPs by identifying those SNPs that alter a protein's amino acid sequence , are located within well-conserved regions or functional protein domains , and alter the biochemical structure of the protein . However , very few methods identify regulatory SNPs ( rSNPs ) that alter the expression of genes . Such rSNPs have been implicated in the etiology of several human diseases , including cancer [17 , 18] , depression [19] , systemic lupus erythematosus [20] , perinatal HIV-1 transmission [21] , and response to type 1 interferons [22] . This work aims to extend computer-based techniques to identify this particular class of functional variants within the core promoter regions of human genes . Conventional computational approaches to rSNP classification have predominantly relied on allele-specific differences in the scoring of transcription factor weight matrices as supplied from databases such as TRANSFAC and Jaspar [15 , 16 , 23] . SNPs located within matrix positions possessing high information content are assumed more likely to be functional . Support for this hypothesis to date , however , has been restricted to single-case examples . Furthermore , a recent study has failed to detect significant weight matrix signals in 65% of regulatory polymorphisms ( n = 40 ) [24] . However , the prevailing hypothesis in computational regulatory element prediction has been that the majority of predictions using unrestricted application of matrix-based approaches are false positives . By extending this technique and using phylogenetic footprinting between mouse and human , it was demonstrated that from ten SNPs that show significant allele-specific differences in Jaspar predictions , seven also demonstrated electrophoretic mobility shift differences [23] . However , only two of the seven had a marked effect in reporter gene assays . Conservation alone has also been demonstrated as a poor discriminant of function in a study of regulatory polymorphisms in Eukaryotic Promoter Database promoters , where zero of ten experimentally validated regulatory variants were in conserved binding sites [25] . A substantial challenge with developing strategies for identifying functional noncoding variants has been the shortage of characterized regulatory variants . Few studies have successfully identified the causative variant ( s ) after a susceptibility haplotype is identified . To address this problem , we have assembled the largest openly available collection of functional regulatory polymorphisms within the ORegAnno database ( http://www . oreganno . org ) [26] . From this dataset , we have examined several features of these SNPs as they relate to polymorphisms of unknown function ( ufSNPs ) within the promoter regions of associated genes ( up to 2 kb ) . Our hypothesis is that using a combination of regulatory and population genetics properties , the discriminative efficacy of individual properties can be evaluated , and significant predictors of rSNP function can be chosen . Within our assayed set , we have found that the best discriminants are the distance to the transcription start site ( TSS ) , local repetitive density and content , sequence conservation , minor allele frequency ( MAF ) and derived allele frequency , and CpG island presence . Notably , the unrestricted application of a matrix-based approach is demonstrated to be one of the least effective classifiers . We have used this dataset of rSNPs and their properties to train a support vector machine ( SVM ) classifier . Two approaches were used to train the classifier: one in which the properties of all rSNPs were compared with that of all the ufSNPs , and one in which each property value of the positive SNPs and ufSNPs within an associated gene were compared with the average values for each property within that gene ( referred to here as the “All” and “Group” approaches , respectively ) . The All approach is designed to determine if there are any properties that are important across the test set , while the Group approach is designed to determine if there are important directional shifts in values within a promoter that may discriminate functional SNPs from ufSNPs . In a 10-fold cross-validated test , the SVM achieves a receiver operating characteristic ( ROC ) value of 0 . 83 ± 0 . 05 for the All analysis ( sensitivity , 0 . 82 ± 0 . 08; specificity , 0 . 71 ± 0 . 13 ) and 0 . 78 ± 0 . 04 for the Group analysis ( sensitivity , 0 . 72 ± 0 . 19; specificity , 0 . 68 ± 0 . 07 ) .
Literature describing noncoding polymorphisms responsible for allele-specific differences in gene expression was surveyed from PubMed [27] . From this literature , 160 regulatory polymorphisms were identified in 103 publications; each was selected based on experimental evidence that confirmed its direct role in altering gene expression . This selection criterion specifically excluded those polymorphisms in which the experimental evidence could only confirm that the reported polymorphism was in linkage disequilibrium with an rSNP . Each identified rSNP was manually curated in the ORegAnno database . Subsequently , 104 polymorphisms were selected based on the criteria that they were SNPs ( excluding seven insertion–deletion polymorphisms ) , and were within 2 kb of the TSS of their associated gene ( as annotated in version 37 of EnsEMBL [28]; Table 1 ) . A 2-kb region was chosen to maximize the number of rSNPs included while minimizing the size of sequence investigated; at 2 kb , the addition of a single further rSNP would increase the surveyed region by 43% , whereas the previous addition resulted in an increase of 9% . At this window size , 39 rSNPs were excluded from analysis . An additional ten polymorphisms were excluded because of deprecated annotation of the gene or discordant genomic location with the associated gene . In total , the remaining 104-rSNP set contained polymorphisms involved in altering the expression of 78 different transcripts . Using each of the 78 transcripts , SNPs within 2 kb of the TSS were extracted from version 37 of EnsEMBL ( dbSNP version 125 ) , producing exactly 951 ufSNPs . The ufSNP and rSNP genomic locations have been mapped ( see Table S1 ) . A total of 23 different properties of relevance to assessing regulatory function were calculated for each SNP in both the 104-rSNP and ufSNP sets ( Table 1 ) . These properties were selected to represent a cross-section of well-documented methodologies for assessing the functional significance of both allele-specific changes and DNA sequences within noncoding regions . Two types of analyses were conducted using the investigated properties . One was an all-versus-all approach , where the 104-rSNP and ufSNP sets were compared en masse . The other was a group analysis , where the average value of each property within each upstream noncoding region was first calculated , and then the individual SNP properties within that region were recalculated as the difference from this average . The All test data were designed to identify global characteristics of rSNPs , while the Group test data were designed to look for directional trends within the sampled region that might be indicative of SNP importance . For example , the All test is able to ask whether rSNPs have generic features that would distinguish them from any other promoter SNP; the Group test is designed to identify whether there are any features that distinguish rSNPs from other SNPs within the same upstream noncoding region . The All and Group test data were input to the Gist SVM implementation [29] . We excluded the logarithmic distance to the TSS to prevent redundant classification with the raw distance to the TSS . Gist was run using the default parameters as described previously [30] . Of note , the Gist SVM requires that every value in the test and training parameter space is filled . To reflect the null hypothesis , that there are no differences between the ufSNPs and rSNPs , the All SVM was filled with promoter-specific average values wherever data could not be calculated . Likewise , the Group SVM was filled with zero values wherever data could not be calculated , indicating no divergence from average within the GROUP test set . The individual importance of each property in discriminating regulatory polymorphisms was assessed in the All and Group test sets using a Wilcoxon rank sum test . Each value was corrected for multiple testing using the BioConductor MTP package ( http://www . bioconductor . org ) by controlling for the family-wise error rate ( α = 0 . 05 and B = 10 , 000 ) [31 , 32] . The performance of the Gist SVM classifier was measured using a ROC curve . ROC scores of 1 indicate perfect discrimination , while those at 0 . 5 indicate random classification of the input SNPs . ROC performance measurements have been previously described in detail elsewhere [30] . A 10-fold cross-validation was performed to assess the overall performance of the SVM . The input data was randomly partitioned by transcript into ten sets . Data from one set were excluded , and the remaining nine sets were trained on for each fold validation . This analysis was performed for each set to cover the entire training site and to calculate an average ROC value for the SVM . We were concerned that several properties may be indirect measurements of distance from the TSS , and that any discrimination strategy would be limited to characterizing this property alone . This concern is a particular challenge since distance ascertainment bias exists; most SNPs surveyed were within a few hundred base pairs of the TSS , which is much smaller when compared with our sampling distance of 2 kb . Furthermore , it has been well established in a previous study that distance to the TSS is correlated to detection of rSNPs ( it is unknown if this is because they are more likely to affect essential transcription factor–binding sites , or because there is a higher density of transcription factor–binding sites in these regions ) [24] . For this reason , the discrimination potential of distance to the TSS could not be ignored . To adjust for bias , however , we calculated the expectancy of observing a feature at a particular distance from the TSS for each individual chromosome ( Figure 1; CpG islands are shown as an example of this trend ) . This expectancy value was used to normalize the observation values for several of the properties in this study ( identified in Table 1 ) . This was performed by subtracting the expectancy value from the observed value . The impact of this normalization is negligible when comparing normalized ROC values against unnormalized ROC values; using a 10-fold cross-validation , the unnormalized ROC values for the ALL test are 0 . 82 ± 0 . 05 ( unnormalized ) and 0 . 83 ± 0 . 05 ( normalized ) , and values for the GROUP test are 0 . 79 ± 0 . 04 ( unnormalized ) compared with 0 . 78 ± 0 . 07 ( normalized ) .
A total of 104 rSNPs and 951 ufSNPs in the upstream noncoding regions of 78 genes were compiled to test properties that discriminate polymorphisms with effects on gene expression . A multiple testing–corrected Wilcoxon rank sum test was used to analyze the All test data ( Table 2 ) . Analyzing the All test data identified several properties of significance in discriminating between rSNPs and ufSNPs ( p < 0 . 05 ) . The properties of significance in the All test data , in order of importance , were: 1 ) distance to the TSS ( properties 13 and 14 ) ; 2 ) in a CpG island ( property 19 ) ; 3 ) long repeat events ( property 16 ) ; 4 ) local repetitive base percentage ( property 13 ) ; 5 ) derived allele frequency ( property 12 ) ; 6 ) minor allele frequency ( MAF; property 11 ) ; 7 ) Regulatory Potential score ( property 22 ) ; 8 ) in a repeat ( property 14 ) ; and 9 ) ClustalW alignment distance ( property 23 ) . However , a concern with the All analysis was that calculated property values for SNPs in individual upstream noncoding regions would not be comparable with those in other upstream noncoding regions due to differences in background property values . To address this , a multiple testing–corrected Wilcoxon rank sum test was also used to analyze the Group test data ( Table 2 ) . The properties of significance ( p < 0 . 05 ) in the Group test data , in order of importance , were: 1 ) distance to the TSS ( properties 13 and 14 ) ; 2 ) long repeat events ( property 16 ) ; 3 ) in a CpG island ( property 19 ) ; 4 ) MAF ( property 11 ) ; 5 ) local repetitive base percentage ( property 13 ) ; 6 ) ClustalW alignment distance ( property 23 ) ; 7 ) derived allele frequency ( property 12 ) ; 8 ) short repeat events ( property 15 ) ; and 9 ) DNaseI hypersensitive site ( property 20 ) . Both lists are highly concordant and demonstrate several properties that may be of utility when prioritizing SNPs for functional analysis either across the genome or within an individual upstream noncoding region . In both tests , distance to the TSS was found to be the most significant discriminant . While it is possible that ascertainment bias in the 104-rSNP set contributes to the strength of this discriminant in our study , this property has also been independently identified as an important discriminant in a previous study where , in 500-bp assayed regions , 50% of rSNPs identified through transfection experiments were within 100 bp of the TSS ( n = 40 ) [24] . Furthermore , several other properties are consistently identified as being significant after normalization against distance to TSS . One property , ClustalW alignment distance , was identified in both the All and Group tests as being significant . The mean value of ClustalW alignment distance was slightly higher for the tested rSNPs compared with the ufSNPs , indicating that 1-kb multiple alignments centered on the tested rSNPs were more divergent than those centered on ufSNPs . This result is concordant with previous analyses of conservation around rSNPs ( n = 10 ) [25] . However , trends in the other conservation scores used in this study , while nonsignificant in discriminating between the tested rSNP and ufSNPs , conversely suggest that the tested rSNPs are more conserved than ufSNPs . Since these metrics use tighter window sizes than those used for calculating the ClustalW alignment distance , this result suggests that increased mutation around an rSNP may be more informative than the conservation status of the rSNP itself . Another property of significance was repetitive element content . Our results indicate that the tested rSNPs were less likely to be in or around repetitive elements . This suggests that regions that are likely to contain a transcription factor–binding site are less likely to accrue repetitive elements and be subject to dysregulation . We note , however , that ascertainment bias by which the 104-rSNPs set was surveyed in terms of repetitive elements is not known , and future collections of discovered rSNPs should address this issue . Both MAF and derived allele frequency are also identified as significant discriminants . Unexpectedly , for genotyped SNPs , the MAF was higher in the 104-rSNP set than in the ufSNP set . Previous analyses of MAF have suggested that most functional SNPs are positioned around 6% [33] or possess no allele frequency bias [24] . In this study , the average MAF was approximately 22% . Since a subset of the 104-rSNP set has been derived from association studies , it is possible that ascertainment bias may explain part of this result as researchers may preferentially be choosing higher MAF SNPs because of their greater statistical power . Of further interest , the derived allele frequency was higher in the 104-rSNP set than in the ufSNP set . This could suggest that many of the derived alleles have been driven to higher frequencies due to new variants increasing in frequency in our population , through either population bottlenecks or positive selection . The former hypothesis is supported by the supplemental observation that when restricting populations to HapMap ( http://www . hapmap . org ) phase I populations only , the Asian and European populations mirror this result , while the African population has lower MAFs on average . The latter hypothesis , however , supports a model of evolution of genetic susceptibility to common diseases explained by ancient alleles recently becoming predisposed to disease due to changes in human lifestyle and life expectancy [34] . Another interesting result was that SNPs in the 104-rSNP set were less likely to be in CpG islands than were ufSNPs . Since CpG expectancy was normalized from average values at specific distances from the TSS of associated genes across individual chromosomes , an admixture of CpG and CpG-less promoters would drive the 104-rSNP set values lower than the ufSNP set values ( Figure 1 ) [35 , 36] . However , without normalization , the significance of this value for the All and Group tests is similar ( All , p = 3 . 78 × 10−5; Group , p = 1 . 96 × 10−3 ) , suggesting that the rSNPs are in fact less likely to be in CpG islands . Many tested properties fell below our significance threshold in these tests . Of interest , both weight matrix–based approaches did not discriminate well . In addition , our definition of coexpression was significantly broad as to allow multiple coexpressed partners for any given gene; this may have reduced the overall effectiveness of reducing transcription factor–binding profiles using this information . However , the performance of the coexpression-filtered approach was moderately better than the TRANSFAC approach alone . This suggests that targeted analysis of specific , biologically relevant transcription factors may further increase the discriminating ability of this approach . This should also act as a warning to those who have in the past applied the TRANSFAC approach to this problem indiscriminately . Furthermore , none of the DNA structural or stability analyses used were successfully discriminatory . This analysis could indicate that not only do these features have nongeneralizable effects using the data in this study , but since these analyses also measure local sequence composition , no particularly important effect is caused by specific base changes . To evaluate whether the combination of the tested properties would enhance discrimination of rSNPs from ufSNPs , we trained a SVM for the ALL and GROUP test data . We tested the classification performance of SVMs by 10-fold cross-validation . For each SVM , the mean area under the ROC curve was 0 . 83 ± 0 . 05 and 0 . 78 ± 0 . 04 , respectively . Both suggest good performance . It is significant , however , that when removing distance from the classification , the performance of each test drops to 0 . 52 ± 0 . 09 and 0 . 48 ± 0 . 07 , respectively ( Figure 2 ) . This reduction in performance should not be taken to indicate that other properties identified in the multiple testing–corrected Wilcoxon rank sum test are not actually discriminatory since 10-fold cross-validation of All and Group test SVMs built with only the properties identified as significant using the multiple testing–corrected Wilcoxon rank sum test ( p < 0 . 05 ) and excluding distance to the TSS achieved ROC values of 0 . 77 ± 0 . 08 and 0 . 75 ± 0 . 07 , respectively . This result suggests that nonsignificant results may act to overparameterize the SVM model and mask subtle , true discriminatory signals . To address the issue of distance bias further , we fortuitously identified that , across our dataset , in the 152 bp immediately upstream of the TSS , the average distance to the TSS for the ufSNPs was identical to that of the rSNPs . This 152-bp window therefore represented a region with no observable distance biases , albeit a greatly reduced subset in size; at this window size , only 16 rSNPs and 21 ufSNPs were available for analysis . When analyzed using a multiple testing–corrected Wilcoxon rank sum test for both All and Group test sets , only two properties were significant ( p < 0 . 05 ) : repetitive element density ( property 13 ) and ClustalW alignment distance ( property 23 ) ( Table 2 ) . We further tested window sizes of 500 bp , 1 kb , and 1 . 5 kb and noticed only a gradual reduction in performance of the tested properties for smaller window sizes ( see Table S2 ) . We also examined the position of identified rSNPs to characterize possible bias . Our expectation was that well-established transcription factor–binding sites such as the TATA and CCAAT boxes may be overrepresented and contribute to lower distance values . A histogram of rSNPs for the first 300 bp of sequence from the TSS shows an expected increase around the 21–31 position where seven rSNPs are located , twice as many as average . However , it is apparent that these types of binding sites are only overrepresented slightly when compared with the distribution of rSNPs at other positions ( Figure 3 ) . All pipeline software has been programmed in Perl and is available under the Lesser GNU Public Licence at http://www . bcgsc . ca/chum under the name CHuM ( cis-acting human mutation modules ) . All data are available from this site .
This study introduces the largest publicly available collection of rSNPs—160 known rSNPs from literature . Using this collection , we investigate 104 rSNPs and 951 ufSNPs in human 2-kb upstream regions to identify properties that may discriminate functional from nonfunctional polymorphisms . We identify several properties that may be useful to researchers attempting to determine the functional status of upstream noncoding SNPs . The most important properties detected suggest that rSNPs are close to the TSS , are not within CpG islands , are isolated from repetitive elements , possess higher MAF and higher derived allele frequency , and are within comparatively more divergent regions . However , within a 152-bp window , where an equal distribution of rSNPs and ufSNPs from the TSS is obtained , the significant results suggest that only repetitive element content and local divergence remain important ( we have included in Table S2 information on how property significance changes with window size ) . We further combined each of the properties identified in the 2-kb region to train an SVM to classify the functional status of the 104-rSNP set and 951-ufSNP set . We hypothesized that subtle differences in individual properties may be more important than any one property in detecting rSNPs . It is of note , despite mentioned ascertainment biases , that our sensitivity and specificity for the All test was 0 . 82 ± 0 . 08 and 0 . 71 ± 0 . 13 , respectively , and for the Group test was 0 . 72 ± 0 . 19 and 0 . 68 ± 0 . 07 , respectively . Also of note , the strength of the distance to the TSS as a discriminatory property was demonstrated in both tests when removal of the property significantly reduced the effectiveness of the classifier to near random performance . However , we observed that this reduction in performance was recovered in part when only the properties identified as significant through the multiple testing–corrected Wilcoxon rank sum test ( p < 0 . 05 ) in the 2-kb All and Group tests were applied , and the distance to the TSS was excluded . Through this work , several challenges are apparent with current predictive approaches to prioritize candidate rSNPs . Necessary to future analyses is a dataset of core promoter polymorphisms that are nonfunctional across a broad range of cell types; since our negative control set was a neutral set , it is assured that more accurate performance metrics can come from addition of a reliable negative control set . Furthermore , recent analysis of allelic expression difference has demonstrated that the effects of rSNPs may be highly context-specific such that function in one cell line may not imply function in others; to address this complication , future analysis may require expanded collections of cell line–specific positive and negative rSNPs [37] . Future studies of promoter polymorphisms will also need to take advantage of known transcription factor–binding sites . Such information will be invaluable in dissecting the causal nature of many of the properties . In summary , this study introduces a new dataset for the investigation of rSNPs . We have also introduced one of the first gene regulation and population genetics–based approaches to classifying rSNPs in the core promoter regions of human genes . We identify the utility of different gene regulation and population genetics properties in discriminating literature-curated rSNPs . Such results are increasingly essential to researchers seeking criteria for prioritizing SNPs to test in association , binding , or expression assays . Furthermore , we provided evidence that popular methodological practices based on identification of allele-specific differences in position weight matrices through unrestricted application of the TRANSFAC database are poor criteria for SNP selection . However , we highlight the fact that because of the lack of extensive unbiased collections of rSNPs , it still remains challenging to dissect the existing effects of investigator or methodological biases in evaluating the importance of these properties . We hope that this work will stimulate active discussion and both the development of expanded collections of rSNPs and an improved class of bioinformatics tools for rSNP analysis that address these challenges . | Computational techniques are used in biology to prioritize DNA sequence variants ( or polymorphisms ) that may be responsible for population diversity and the manifestation of species-specific traits . Predominantly , they have been used to predict the class of polymorphisms that alter protein function through allele-specific changes to amino acid composition . However , polymorphisms that alter gene expression have been increasingly implicated in manifestation of similar traits . Prioritization of these polymorphisms is challenged , though , by the lack of knowledge regarding the mechanisms of gene regulation and the paucity of characterized regulatory polymorphisms . Our work attempts to address this issue by assembling a collection of regulatory polymorphisms from the existing literature . Furthermore , we use this collection to investigate and prioritize various properties that may be important for identifying novel regulatory polymorphisms . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"evolutionary",
"biology",
"homo",
"(human)",
"genetics",
"and",
"genomics",
"computational",
"biology"
] | 2007 | A Survey of Genomic Properties for the Detection of Regulatory Polymorphisms |
Replication of plus-stranded RNA viruses is greatly affected by numerous host-coded proteins acting either as susceptibility or resistance factors . Previous genome-wide screens and global proteomics approaches with Tomato bushy stunt tombusvirus ( TBSV ) in a yeast model host revealed the involvement of cyclophilins , which are a large family of host prolyl isomerases , in TBSV replication . In this paper , we identified those members of the large cyclophilin family that interacted with the viral replication proteins and inhibited TBSV replication . Further characterization of the most effective cyclophilin , the Cyp40-like Cpr7p , revealed that it strongly inhibits many steps during TBSV replication in a cell-free replication assay . These steps include viral RNA recruitment inhibited via binding of Cpr7p to the RNA-binding region of the viral replication protein; the assembly of the viral replicase complex and viral RNA synthesis . Since the TPR ( tetratricopeptide repeats ) domain , but not the catalytic domain of Cpr7p is needed for the inhibitory effect on TBSV replication , it seems that the chaperone activity of Cpr7p provides the negative regulatory function . We also show that three Cyp40-like proteins from plants can inhibit TBSV replication in vitro and Cpr7p is also effective against Nodamura virus , an insect pathogen . Overall , the current work revealed a role for Cyp40-like proteins and their TPR domains as regulators of RNA virus replication .
Replication of plus-stranded ( + ) RNA viruses takes place in membrane-bound viral replicase complexes ( VRCs ) in the cytoplasm of infected cells . ( + ) RNA viruses usurp a number of host-coded proteins to aid the replication process [1]–[8] . Many host proteins , however , have antiviral activities by inhibiting various steps of viral replication and infection . Accordingly , genome-wide screens to identify host factors affecting ( + ) RNA virus infections , such as Tomato bushy stunt virus ( TBSV ) , West Nile virus , Brome mosaic virus ( BMV ) , Hepatitis C virus ( HCV ) , Dengue virus and Droshophila virus C in yeast or animal cells led to the identification of stimulatory as well as inhibitory host proteins [9]–[17] . The functions of the majority of the identified host proteins in ( + ) RNA virus replication have not been fully revealed . TBSV is a small ( + ) RNA virus that has recently emerged as a model virus to study virus replication , recombination , and virus - host interactions due to the development of yeast ( Saccharomyces cerevisiae ) as a model host [18]–[21] . Genome-wide screens of yeast genes and global proteomics approaches have led to the identification of over 300 host genes/proteins that affected either TBSV replication or recombination [9] , [11] , [22] , [23] . Also , proteomics analysis of the highly purified tombusvirus replicase complex revealed the presence of the two viral replication proteins ( i . e . , p33 and p92pol ) and 6–10 host proteins in VRC [24] , [25] , [26] . These host proteins have been shown to bind to the viral RNA and the viral replication proteins [1] , [25] , [27] . For example , heat shock protein 70 ( Hsp70 ) , eukaryotic elongation factor 1A ( eEF1A ) and the ESCRT ( endosomal sorting complexes required for transport ) family of host proteins are required for the assembly of VRC , while glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) and eEF1A have been shown to affect viral RNA synthesis [27]–[31] . The auxiliary p33 replication protein has been shown to recruit the TBSV ( + ) RNA to the site of replication , which is the cytosolic surface of peroxisomal membranes [32]–[34] . The RdRp protein p92pol binds to the essential p33 replication protein that is required for assembling the functional VRC [20] , [34]–[36] . In contrast , the roles of identified host proteins with inhibitory functions in TBSV replication are less characterized . Nucleolin , an RNA binding protein , has been shown to interfere with the recruitment of the viral RNA into replication [37] , while Rsp5p , a Nedd4 family of E3 ubiquitin ligase , regulates the degradation of p92pol in yeast cells and the activity of VRC in vitro [38] . In addition , Cpr1p cyclophilin and Ess1p parvulin also decrease TBSV RNA accumulation in yeast , but the mechanism is not yet unraveled [39] . Cyclophilin family of proteins are ubiquitous , highly conserved proteins with prolyl isomerase ( PPIase ) activity . Cyclophilins and the structurally unrelated FKB proteins ( FK506-binding proteins ) and parvulins constitute a family of 13 prolyl isomerases in yeast . These proteins catalyze cis-trans isomerization of the peptidyl-prolyl bonds that could alter the structure , function or localization of the client proteins [40]–[41] . The isomerization of the peptidyl-prolyl bonds are frequently required for protein refolding after trafficking through cellular membranes [41] . The best-known member of prolyl isomerases in eukaryotes is cyclophilin A ( CypA in mammals and Cpr1p in yeast ) . The most important functions of cyclophilins in cells are in protein folding , assembly of multidomain proteins , muscle differentiation , detoxification of reactive oxygen species , and immune response . Cyclophilins have been implicated in various diseases , such as cancer , atherosclerosis , diabetes and neurodegenerative diseases [40] , [42] , [43] . Because of their roles in immunosuppression , cyclophilins and FKBs are also called immunophilins . To determine the mechanism of cyclophilin-based inhibition of tombusvirus replication , in this work , we first identified those cyclophilins , which interacted with the TBSV p33 replication protein , followed by testing the effect of cyclophilins in a cell-free tombusvirus replication assay . Further analysis of Cpr7p , which , among cyclophilins , is the strongest inhibitor of TBSV replication in yeast and in vitro , revealed that it binds to the RNA-binding domain of p33 . This binding by Cpr7p , via its TPR ( tetratricopeptide repeats ) domain , leads to inhibition of p33/p92-based recruitment of the TBSV RNA for replication and decreases the efficiency of the VRC assembly . Thus , Cpr7p is a negative regulator of TBSV replication . We also show that Cpr7p can inhibit the replication of the insect alfanodaviruses in yeast model host , suggesting that Cyp40-like cyclophilins might have broad antiviral activities during RNA virus infections .
A previous screen based on MYTH ( membrane yeast two-hybrid ) split-ubiquitin assay and a yeast cDNA library has identified Cpr1 cyclophilin ( CypA-like ) as a strong interactor with p33 replication protein [39] . Since cyclophilins , together with the structurally unrelated FKB proteins and parvulins , constitute a family of 13 prolyl isomerases in yeast , we wanted to know if other members of PPIases could also interact with the TBSV replication proteins . The MYTH assay revealed that 6 members of the cyclophilin family , namely Cpr1p , Cpr3p , Cpr6p , Cpr7p , Fpr1p ( Figure 1A ) and Ess1p [39] , interact with p33 replication protein . To test if cyclophilins can directly affect the activity of the tombusvirus replicase , we prepared cell-free extracts ( CFE ) from yeast strains with deletion of multiple cyclophilin genes ( Figure 1B ) . These yeast extracts contained comparable amount of total proteins ( not shown ) or an ER-resident marker protein [Dpm1p , [29]] ( Figure 1C ) . The advantage of the CFE extract-based TBSV replication assay is that the CFE can be programmed with the TBSV ( + ) repRNA in the presence of purified recombinant p33 and p92pol obtained from E . coli that leads to the in vitro assembly of the viral replicase , followed by a single cycle of complete TBSV replication , resulting in both ( − ) -stranded and ( + ) -stranded repRNA progeny [31] , [35] . Using CFE prepared from a yeast strain missing all 8 CPR and 4 FPR genes ( ESS1 is an essential gene and thus it could not be deleted ) resulted in ∼7-fold increase in TBSV repRNA accumulation in vitro ( Figure 1C , lanes 9–10 ) . This result firmly established that in general cyclophilins are strong inhibitors of TBSV replication . Using CFEs from yeast missing all 8 CPR genes also resulted in ∼5 . 5-fold increase in TBSV repRNA accumulation in vitro ( Figure 1C , lanes 5–6 ) . In contrast , CFE missing all 4 FPR genes supported TBSV replication only a little better than CFE from wt yeast ( Figure 1C , lanes 3–4 ) , suggesting that FPRs are not as important inhibitory proteins as CPRs . Also , CFE missing the five cyclophilins that interact with p33 ( i . e . , Cpr1p , Cpr3p , Cpr6p , Cpr7p , Fpr1p ) supported TBSV replication ∼6 . 5-fold more efficiently than wt ( Figure 1C , lanes 11–12 ) , while deletion of those that do not interact ( Cpr2p , Cpr4p , Cpr5p , Cpr8p , Fpr2p ) resulted in close to wt level of TBSV replication ( Figure 1C , lanes 7–8 ) , suggesting that only the p33-interacting cyclophilins are important for inhibition of TBSV replication in vitro . To identify which CPRs are the most potent inhibitors of TBSV replication , first we have tested TBSV repRNA replication in CFE when purified recombinant cyclophilins were separately added to the reaction . These CFE-based experiments revealed that recombinant Cpr3p and Cpr7p inhibited ( Figure S1A , lanes 9–14 ) , while Cpr1p ( lanes 6–8 ) and Cpr6p ( Figure S1C , lanes 6–8 ) did not inhibit TBSV repRNA accumulation in vitro . Additional testing of TBSV repRNA accumulation in single and double CPR deletion yeast strains revealed that CPR7 is the strongest inhibitor ( Figure 2 and not shown ) . Indeed , we observed that deletion of CPR7 , which is a Cyp40-like cyclophilin that carries both the catalytic Cyp and TPR domains ( involved in protein-protein interactions ) , in yeast resulted in the largest , ∼2–2 . 5-fold increase in TBSV repRNA accumulation ( Figure 2A–B ) among the single deletion strains ( Figure 2 and not shown ) [39] . Interestingly , deletion of CPR6 , which is the second member of the Cyp40-like yeast cyclophilins , did not affect TBSV replication ( Figure 2A–B ) , suggesting that , in spite of the similarities in protein sequence , CPR7 and CPR6 do not have overlapping function in inhibition of TBSV accumulation . This conclusion was further strengthened by the double-deletion yeast strain ( cpr6Δ cpr7Δ ) , which supported TBSV accumulation at ∼2–3-fold higher level than that in the WT yeast , similar to the level seen in cpr7Δ ( Figure 2A–B ) . To measure the direct effect of Cpr7p on TBSV RNA accumulation , we performed CFE-based TBSV replication assays with added purified recombinant Cpr7p . The inhibition of TBSV RNA replication by Cpr7p reached up to 92% in vitro ( Figure 3A , lane 8 ) . The purified TPR domain of Cpr7p was even more potent inhibitor of TBSV replication in vitro ( Figure 3A , lanes 12–14 ) than the full-length protein , while the catalytic Cyp domain had a lesser effect ( up to 80% , Figure 3A , lane 11 ) . The MYTH-based interaction of Cpr7p and its domains with p33 showed that the TPR domain is the strongest interactor , while the full-length protein was also a good interactor when compared with the Cyp-domain ( Figure 3B ) . Thus , there seems to be correlation between the strength of interaction and extent of inhibition by Cpr7 and its derivatives on TBSV replication in vitro . Interestingly , this is not the case with Cpr6p , which strongly interacted with p33 , but did not inhibit TBSV replication effectively ( Figure S2A ) . Data shown below in the paper , however , can explain why CPR6 deletion in yeast did not increase TBSV replication as much as deletion of CPR7 . To determine if plant Cyp40-like proteins can also inhibit tombusvirus replication , we cloned , expressed and purified the TPR domains of three Arabidopsis thaliana Cyp-40-like proteins and two full-length proteins , followed by testing them in our CFE-based TBSV replication assay ( Figure S3 ) . We found that the TPR domains of all three Arabidopsis Cyp40 proteins inhibited TBSV replication . The most efficient inhibitor was the full-length At5g48570 Cyp40 protein by reducing TBSV RNA accumulation by ∼90% in vitro ( Figure 3C , lane 8 versus lane 1 ) . The split-ubiquitin assay revealed that the TPR-domain of At5g48570 and AtTWD1 interacted strongly with both p33 and p92 , while At2g15790 interaction was weak , but detectable ( Figure S3B–C ) . Altogether , these data indicate that the plant Cyp40 proteins and their TPR domains interact with TBSV p33/p92 replication proteins and At5g48570 is as potent inhibitor of tombusvirus replication as the yeast Cpr7p . Since the recombinant Cpr7p was a potent inhibitor of TBSV replication in vitro , we used our CFE-based approach to further dissect the mechanism of Cpr7p-mediated inhibition . Briefly , a two-step replication assay based on yeast CFE was used to determine what steps of TBSV replication could be inhibited by Cpr7p . In this assay , the first step includes the assembly of the replicase complex on the endogenous membranes present in CFE in the presence of the viral ( + ) repRNA , the recombinant p33 and p92 replication proteins and ATP/GTP [31] ( Figure 4A , step 1 ) . Under these conditions , the viral replication proteins recruit the ( + ) repRNA to the membrane and the viral replicase becomes partially RNase and protease insensitive . However , the assembled replicase cannot initiate minus-strand synthesis yet , due to the absence of CTP/UTP [31] . Then , centrifugation and washing the membranes will remove all the proteins and molecules not bound to the membrane . This is then followed by addition of ATP/CTP/GTP/UTP ( 32P-labeled ) to initiate RNA synthesis during the second step . Addition of purified recombinant Cpr7p to the CFE during the first step inhibited TBSV replication up to ∼80% ( Figure 4B , lane 10 versus 1 ) , similar to the strong inhibitory effect of Cpr7p during standard CFE replication assay ( Figure 3 ) . This suggests that Cpr7p might inhibit the assembly of the replicase complex . Interestingly , the TPR domain was again a more potent inhibitor ( Figure 4B , lanes 14–16 ) than the full-length Cpr7p , while the Cyp domain was a lesser inhibitor ( Figure 4B , lanes 11–13 ) . However , addition of Cpr7p or its truncated mutants exclusively during the second step of the CFE assay did not inhibit TBSV RNA replication ( Figure 4C ) . The lack of inhibition by Cpr7p during the second step suggests that either Cpr7p cannot inhibit RNA synthesis by the pre-assembled replicase or Cpr7p cannot access , and thus cannot inhibit the membrane-bound replicase ( possibly due to physical separation or constrainst by the membranous structure ) . To systemically dissect what steps in TBSV replication are inhibited by Cpr7p , first we performed viral ( + ) RNA recruitment assay ( Figure 5A ) . The recruitment of the cytosolic TBSV ( + ) repRNA to the peroxisomal or ER membrane surfaces , where replication takes place , requires p33 and p92 replication proteins [29] , [33] , [44] . We found the purified recombinant Cpr7p and the TPR domain strongly inhibited the recruitment of ( + ) repRNA to the membrane ( Figure 5B , lanes 6–8 and 12–14 versus 1 ) , while the Cyp domain only had a small inhibitory effect ( Figure 5B , lanes 9–11 ) . These data strongly support that Cpr7p can inhibit ( + ) repRNA recruitment , which is critical for TBSV replication [29] , [33] , [44] . The second test focused on the assembly of the viral replicase , which can be performed in the CFE in the presence of ATP and GTP ( Figure 5C ) . Then the assembled , membrane-bound replicase complex is solubilized with nonionic detergent , affinity-purified , and followed by testing its activity with added TBSV ( − ) repRNA [29] , [31] . The data show strong inhibition of replicase activity by the full-length Cpr7p and the TPR domain ( Figure 5D , lanes 2 and 4 ) , suggesting that these proteins inhibited the in vitro assembly of the TBSV replicase complex in vitro . The third assay tested the effect of recombinant Cpr7p and its derivatives on the ( + ) and ( − ) RNA synthesis in the CFE ( Figure 5E ) . This assay is based on the previous observation that the 32P-labeled ( − ) RNA runs as dsRNA , while the newly made 32P-labeled ( + ) RNA migrates as single-stranded product in nondenaturing PAGE [29] . The in vitro results demonstrated that both the ( + ) and the ( − ) RNA synthesis was strongly inhibited by the full-length Cpr7p and the TPR domain , but the ratio of these RNAs did not change ( Figure 5E ) . We interpret these data that RNA synthesis was inhibited by Cpr7p , but this could be either direct inhibition or the consequence of inhibition of the replicase assembly step ( see above ) , which precedes RNA synthesis . For the fourth assay , we utilized detergent-solubilized and affinity-purified tombusvirus replicase from yeast ( Figure 6 ) . This purified replicase lacks endogenous RNA , which is removed during purification , and can only synthesize complementary RNA products on added TBSV templates [20] , [25] , [36] . However , unlike the above membrane-bound replicase in the CFE-based assay , it cannot perform a complete cycle of RNA synthesis [20] , [36] . The addition of purified recombinant Cpr7p to the purified tombusvirus replicase programmed with the ( − ) repRNA inhibited ( + ) -strand synthesis by up to ∼3-fold ( Figure 6A , lanes 7–9 versus 4–6 ) . Interestingly , Cpr7p inhibited not only the production of the full-length ( + ) -strand RNA product ( produced via de novo initiation ) , but the amount of 3′-terminal extension product ( 3′TEX; due to self-priming by the 3′ end of the template [45] , [46] , [47] ) as well . Therefore , we suggest that Cpr7p can inhibit both de novo initiation and 3′TEX on the ( − ) RNA template by the tombusvirus replicase . Pre-incubation of Cpr7p with the purified replicase preparation , prior to the addition of the template ( − ) repRNA and the ribonucleotides to start complementary RNA synthesis , resulted in inhibition of RNA synthesis by ∼2-fold ( Figure 6B , lanes 7–9 versus 4–6 ) . These data demonstrate that Cpr7p likely associates rapidly with the tombusvirus replication proteins and pre-incubation is not needed to increase the strong inhibitory function of Cpr7p on tombusvirus replicase activity . To obtain information on how Cpr7p could inhibit RNA recruitment and replicase assembly as well as block the replicase function , we decided to map the Cpr7p binding site in the overlapping TBSV replication proteins . To this end , we have used pull-down experiments with immobilized MBP-p33 and its truncation derivatives ( Figure 7A ) from yeast extract containing FLAG-tagged Cpr7p . These experiments revealed that Cpr7p binds to the arginine-proline-rich ( RPR ) motif in p33 ( compare pC4 and pC6 versus pC7 , lanes 6 , 10–11 , Figure 7B ) . Indeed , deletion of the RPR motif inhibited p33 binding to Cpr7p ( p33CΔRPR , Figure 7C , lane 3 ) . The RPR-motif is the well-characterized RNA-binding site in p33 and p92 replication proteins required for specific viral RNA recruitment and replicase assembly [33] , [35] , [44] . In contrast , Cpr6p , another Cyp40-like protein with TPR domains , which also binds p33 replication proteins ( Figure 1A ) , does not bind to the RPR-motif in p33 ( Figure 7C , lane 6 ) , but instead to a region proximal to the C-terminus ( Figure S2B–C ) . Based on this difference between Cpr6p and Cpr7p in binding to p33 ( and likely the overlapping p92 ) , we propose that Cpr7p-binding to the RPR-motif in p33 is required for the inhibitory effect of this cyclophilin on tombusvirus replication . To test if replication of other RNA viruses could also be regulated by Cyp40 proteins , first we have performed MYTH assay with cyclophilins and replication proteins of plant and insect viruses . These experiments revealed that both Cpr7p and Cpr6p could interact with protein A of Nodamuravirus ( NoV ) , an insect virus , p130 replication protein of Tobacco mosaic virus ( TMV ) and p28 of Turnip crinkle virus ( TCV ) ( Figure 8A ) . In addition , we found that Cpr1p ( Figure S4A–D ) and Cpr3p ( Figure 8A ) can also interact with NoV protein A , TMV p130 and TCV p28 , although the importance of these interactions was not studied further . Then , we tested if deletion of CPR7 and CPR6 could affect NoV and the related Flock house virus ( FHV ) replication in yeast model host [48] . Interestingly , NoV RNA replicated ∼5-fold more efficiently in cpr7Δ and cpr6Δcpr7Δ yeast when compared with the wt yeast cells ( Figure 8D , lanes 7–12 versus 1–3 ) . Similar to TBSV , NoV RNA also replicated only slightly better in cpr6Δ yeast ( Figure 8D , lanes 4–6 ) than the wt yeast , suggesting that CPR6 is not as important as CPR7 in regulation of NoV replication in yeast . We found similar pattern with FHV ( Figure 8E ) , which is closely related to NoV , that CPR7 deletion has a bigger effect than CPR6 deletion on virus replication . Overall , these experiments confirmed that replication of two insect RNA viruses is also affected by Cpr7p , and that Cpr7p can interact with the replication protein of NoV in yeast . Therefore , it is possible that replication of other RNA viruses could be regulated by Cyp40 proteins .
Genome-wide screens and global proteomics approaches with tombusviruses unraveled many host proteins that can facilitate or inhibit virus infections [9] , [11] , [22]–[24] , [29] , [39] , [49] , [50] . One group of these host factors is cyclophilins , a large family of peptidyl-prolyl cis-trans isomerases with protein chaperone-like function that play a global role in facilitating correct protein folding and conformational changes in client proteins [40] , [41] . Isomerization of peptidyl-prolyl bonds is frequently required for protein refolding , maturation and trafficking . Cyclophilins share a common 109 aa cyclophilin-like domain ( CLD ) performing the PPIase activity and additional domains unique to each member of the family . The unique domains , such as the TPR domain , are important for selection of protein substrates and subcellular compartmentalization . S . cerevisiae and Arabidopsis have 8 and 29 cyclophilins , respectively , while humans have 16 cyclophilin isoforms that have different cellular and tissue distribution [41] , [43] , [51] . We have found that four yeast cyclophilins ( Cpr1p , Cpr3p , Cpr6p and Cpr7p ) , in addition to Fpr1p ( Figure 1 ) and Ess1p PPIases [39] , interacted with p33 . CFEs prepared from a yeast strain lacking these genes ( except ESS1 , which is essential for yeast growth ) supported TBSV replication in vitro at a higher level than CFE from wt yeast , or a yeast strain lacking the remaining noninteracting members of cyclophilin and FKB genes . Altogether , the data firmly established that the group of cyclophilins that interacts with p33 is a robust negative regulator of TBSV replication . Among the cyclophilins , we have discovered that the yeast Cyp40-like Cpr7p strongly inhibits TBSV replication in yeast model host and in vitro in a TBSV replication assay based on CFE . Three Cyp40-like proteins from Arabidopsis plant also interacted with p33/p92 replication proteins and they showed remarkable inhibitory effect on TBSV repRNA accumulation when added to the CFE-based replication assay ( Figure 3C and Figure S3 ) . Thus , the inhibitory effect of Cyp40-like proteins against TBSV is conserved between S . cerevisiae and Arabidopsis . Also , it seems that Cpr7p can also inhibit the replication of insect RNA viruses NoV and FHV ( Figure 8 ) [52] , which are distantly related to TBSV . Based on these data , we propose that Cyp40-like proteins could be important inhibitors or regulators of various RNA viruses . Interestingly , Cyp40 is required for the activity of microRNAs in plants [53] . Thus , in addition to the regulatory role in RNA viruses , Cyp40 is also involved in other RNA-based processes . The inhibitory effect of Cpr7p on TBSV replication seems to depend on the TPR-domain , which can specifically bind to the RNA-binding region of p33/p92 replication proteins of TBSV ( Figure 7 ) . Indeed , Cpr7p or the TPR domain: p33 interaction inhibits the recruitment of the viral RNA to membranes ( Figure 9 ) , where viral replication takes place . In addition , Cpr7p also inhibits the assembly of the functional VRC in vitro ( Figure 9 ) , which then leads to decreased level of viral RNA synthesis [both ( + ) and ( − ) RNA synthesis is inhibited proportionately , Figure 5E] . Inhibition of VRC assembly could be due to binding of Cpr7p to the RPR RNA binding motif , thus inhibiting the RNA binding activity of p33 and p92 . Without the bound ( + ) RNA , p33 and p92 cannot assemble functional VRC since multiple cis-acting elements within the TBSV ( + ) RNA are essential for VRC assembly [36] , [54] . However , we cannot exclude that Cpr7p might inhibit VRC assembly indirectly via inhibition of viral ( + ) RNA recruitment , which is required for VRC assembly [20] , [36] . In addition , recombinant Cpr7p binding to the purified tombusvirus replicase also results in reduced RNA synthesis in vitro , indicating that Cpr7p can block viral RNA replication at several steps ( Figure 9 ) . Interestingly , Cpr7p cannot efficiently inhibit the pre-assembled , membrane-bound TBSV replicase in a CFE-based assay ( Figure 4C ) , suggesting that Cpr7p has to be present at the early stage of replicase assembly for robust inhibition . It is likely that the membrane-bound VRC might not be accessible for Cpr7p after the VRC assembly is completed in CFE . Similar to Cpr7p , another Cyp40-like protein ( i . e . , Cpr6p with 38% sequence identity with Cpr7p ) can also bind to p33/p92 replication proteins ( Figure 1 and S2B ) . However , this binding does not lead to significant inhibition of TBSV replication in yeast ( Figure 2 ) or in the CFE-based TBSV replication assay ( Figure S1C ) . Since the Cpr6p : p33 interaction does not involve the RNA-binding region in p33 ( Figure S2B ) , we propose that this interaction is less inhibitory to TBSV replication by likely allowing p33 to perform its important functions in viral RNA recruitment and VRC assembly as well as its RNA chaperone activity [33] , [55] . Another possibility for the weak inhibitory effect of Cpr6p is its low chaperone activity . Accordingly , it has been shown that Cpr6p and Cpr7p have different activities: Cpr6p is far more active as a PPIase , while Cpr7p is a much better chaperone than Cpr6p [56] . Indeed , CPR6 could not complement the growth defect caused by deletion of CPR7 in yeast and the defined functions of Cpr7p in signaling and HSP90 binding are dependent on the TPR domain [56]–[58] . Interestingly , Cpr6p can also interact with protein A of NoV without effective inhibition of NoV replication ( Figure 8 ) . Thus , it seems that Cpr6p has evolved different functions from Cpr7p , including the regulatory role in viral RNA replication . In contrast , all three Arabidopsis Cyp40-like proteins are inhibitory to TBSV replication in vitro ( Figure 3 ) , suggesting that plants might have more cyclophilins to combat some viral infections than yeast does . Similar to our findings with Cyp40 homologs , other cyclophilins have been shown to inhibit accumulation of several RNA viruses . For example , CypA was found to bind to the matrix protein ( M1 ) of influenza A virus and interfered with the nuclear localization of M1 [59] . Over-expression of CypA increased the self-association of M1 protein and led to decreased viral replication . Similarly , CypA was also found to inhibit the infectivity of HIV-1 ( human immunodeficiency virus-1 ) virions [60] , [61] . Interestingly , due to retrotransposition of CypA between exon 7 and 8 in TRIM5 gene in New World owl monkeys , a CypA-TRIM5 fusion protein has emerged , which targets the HIV-1 coat protein causing resistance to HIV-1 infections [62] , [63] . CypA gets incorporated into HIV-1 virions via direct binding to Gag , the polyprotein precursor of virion structural proteins [61] , [64] , [65] . The role of CypA as an anti-HIV protein is neutralized by the retroviral Vif protein , which inhibits the incorporation of CypA into the viral particles [66] . A genome-wide screen for host factors affecting West Nile virus identified FKBP1B immunophilin as an inhibitor of this virus [67] . Overall , cyclophilins/immunophilins are potent inhibitors of several RNA viruses and they might be part of the innate response of the host against some viruses . Since PPIases are conserved and ubiquitous proteins , their role could be common against viruses . On the contrary , several RNA viruses seem to hijack cyclophilins to facilitate their replication . For example , cyclosporine , an inhibitory peptide of cyclophilins , markedly inhibited HCV replication in cell cultures [68] , [69] . It has been proposed that the cytosolic CypA affects the assembly of the HCV VRC by likely influencing the cleavage of NS5A-NS5B fusion protein and the folding of the NS5A and NS5B RdRp proteins [70]–[72] . CypA seems to affect multiplication of several flaviviruses , such as WNV , Dengue virus and yellow fever virus [73] . CypA was shown to bind to the NS5 replication protein and is likely component of the VRC and cyclosporine has been shown to inhibit flaviviral RNA synthesis [73] . The human FKBP8 immunophilin , which interacts via the three TPR repeats with the HCV NS5A is also important for HCV replication [74] . FKBP8 might function in HCV replication by forming a complex between NS5A , FKBP8 and Hsp90 [74] . Interaction between NS5A and FKBPs is also important for HCV to block apoptosis in hepatoma cells [75] . Overall , cyclophilins and other PPIases seem to play important roles during RNA virus infections . The current work revealed a new role for Cyp40-like proteins and their TPR domains as inhibitors and regulators of RNA virus replication . This function for Cyp40 seems to be conserved between yeast and plants and also effective against tombus- and alfanodaviruses .
The following yeast strains with deletion of various cyclophilin genes were used in this study: the wild type #149 ( JK93da ) ; 163: KDY81 . 18C ( fpr1-4Δ ) ;164: KDY97 . 19 ( cpr1-8Δ ) ; 165: SMY101-1 ( cpr2 , 4 , 5 , 8Δ/fpr2Δ ) ; 166: KDY98 . 49 ( cpr1-8Δ/fpr1-4Δ ) ( generous gift of Joseph Heitman ) [76] . Yeast strain 168 ( cpr1 , 3 , 6 , 7Δ/fpr1Δ ) was created by homologous recombination based on strain KDY75 . 3b using the HYG gene to replace CPR3 . PCR was performed using plasmid pFA6-hphNT1 ( EUROSCARF ) [77] as template and primer set #3279/#3280 ( Table S1 ) . The PCR product was transformed to KDY75 . 3b yeast strain and recombinant yeast colonies were selected in SC-LA− plates supplemented with G418 and hygromycin . The plasmids pHisGBK-CUP-His33/GAL-DI72 and pGAD-CUP-Hisp92 used for viral replication experiments in yeast , have been previously described [26] , [78] . Plasmids pGAD-ADH-HFp92 , pYC-GAL-DI72 , and pHisGBK-ADH-HFp33 were generated by Serva and Nagy [25] . To generate the Escherichia coli expression plasmids for GST-CPR7 and its truncated derivatives , CPR7-CYP and CPR7-TPR , we PCR-amplified the full-length sequence of CPR7 as well as CPR7-CYP and CPR7-TPR domains ( Figure 3B ) with primer sets #3152/#4116 , #3152/#4132 , and #4131/#4116 , respectively . To generate the E . coli expression plasmids for GST-At5g48570 and GST-At5g48570-TPR , we PCR amplified the full-length sequence of At5g48570 and At5g48570-TPR with primer sets #4332/#4333 and #4374/#4375 ( Table S1 ) , respectively . The PCR products were digested with BamHI and EcoRI and ligated into pGEX-2T using the same enzymes . The FHV expression plasmid pESC-His/gal/FHV/RNA1 has been generated before [48] . To launch NoV RNA1 replication in yeast , we generated plasmid pEsc-His/Cupm/NoV/RNA1/TRSVrz that expressed the full-length NoV RNA1 via cleavage of the transcribed RNA1 at the 3′ end by a ribozyme , similar to the strategy used for FHV RNA1 expression [48] . Three PCR products were ligated to each other in a stepwise fashion . The first fragment was amplified from pGAD/CupHis92 with primers #3873 and #3861 to obtain a modified CUP1 promoter with a predicted transcriptional start site at or around the first viral nucleotide . The second fragment was amplified from pMT/NoVRNA1 ( provided by SW Ding ) with primers #3862 and #3863 to obtain the full-length cDNA for NoV RNA1 . The third fragment was generated with primers #3871 and #3875 from pYC/DI72/sat to amplify the ribozyme . The PCR reactions were carried out with Phusion polymerase . Primers #3861 , #3862 , #3863 and #3871 were kinased with T4 polynucleotide kinase prior to PCR . The final PCR was carried out with primers #3873 and #3875 using High Fidelity Taq polymerase ( Invitrogen ) . The resulting PCR product was digested with BglII and NheI and cloned into pEsc-His ( Agilent ) digested with BamHI and BcuI . The split-ubiquitin assay is based on the Dualmembrane kit3 ( Dualsystems ) and performed as previously described [39] , [79] . The bait constructs , pGAD-BT2-N-His33 and pGAD-BT2-N-His92 , expressing tombusvirus replication proteins p33 and p92 , respectively , were as described [26] , [39] . The CPR7 and the truncated PCR products of CPR7 were digested with BamHI and NheI and ligated into the pPRN-N-RE ( Dualsystems ) vector digested with the same enzymes to generate pPRN-N-CPR7 . Yeast strain NMY51 was co-transformed with pGAD-BT2-N-His33 and pPR-N-RE ( NubG , as a negative control ) or one of the prey-constructs ( pPR-N-CPR7 ) and plated onto Trp−/Leu− ( TL− ) synthetic minimal medium plates for plasmid selection . Yeast colonies were then re-suspended in 50 µl water and spotted onto Trp−/Leu−/His−/Ade− ( TLHA− ) plates to detect p33 : Cpr7p interactions as described [26] . Plasmid expressing Ssa1p protein ( pPR-N-SSA1 ) was used as a positive control as previously described [39] , [79] . The MBP-tagged p33 and p33C were purified from E . coli as described previously [80] . E . coli cells were resuspend in cold column buffer ( 10 mM Tris-HCl [pH 7 . 4] , 1 mM EDTA , 25 mM NaCl , 10 mM β-mercaptoethanol ) and broken by sonication . The cleared lysate was passed through an amylose column to bind the MBP-tagged viral proteins or MBP ( negative control ) . The columns were washed three times with cold column buffer prior to addition of the yeast lysate . For the pull-down assay , 100 mg of yeast pellets containing FLAG-tagged Cpr7p ( pESC-His-CupFlag-CPR7 ) or its truncated forms were resuspended in 150 µl pre-chilled Buffer I ( 20 mM Tris-HCl [pH 7 . 5] , 1 mM EDTA , 200 mM NaCl , 10% [V/V] glycerol , 0 . 1% [V/V] NP40 , 10 mM β-mercaptoethanol , 1% [V/V] yeast protease inhibitor cocktail ( Ypic ) ] ) and 1 µl of RNase A ( 1 mg/ml ) . Yeast cells were broken in Genogrinder with 250 µl volume of acid washed glass beads for 2 min at 1 , 500 rpm followed by addition of 600 µl of Buffer I . The yeast lysate was centrifuged at 100× g at 4°C for 5 min , and the supernatant was transferred to a pre-chilled eppendorf tube and centrifuged at 15 , 000 rpm at 4°C for 5 min before loading onto the amylose columns and incubated for 3 h at 4°C . The columns were washed five times with cold column buffer II ( 20 mM Tris-HCl [pH 7 . 5] , 1 mM EDTA , 100 mM NaCl , 10% [V/V] glycerol , 0 . 1% [V/V] NP40 ) , and the bound protein complexes were eluted with column buffer supplemented with 10 mM maltose . The presence of FLAG-Cpr7p protein in the eluate was analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) followed by Western blotting using an anti-Flag antibody ( Sigma ) . The amount of MBP-tagged viral proteins in the eluate was visualized by Coomassie blue staining of the 12% SDS-PAGE gels . For measuring TBSV repRNA accumulation , yeast strain BY4741 , cpr6Δ , cpr7Δ , and cpr6 , 7Δ were transformed with plasmids pGAD-CUP-Hisp92 , pHisGBK-CUP-Hisp33/GAL-DI72 . Replication assay was performed by measuring the accumulation of DI-72 ( + ) repRNA ( normalized to the 18S rRNA ) . Replication was induced by adding 50 µM CuSO4 to the SC-LH− medium and then yeast was cultivated for 24 h at 23°C or 29°C . For measuring NoV RNA1/3 accumulation , yeast strain BY4741 , cpr6Δ , cpr7Δ , and cpr6 , 7Δ were transformed with plasmid pESC-His/Cupm/NoV/RNA1/TRSVrz . Replication was induced by adding 50 µM CuSO4 to the SC-H− medium containing 2% glucose and then yeast was cultured for 48 h at 29°C . For measuring FHV RNA1/3 accumulation , yeast strain BY4741 , cpr6Δ , cpr7Δ , and cpr6 , 7Δ were transformed with plasmids pESC-URA/Cupm/FHV/DI634/TRSVrz and pESC-His/Gal/FHV/RNA1/TRSVrz . Replication was induced by adding 50 µM CuSO4 to the SC-UH− medium containing 2% galactose and then yeast was cultured for 24 h at 29°C . The different yeast strains used for preparation of CFEs were grown in YPD medium at 29°C for 6–8 h to a final OD 1 . 5 followed by 30 min at 37°C before harvest and prepared as described [31] . A total reaction volume ( 25 µl ) contained 3 µl of whole cell extract , 0 . 5 µg DI-72 ( + ) repRNA transcript , 700 ng purified MBP-p33 , 1 . 4 µg purified MBP-p92pol ( both recombinant proteins were purified from E . coli , see below ) , 30 mM HEPES-KOH , pH 7 . 4 , 150 mM potassium acetate , 5 mM magnesium acetate , 0 . 13 M sorbitol , 0 . 4 µl actinomycin D ( 5 mg/ml ) , 2 µl of 150 mM creatine phosphate , 0 . 2 µl of 10 mg/ml creatine kinase , 0 . 2 µl of RNase inhibitor , 0 . 2 µl of 1 M dithiothreitol ( DTT ) , 2 µl of 10 mM ATP , CTP , and GTP and 0 . 25 mM UTP and 0 . 1 µl of [32P]UTP [35] . The reaction mixture was incubated at 25°C for 3 h and terminated by adding 100 µl stop buffer ( 1% sodium dodecyl sulfate [SDS] and 0 . 05 M EDTA , pH 8 . 0 ) followed by phenol-chloroform extraction , isopropanol-ammonium acetate precipitation overnight at −20°C and a washing step with 70% ethanol as described [81] . The newly synthesized 32P-labeled RNA products were separated by electrophoresis in a 5% polyacrylamide gel ( PAGE ) containing 0 . 5× Tris-borate-EDTA ( TBE ) buffer with 8 M urea . Signals were detected using a Typhoon 9400 imaging scanner ( Amersham ) and quantified by imageQuant software . Reaction mixtures for in vitro replicase assays were set-up according to the in vitro replication assay previously described [31] , [35] with the following modification . In addition to the recombinant viral replication proteins , MBP-p33 and MBP-p92pol , 800 ng of GST-Cpr7p or its deletion derivatives were also included in the assay . Reactions containing only GST and CFE served as negative control . The assays included increasing amounts ( 0 . 4 , 0 . 8 and 1 . 6 µg ) of purified recombinant proteins , such as GST-Cpr7p ( 5 . 6 , 11 . 3 and 22 . 5 µM ) , GST-CYP ( 7 . 6 , 15 . 3 and 30 . 5 µM ) , GST-TPR ( 8 . 3 , 16 . 7 and 33 . 4 µM ) or GST ( 15 . 4 , 30 . 7 and 61 . 5 µM ) . Expression and purification of the recombinant TBSV p33 and p92 replication proteins from E . coli were carried out as described earlier with modifications [80] , [82] . The expression plasmids were transformed separately into E . coli strain BL21DE3CodonPlus . Protein expression was induced using isopropyl β-D-thiogalactopyranoside ( IPTG ) for 8 h at 16°C , and the cells were harvested by centrifugation at 5 , 000 rpm at 4°C for 5 min to remove the medium prior to −80°C storage . Affinity columns containing amylose resin ( NEB ) were used to purify MBP-tagged recombinant proteins as described [80] , [82] . Purification of GST-tagged proteins ( pGEX- ) was carried out using glutathione resin and eluted with 10 mM glutathione , 10 mM ß-mercaptoethanol in the column buffer following the same protocol as MBP-proteins . Briefly , the frozen pellets were suspended and sonicated in MBP column buffer containing 20 mM Tris-Cl pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 10 mM β-mercaptoethanol and 1 mM phenylmethylsulfonyl fluoride ( PMSF ) . The sonicated extract was centrifuged at 15 , 000 rpm for 5 min , and the supernatant was added to the pre-equilibrated amylose resin for 1 h rotating incubation at 4°C . After washing the resin 3 times with column buffer and once with a low salt column buffer ( 25 mM NaCl ) , the proteins were eluted with a low salt column buffer containing 0 . 18% ( V/W ) maltose and 6% ( V/V ) glycerol and aliquoted for storage at −80°C . The concentration of the purified recombinant proteins was measured by Bio-Rad protein assay . Protein fractions used for the replication assays were 95% pure , as determined by SDS-PAGE . Yeast BY4741 strain was transformed with the plasmids pGAD-ADH-HFp92 , pYC-GAL-DI72 , and pHisGBK-ADH-HFp33 expressing 6xHis- and Flag-tagged tombusvirus p33 and p92 . Initially , yeast transformants were grown in 10 ml of ULH− selective medium overnight at 30°C then transferred to fresh 200 ml ULH− medium to 0 . 2 OD600 and grown at 23°C until 1 . 5 OD600 . FLAG-affinity-purification was done according to a previously described procedure with the following modification [24] . Briefly , 2 g of yeast cells were resuspended and homogenized in TG buffer [50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , 10 mM KCl , 0 . 5 M NaCl , 0 . 5% Triton , and 1% [V/V] yeast protease inhibitor cocktail ( Ypic ) ] by glass beads using FastPrep Homogenizer ( MP Biomedicals ) . The yeast cell lysate was cleared by centrifugation at 500× g for 5 min at 4°C to remove unbroken cells and debris . The membrane fraction containing the viral replicase complex was collected by centrifugation at 35 , 000× g for 15 min at 4°C and then solubilized in 1 ml TG buffer with a buffer containing 2% Triton , 1% [V/V] Ypic via gentle rotation for 3 h at 4°C . The solubilized membrane fraction was centrifuged at 35 , 000× g for 15 min at 4°C and the supernatant was incubated with 100 µl anti-FLAG M2-agarose affinity resin ( Sigma ) pre-equilibrated with 1 ml TG buffer . After 3 h of gentle rotation at 4°C , the column was washed 5 times with TG buffer containing 0 . 5% Triton . The resin-bound replicase complex was eluted in 700 µl elution buffer [50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , 10 mM KCl , 0 . 05 M NaCl , 0 . 5% Triton , 1% Ypic and 0 . 15 mg/ml FLAG peptide ( Sigma ) ] following overnight rotation at 4°C . In vitro replicase activity of the purified preparations was tested using DI-72 ( − ) RNA template transcribed in vitro by T7 transcription [24] . The in vitro RNA recruitment assay based on yeast CFE was performed according to [31] , [35] , except that 32P-labeled DI72 ( + ) repRNA were used and rCTP , rUTP , 32P-labeled UTP , and Actinomycin D were omitted from the assay . As a negative control , GST protein purified from E . coli was used in yeast CFE in the absence of p33/p92 . Following two-hour incubation at 25°C , 1 ml of reaction buffer was added to the in vitro reaction mixture and further incubated on ice for 10 min before centrifugation at 35 , 000× g for 45 min . The membrane-bound 32P-labeled ( + ) repRNA was extracted from the pellet by adding 0 . 1 ml stop buffer and 0 . 1 ml phenol/chloroform followed by brief vortex at high setting and centrifuged at 27 , 000× g for 4 min . The supernatants from each reaction were precipitated with isopropanol/ammonium acetate overnight at −20°C . The RNA samples were analyzed by denaturing PAGE and phosphoimaging as described [31] , [35] . Total-RNA isolation and Northern blot analysis were performed as described previously [21] . The probes for Northern blot to detect FHV RNA1/RNA3 ( annealing to the 3′ end of FHV RNA1 ) and NoV RNA1/RNA3 ( annealing to the 3′ end of NoV RNA1 ) was obtained via T7 transcription on PCR templates generated by using the following primers: #3676 and #3675 ( for FHV ) and #3867 and #3868 ( for NoV ) ( Table S1 ) . Protein analysis was performed as described previously using an anti-His6 antibody [20] as the primary antibody for the detection of His6-p33 and His6-p92 . Detection of FLAG-CPR7 and its truncated derivatives were carried out using primary anti-FLAG antibody following the manufacturer's instructions ( Sigma ) . The secondary antibody for both primary antibodies was alkaline phosphatase-conjugated anti-mouse immunoglobulin G ( Sigma ) . Additional Materials and Method section is available as Text S1 . | Replication of plus-stranded RNA viruses , which are important pathogens of humans , animals and plants , can be inhibited by host-coded proteins . In this paper , the authors show that the Cyp40-like Cpr7p prolyl isomerase of yeast can effectively inhibit tombusvirus replication . This inhibition is due to binding of the TPR ( tetratricopeptide repeats ) domain of Cpr7p to the RNA-binding region of the tombusvirus replication proteins that leads to inhibition of RNA binding by the viral replication proteins , interference with the assembly of the viral replicase and blocking viral RNA synthesis . Cpr7p is also effective against the distantly-related alfanodaviruses of insects . Overall , this work reveals a role for a Cyp40-like protein as a regulator of RNA virus replication . This function of Cyp40 during RNA virus infection seems to be conserved between yeast and plants . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"plant",
"science",
"plant",
"biology",
"biology",
"microbiology"
] | 2012 | The TPR Domain in the Host Cyp40-like Cyclophilin Binds to the Viral Replication Protein and Inhibits the Assembly of the Tombusviral Replicase |
Multiple discrete regions at 8q24 were recently shown to contain alleles that predispose to many cancers including prostate , breast , and colon . These regions are far from any annotated gene and their biological activities have been unknown . Here we profiled a 5-megabase chromatin segment encompassing all the risk regions for RNA expression , histone modifications , and locations occupied by RNA polymerase II and androgen receptor ( AR ) . This led to the identification of several transcriptional enhancers , which were verified using reporter assays . Two enhancers in one risk region were occupied by AR and responded to androgen treatment; one contained a single nucleotide polymorphism ( rs11986220 ) that resides within a FoxA1 binding site , with the prostate cancer risk allele facilitating both stronger FoxA1 binding and stronger androgen responsiveness . The study reported here exemplifies an approach that may be applied to any risk-associated allele in non-protein coding regions as it emerges from genome-wide association studies to better understand the genetic predisposition of complex diseases .
Chromosome 8q24 is an established risk locus for many common epithelial cancers . The region was originally discovered by fine-mapping of a prostate cancer linkage peak from a family-based study by deCODE genetics [1] and common alleles in the region have subsequently been found in genome-wide scans of prostate , colon and breast cancer [2]–[4] . More recently , several other cancer types were associated with different discrete regions of 8q24 , with the exception of rs6983267 , which is a susceptibility marker for prostate and colon cancers , and perhaps also ovarian and other cancers [5] , [6] . The alleles reside in distinct linkage disequilibrium blocks including three independent regions for prostate cancer risk ( regions 1–3 ) , one for breast cancer risk and one for bladder cancer risk [2] , [4] . These findings suggest that a common biological mechanism underlies the association of cancer with 8q24 polymorphisms , and also argue for organ- and site-specific functions of elements in this region . Most of the cancer risk variants at 8q24 are encompassed in an approximately 500-kb long stretch of sequence that is devoid of well-characterized genes - the closest annotated gene locus in this area is the oncogene MYC that resides approximately 200-kb telomeric from the nearest linkage disequilibrium block region containing a risk variant . Since the consequences of sequence changes in non protein-coding regions of the genome are more difficult to predict than changes in coding regions , defining the mechanisms by which the 8q24 alleles confer risk has so far been challenging . Another complication is that genetic variants discovered through association studies are rarely the actual causal variant , since they may be associated with disease risk simply due to linkage disequilibrium , which sometimes extends over relatively large distances in the human genome . Because of these factors , understanding the mechanisms that increase cancer risk requires an integrated and systematic approach . One hypothesis is that the 8q24 alleles affect the sequence of unannotated transcripts ( e . g . noncoding RNAs or unknown protein-coding genes ) or change the regulation of such transcripts in cis . The ENCODE project and the recent reports on long noncoding RNAs [7] clearly demonstrated that a large number of unannotated transcripts are expressed in the human genome [8] . Another hypothesis is that the 8q24 risk regions contain specialized and perhaps tissue-specific regulatory elements ( enhancers ) that can influence the behavior of other loci ( i . e . their target genes ) . Post-translational modifications of histones ( e . g . methylation , acetylation , etc . ) have proven useful in annotating sites of regulatory activity in the human genome . Histone 3 acetylation ( AcH3 ) is typically associated with chromatin accessibility and transcriptional activity , and widely used for the prediction of functional elements such as promoters and enhancers [9] . Studies further demonstrate that other histone modifications [e . g . , mono- and tri-methylation at histone 3 lysine 4 ( H3K4me1 & 3 , respectively ) and trimethylation at histone 3 lysines 27 and 36 ( H3K27me3 & H3K36me3 , respectively ) ] are strongly correlated with different modes of genomic activity . Specifically , H3K4me3 is often associated with active transcription start sites ( TSSs ) , H3K4me1 with enhancers and regions flanking TSSs , H3K27me3 with transcriptional silencing and H3K36me3 with transcriptional elongation through genes [10] , [11] . Loci that are mapped as putatively active based on epigenomic profiling can then be independently evaluated through functional analyses , such as reporter assays [10] , [12] . The overall objective of this study was to systematically evaluate the possible role ( s ) of regions within the 8q24 genomic risk interval , overcoming the aforementioned difficulties using a combination of epigenetic , bioinformatic , and molecular biological analyses on multiple cell lines and tissue samples . We report here two main findings: ( i ) evidence is presented that certain 8q24 risk regions exhibit minimal RNA transcriptional output but bear the markers of regulatory elements that are functionally active as enhancers . ( ii ) More specifically , we demonstrate that a new androgen-dependent enhancer in one of the prostate cancer risk regions is functionally influenced by a risk-associated single nucleotide polymorphism ( SNP ) via differential FoxA1 binding .
To study the transcriptional landscape at the 8q24 region , we generated double-stranded cDNA from 20 normal prostate tissue samples , the prostate cancer cell lines LNCaP and PC3 , the colon cancer line HCT116 and the breast cancer line MCF7 . We hybridized each of these cDNA samples to our custom tiling array and normalized the probe intensity values against their genomic DNA background . As shown in Figure 1 , the overall expression data display a very robust expression pattern from known genes , including MYC , PVT1 , FAM84 and TRIB1 , with exon probes showing higher intensity levels than introns , and intron probes showing higher intensity levels than intergenic sequences ( Figure S1 ) . The tissue expression data clearly reflect the organization of the genomic interval surrounding MYC , including a 400-kb region spanning risk region 1 where no significant RNA could be detected . Interestingly , the transcription signature in LNCaP follows reasonably closely that of the prostate tissues , but the other three cell lines ( including the PC3 prostate cancer line ) behave differently . In region 2 , we observed a highly reproducible transcriptional signature in tissues and LNCaP cells , but not in the other cell lines . We detected a strong putative transcript downstream of region 2 in the colon cancer line HCT116 , and other cell type-specific signatures outside of the risk regions . We also observed weak evidence of transcripts in the breast cancer region and in region 3 , including a possible transcript from the POU5F1 pseudogene in all cell lines ( Figure S2 ) . In contrast , region 1 was totally devoid of transcripts in all tissues and cell lines . We did not investigate the transcripts originating in region 2 and 3 further , since their abundance in prostate tissue was not affected by risk haplotypes in the region [14] . In parallel , we generated high-resolution epigenomic profiles for the entire 5-Mb interval using ChIP-chip . For this purpose we hybridized ChIP material to the same tiling array used earlier for transcriptional profiling . Initially , we analyzed AcH3 in three cell lines representing prostate ( LNCaP ) , breast ( MCF7 ) and colorectal ( HCT116 ) cancer . Because three regions independently impose prostate cancer risk , we also interrogated two prostate cancer cell lines ( PC3 & LNCaP ) more extensively for other key epigenetic marks at high resolution . Additional histone modifications chosen were the activation marks H3K4me1 & H3K4me3 [10] , the transcription elongation mark H3K36me3 and the polycomb repressive mark H3K27me3 . We also profiled RNA polymerase II ( RNAPII ) and patterns of androgen receptor ( AR ) occupied regions ( ARORs ) . This entire multi-dimensional dataset ( including cDNA profiles ) was then subjected to extensive statistical analysis using spatial clustering , a new method that allows the dissection of large genomic regions into distinct clusters , each reflecting a specific combinatorial pattern of epigenetic marks in an unbiased manner [15] , [16] . Spatial clustering of the 5-Mb region surrounding and including the 8q24 risk loci is shown in Figure 2A and 2B . This unsupervised cluster analysis revealed domains of combinatorial histone modification and cDNA patterns , and determined the most likely type of behavior at each genomic locus . Six domain types were evident , color-coded and numbered I–VI ( Figure 2A ) . The cancer risk regions are bordered by two distinct domains located 2-Mb apart: a 1-Mb type IV domain ( located ∼127 Mb ) , which is weakly enriched with H3K27me3 marks , and a type I domain-encompassing MYC ( located ∼129 Mb ) , which is strongly enriched with activation-associated marks and transcription ( Figure 2B ) . The prostate cancer risk regions 1–3 were assigned to a type VI domain , indicating that the chromatin of the risk-linked domain is uniquely structured , and includes features that are distinctly different from the aforementioned flanking regions . Importantly , an additional LNCaP H3K27me3 domain ( domain IV ) is located downstream of MYC , with significant H3K27me3 enrichment limited to LNCaP ( Figure S3 ) . As H3K27me3 is a modification associated with polycomb-mediated repression , this suggests that in LNCaP the chromosomal architecture may group the MYC genes and the risk regions in between large repressed domains , possibly facilitating interactions between them . A higher-resolution epigenetic map of the risk regions in LNCaP is shown in Figure 3 . As noted above , regions 1 and 3 were not robustly transcribed in either the normal tissues or prostate cancer cell lines . The histones in this region , however , were highly modified in LNCaP , with particular enrichment for active chromatin marks , i . e . AcH3 , H3K4me1 and H3K4me3 . Additionally we observed significant occupancy of AR and RNAPII . Importantly , these patterns of activity were absent from PC3 , which does not express the AR . The risk regions were also enriched for the elongation mark H3K36me3; however , in line with the general lack of transcription , the H3K36me3 areas were not polarized to a specific side of adjacent RNAPII peaks . Risk region 1 included , in addition , the three strongest H3K27me3 peaks in the 5-Mb region , suggesting that some polycomb dependent repression may affect region 1 activity in LNCaP cells . The epigenomic organization of the risk regions therefore reflects multiple hotspots of active chromatin , involving RNAPII , AR occupancy and activation as well as elongation marks , but without any detectable transcriptional footprints . Thus , these features may be understood as describing enhancers that regulate either dormant transcriptional units in cis or remote active transcriptional units in trans . We note that we could not rule out the possibility of small non-coding RNAs being transcribed from the region , since RNA species shorter than 200-bp were excluded from our preparation . In order to investigate the regulatory potential of the loci exhibiting active chromatin marks , we next performed a systematic series of heterologous enhancer assays , focusing initially on defined acetylation peaks contained within the cancer risk intervals ( called AcP1 through AcP15 , in Figure 3 ) . We cloned approximately 1 . 5-kb DNA fragments , centered on AcPs from LNCaP , HCT116 or MCF7 cells , upstream of a luciferase reporter gene driven by the thymidine kinase ( TK ) minimal promoter . Enhancer activities of the fragments were determined by transient transfection and luciferase assays in LNCaP & PC3 ( prostate cancer cells ) , HCT116 & COLO 205 ( colorectal cancer cells ) and MCF7 ( breast cancer cells ) ( Figure 4 ) . AcP6 ( in the breast cancer risk region ) and AcP10 ( in prostate cancer/colorectal cancer risk region 3 ) had the most pronounced enhancer activities , whereas AcPs12 – 15 ( in prostate cancer risk region 1 ) had activities that were lower , but clear compared to the negative control and several other AcPs . Interestingly , these active enhancers also displayed unmistakable H3K4me1 and H3K4me3 marks . The results suggest that some of the active chromatin foci we identified ( Figure 3 , right inset ) have intrinsic enhancer activities within cellular contexts . This concept was further supported in a parallel study , in colorectal cells , which demonstrated that region 3 , encompassing AcP10 and harboring SNP rs6983267 , bound transcription factor T-cell factor 4 ( TCF4 ) in an allele specific manner [17] . In the present study we did not study this region but rather analyzed region 1 further in prostate cancer cells . Risk region 1 is specifically linked to prostate cancer risk , and the three most robust acetylation peaks also exhibited strong AR binding ( Figure 3 ) . This region additionally exhibited both active marks ( H3K4me1&3 , found at active TSSs and enhancers ) and inactive marks ( H3K27me3 , found throughout silenced genes and some intergenic regions ) , as well as occupancy of RNAPII . Further analysis of potential AR-mediated enhancers was strongly justified considering the major involvement of AR in all phases of prostate cancer development , including advanced ablation-resistant disease [18] . Consequently , we investigated the potential for androgen-dependent enhancer activities in this region . First , to verify and further characterize the AR binding at AcPs13 , -14 & -15 as suggested by ChIP-chip ( Figure 3 ) , site-specific ChIP analyses were conducted using cells treated with dihydrotestosterone ( DHT ) or vehicle ( Figure 5A ) . All three sites , in particular AcPs 14 & 15 , revealed strong DHT-stimulated AR occupancy . Second , to test directly for androgen-dependent enhancer activities , we cloned narrower regions ( ∼0 . 5-kb fragments ) than the original AcP regions ( which were ∼1 . 5-kb in length ) , centered around the AR occupancy peaks in the same TK-luciferase reporter plasmid described above , named AROR13 , -14 & -15 , respectively . LNCaP cells , which express AR , were transfected with these plasmids and luciferase activity was measured . The results revealed robust DHT-dependent enhancer activity in AROR14 and -15 , even higher than that of the PSA enhancer used as a positive control ( Figure 5B ) , and this level of activity roughly correlated with the DHT-stimulated AR occupancies at the respective sites ( Figure 5A ) . AROR-14 exhibited a remarkable basal activity but only a 3-fold response to DHT . In order to capture all common genetic variations in this region , we resequenced ARORs14 and -15 in prostate cancer cases of European ancestry ( 172 chromosomes ) . Through this effort we identified two SNPs in AROR15 that were strongly correlated with the risk variant rs10090154 ( reported in [4] ) , which itself was not located within an AROR ( rs11986220 , r2 = 1 . 0 and rs11988857 , r2 = 0 . 923; Figure 5C ) . We introduced all allelic combinations of both SNPs into the AROR15 reporter , creating 4 plasmids representative of the 4 alleles as shown in Figure 5D . In six independent experiments , using six independently constructed sets of plasmids , the DHT-dependent enhancer activity observed with the A-allele of rs11986220 was ∼2-fold higher than the enhancer activity observed with the T allele , regardless of the SNP at rs11988857 . Since the A allele at rs11986220 is also the allele associated with the risk allele for prostate cancer at rs10090154 , these results suggest that the increased androgen-mediated activity of the enhancer may upregulate expression of an important oncogene in prostate epithelial cells . What are the mechanism ( s ) that govern the SNP effect on the DHT-mediated enhancer activity described above ? Interestingly , the SNP at rs11986220 resides within a putative binding site for forkhead transcription factors , with the A allele better matching the consensus sequence ( Figure S4 ) . An interesting and relevant forkhead transcription factor is FoxA1 , which has been implicated in augmenting responsiveness of some ARORs to androgens [12] , [19] . Although LNCaP cells are homozygous for the T allele at rs11986220 , the physical presence of FoxA1 at the AROR15 enhancer was nevertheless demonstrated by site-specific ChIP analysis ( Figure 5E ) . Importantly , this occupancy was enhanced by DHT treatment of the cells . In a competition electromobility shift assay ( Figure 5F ) , an oligonucleotide centered around SNP rs11986220 competed better for FoxA1 binding to a consensus Fox oligonucleotide , when the SNP position was an A as compared to a T . Thus , the stronger DHT-responsiveness of the AROR15 enhancer observed with the A SNP at rs11986220 is attributable to higher affinity for the AR collaborator , FoxA1 . Since the histone acetyl transferase and transcriptional coactivator p300 accurately predicts enhancer activity at many loci [20] , we evaluated p300 occupancy at AROR15 by site-directed ChIP in LNCaP cells . As can be seen in Figure S5 , robust occupancy of p300 was observed , providing independent evidence for the likelihood of strong in vivo enhancer activity from this region . To follow up on our functional assays , we next genotyped rs11986220 in prostate cancer cases and controls from five ethnic populations in the Multiethnic Cohort ( 2 , 261 cases and 2 , 052 controls ) . The frequency of the A allele and the magnitude of the association was the same as those of the T allele of rs10090154 ( the index signal ) in European Americans , Latinos , Native Hawaiians and Japanese ( Table 1 ) , but not in African Americans . In this population group the A allele was less common than the T-allele of rs10090154 ( risk allele frequency: 0 . 06 vs . 0 . 16 , respectively ) . The association with rs11986220 was marginally stronger than rs10090154 in African Americans and when modeled concurrently in the pooled sample , rs11986220 remained nominally significant ( OR , 1 . 39; 95% CI , 1 . 06–1 . 84; p = 0 . 02 ) , whereas rs10090154 did not ( p = 0 . 18 ) , suggesting that rs11986220 better captures the effect of the functional allele at this locus ( and may be the biologically relevant allele ) . A main question remaining is what are the gene targets of our identified enhancers ? We suspect that they loop to their target ( s ) at some distance . Such looping in the three dimensional space of the nucleus may represent the underlying mechanism of transcriptional regulation [21] . Looping to an RNA synthesizing hub may establish coordinated control of systematic gene expression subject to cell lineage phenotypes that may include predisposition to cancer in particular cell types [22] . Various approaches will likely be necessary to identify the genes through which the risk variants act . For the 8q24 risk loci , the MYC gene is a strong candidate and must be fully considered . Future experiments must address this and whether the region 1 enhancers characterized in the present study interact with the MYC locus as was recently demonstrated for region 3 in colon cancer [17] . Since transcript abundance is a heritable trait , associations between risk allele status and mRNA transcript levels can serve as a powerful way to evaluate potential candidate genes . Recently , our group studied the association between 6 prostate cancer risk alleles at the 8q24 locus and MYC mRNA expression in prostate tissue . A large number of specimens ( 280 ) were evaluated ( across both normal and tumor prostate tissue ) and no association was observed [14] . One reason may be that this type of analysis only captures basal steady state levels of MYC; perhaps differences in MYC expression are apparent only under rare conditions when MYC is stimulated or during specific developmental stages [23] . With the advent of genome-wide associations of alleles with major diseases , the challenge of characterizing the biological function that is associated with the genomic region of interest is becoming more acute than ever . This challenge is particularly difficult when risk alleles are not located near annotated genes . We need to establish methodologies that can comprehensively and rapidly characterize the main genomic features in a region of interest , which can then be used to lay the foundation for follow-up studies that may lead to the uncovering of disease mechanisms . Here we have shown how the combination of high-density tiling arrays , transcript and epigenetic profiling , and computational analysis can facilitate functional characterizations , which may be tested directly with molecular biology techniques . Accordingly , we used the above-mentioned approach to identify how prostate cancer risk SNPs may affect enhancer activity at the gene-poor 8q24 region . Our chromatin analyses narrowed the location of putative functional domains to regions less than 1 . 5-kb in size , containing gene enhancers that may influence cancer risk via regulation of gene expression at a distance . We verified that gene regulation is involved by using reporter assays and further showed that the androgen-responsive activity of a strong enhancer in region 1 is affected by a SNP ( rs11986220 ) associated with prostate cancer risk .
LNCaP and PC3 cells were maintained in RPMI 1640 supplemented with 5% ( v/v ) fetal bovine serum ( FBS ) . HCT 116 and COLO 205 cells were cultured in McCoy's 5A with 10% FBS , and MCF7 cells were cultured in DMEM with 10% FBS . All cell lines were obtained from the American Type Culture Collection ( ATCC; Manassas , VA ) , except PC3 cells , originally from ATCC , which were derived by us as strongly AR-transcriptionally competent , although not expressing functional AR [24] . ChIP analyses were performed as described previously [25] . DNA samples from ChIP preparations were analyzed by qPCR using TaqMan PCR Master Mix ( Applied Biosystems , Branchburg , NJ ) . The primers and probes are listed in Table S1 . For ChIP-chip analyses , ChIP DNA and input DNA were purified using MinElute PCR Purification Kit ( Qiagen ) , and then amplified using the Whole Genome Amplification ( WGA ) Kit ( Sigma ) . Nimblegen Systems , Inc . performed the labeling and hybridization to a high-density custom array using standard procedures . We selected unique array probes to cover all non-repetitive sequence in and around the 8q24 risk loci ( chr8:125M–130M ) within 5-bps resolution on average . Antibodies used were anti-AcH3-K9/K14 ( 06-599 , Upstate ) , anti-H3K27me3 ( 07-449 , Upstate ) . anti-H3k4me1 ( ab8895 , Abcam ) , anti-H3K4me3 ( ab8580 , Abcam ) , H3k36me3 ( ab9050 , Abcam ) , anti-RNAPII ( sc-9001 , Santa Cruz ) , anti-AR ( N20 ) ( sc-816 , Santa Cruz ) , anti-FoxA1 ( sc-22841 , Santa Cruz ) and normal rabbit IgG ( sc-2027 , Santa Cruz ) . About 100 µg total RNA was extracted from each cell line ( LNCaP , PC3 , HCT116 , and MCF7 ) using Aurum Total RNA Kit ( Bio-Rad ) . Ribosomal RNA ( rRNA ) was depleted using RiboMinus Transcriptome Isolation Kit ( Invitrogen ) according to manufacturer's protocol . About 5 µg double strand cDNA was made from recovered RNA after rRNA depletion using Superscript Double-Stranded cDNA Synthesis Kit ( Invitrogen ) , and then submitted to NimbleGen , Inc . along with 5 µg sonicated genomic DNA ( size between 500–2000 bp ) from each cell line as a reference . The DNA samples were labeled and hybridized to the same custom tiling array used in ChIP-chip . Twenty fresh frozen radical prostatectomy ( RP ) samples were derived from an institutional review board-approved study cohort at Dana-Farber Cancer Institute ( DFCI ) [26] . Patients underwent RP between 2001 and 2005 . Five-micron sections of each RP specimen were reviewed by a pathologist to confirm the diagnoses of prostatic adenocarcinoma . Areas of tumor were selected where >60% of cells consisted of tumor cells . Areas of benign tissue were selected where >50% of cells consisted of non-neoplastic epithelium and were at least 5 mm away from any area of tumor focus . From these areas , two 2 mm punch biopsy cores of frozen tissue were processed for DNA and RNA extraction using a modified Qiagen Allprep DNA/RNA protocol . Double-stranded cDNA synthesis from RNA was performed using the Promega ImProm-II kit . Resulting cDNA from benign RP tissue and corresponding genomic DNA were hybridized to the tiling array described above . Array data were normalized by estimating the curve of log channels intensity ratio [log ( cy5/cy3 ) ] as a function of the control channel [usually log ( cy3 ) ] . This curve was subtracted from the observed log intensity ratio and used in subsequent analysis . We also computed the effect of variable probes' G+C content on the array binding ratios , but decided not to use this for further normalization due to the relatively small region covered by the array and the danger of systematic biases . We note that the RNA hybridization readouts were distributed in a highly non-normal fashion , with a significant fraction of the probes covering intronic regions and having higher than average readouts , and a smaller fraction of the probes covering exonic regions that are highly enriched . Since the study focused on a very high resolution mapping of a region that include only a few genes , we decided not to explicitly model RNA data as the product of some putative exonic structure , but focused on an unsupervised analysis of a combined dataset that included both RNA and ChIP data . The spatial clustering algorithm is an unsupervised hidden markov model ( HMM ) -based method that identifies a set of common pattern in multi-dimensional data that is defined over contiguous genomic segments . The method uses a probabilistic model describing a set of states ( clusters in this case ) and the probability of transitioning to a particular state Y given that one is presently at state X ( self transitions from X to X are allowed ) . Each cluster defines a distribution of values for the measured data tracks and an algorithm assigns each data instance ( the measurements for each track at a given locus in the genome ) to the cluster that describes it best . The algorithm iteratively updates the distributions defined by the clusters , the data points assigned to the clusters , and the transition probabilities from one cluster to another , until all data points are assigned to clusters that describe them well and which are highly likely to self-transition . This last property ensures that data points representing adjacent regions in the genome are likely to belong to the same cluster , maintaining the biological tendency of contiguous genomic regions to behave similarly . To dissect the 8q24 into regions with distinct epigenomic behaviors we used our recently described implementation of the algorithm [15] , [16] in a non-hierarchical mode , with a 12-cluster model and assuming data is distributed normally once the cluster is known . Other selections of model structure generated similar results . Due to the limited size of the analyzed region we did not try to use the model to define coupling between clusters or higher-level organizational behaviors . In Figure 2 , we report only on clusters that were defined as informative , containing at least one genomic track with significantly high or low mean , as other clusters represent statistical variants of background signals and are routinely ignored . Fifteen enhancer candidates ( ∼1500-bp sequence surrounding the AcH3 peak center ) and three ARORs ( ∼500 bp sequence surrounding the AROR peak center ) were amplified from LNCaP genomic DNA using High Fidelity Platinum Tag DNA polymerase ( Invitrogen ) . The amplified sequences were then subcloned in either the KpnI or Sac II restriction sites upstream of a thymidine kinase ( TK ) minimal promoter-firefly-luciferase vector in both directions . All clones were confirmed by sequencing . The primers for subcloning are listed in Table S1 . LNCaP , PC3 , HCT116 , COLO 205 , and MCF7 cells were transfected with reporter plasmids along with constitutively active pRL-TK Renilla luciferase plasmid ( Promega ) using Lipofectamine LTX Reagent ( Invitrogen ) according to the manufacturer's protocol . Dual luciferase activities were measured as previously described [25] . For DHT-mediated enhancer activities of ARORs , LNCaP cells were transfected with AROR containing TK-luciferase reporter plasmids . After transfection , cells were treated with DHT ( 10 nM ) or ethanol vehicle for 24 h . Where indicated , point mutations were introduced to create enhancer-reporter constructs with specific SNP alleles using QuikChange site-directed mutagenesis kit ( Stratagene ) . In these cases , six independent clones of each construct were made , and confirmed by sequencing . DHT-mediated fold activities are presented and values are means±SD of the six independent clones of each allele . For each clone average values of three independent transfections were used . Two-side p-values between alleles were calculated using the student t-test . Whole cell extracts were prepared from LNCaP cells , cultured in 5% FBS RPMI 1640 , and EMSA was performed all as previously described [27] . Oligonucleotides ( Table S1 ) and anti-FoxA1 antibody ( ab 23738 , Abcam ) were used as indicated . | Genome-wide scans of inherited genetic variation in the normal population have recently identified many sites ( loci ) associated with the predisposition to complex diseases such as cancer . Some of these cancer-associated loci , however , are devoid of genes ( situated in so-called “gene deserts” ) and the mechanism ( s ) of the association are not readily apparent . In the work reported here , we show that loci associated with several cancers in a gene desert found at chromosomal area 8q24 have embedded regulatory sequences affecting gene expression as enhancers , and in one case this activity is modulated by genetic variation . The results provide insight into the mechanism ( s ) governing genetic cancer risk . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/epigenetics",
"genetics",
"and",
"genomics/cancer",
"genetics",
"genetics",
"and",
"genomics/functional",
"genomics"
] | 2009 | Functional Enhancers at the Gene-Poor 8q24 Cancer-Linked Locus |
Community-acquired ( CA ) Staphylococcus aureus cause various diseases even in healthy individuals . Enhanced virulence of CA-strains is partly attributed to increased production of toxins such as phenol-soluble modulins ( PSM ) . The pathogen is internalized efficiently by mammalian host cells and intracellular S . aureus has recently been shown to contribute to disease . Upon internalization , cytotoxic S . aureus strains can disrupt phagosomal membranes and kill host cells in a PSM-dependent manner . However , PSM are not sufficient for these processes . Here we screened for factors required for intracellular S . aureus virulence . We infected escape reporter host cells with strains from an established transposon mutant library and detected phagosomal escape rates using automated microscopy . We thereby , among other factors , identified a non-ribosomal peptide synthetase ( NRPS ) to be required for efficient phagosomal escape and intracellular survival of S . aureus as well as induction of host cell death . By genetic complementation as well as supplementation with the synthetic NRPS product , the cyclic dipeptide phevalin , wild-type phenotypes were restored . We further demonstrate that the NRPS is contributing to virulence in a mouse pneumonia model . Together , our data illustrate a hitherto unrecognized function of the S . aureus NRPS and its dipeptide product during S . aureus infection .
Staphylococcus aureus is a notorious human pathogen that can cause a variety of diseases and thus causes a dramatic disease burden and death toll especially in the hospital setting [1 , 2] . The advent of methicillin resistant S . aureus ( MRSA ) led to a loss of therapeutic options and in recent years a shift was observed from mostly healthcare-associated ( HA ) to community-acquired ( CA ) MRSA infections . CA-MRSA strains are epidemic and often cause serious infections in otherwise healthy individuals . Enhanced virulence of CA-MRSA has been attributed in part to increased production levels of toxins such as phenol-soluble modulins ( PSM ) [3 , 4 , 5] . Studies in recent years have shown that S . aureus is a facultative intracellular pathogen since it is not only internalized by professional but also by non-professional phagocytes ( e . g . reviewed in [6] ) . Further , intracellularity has recently been shown to foster dissemination of S . aureus in disease models [7] . Following internalization S . aureus is capable of avoiding destruction within the phagolysosome by disruption of the endosomal membranes [8 , 9 , 10 , 11 , 12] . S . aureus disrupts phagosomal membranes using PSMs [9 , 12] and readily kills host cells in a PSM-dependent manner [13 , 14 , 15] . However , PSMs are not sufficient to mediate these processes [12] . Further , in phagocytes S . aureus has been reported to grow inside mature phagolysosomes and is able to kill host cells without prior translocation to the host cytoplasm [16 , 17] . In order to identify additional genes involved in intracellular survival of S . aureus , we sought to identify MRSA mutants within an established transposon mutant library [18] that were not able to escape the phagosome upon internalization by host cells . We detected phagosomal escape rates by automated fluorescence microscopy using transgenic escape reporter cells . These cells express a fluorescent marker which is recruited to the staphylococcal cell wall once the bacteria are located within the host cell cytoplasm [9 , 12] . Among others , we thereby identified two genes , ausA and B , that were required for efficient escape . Both genes are arranged in a bicistronic operon and encode subunits of the non-ribosomal peptide synthase ( NRPS ) AusA and a phosphopantetheinyl transferase AusB , which is involved in AusA activation[19] . An initially described virulence-regulatory function of the NRPS [20] was later found to be caused by a secondary site mutation and NRPS involvement in virulence was contested [21] . The NRPS AusAB produces three cyclic dipeptides named phevalin , tyrvalin , and leuvalin [19] which belong to the class of monoketopiperazines or pyrazinones . Non-ribosomally synthesized peptides are widely distributed among a variety of bacteria species , where they assume functions as antibiotics , siderophores or toxins [22 , 23 , 24] . Notably , the major product of the NRPS , phevalin , was initially identified in Streptomyces spp . in a screen for calpain inhibitors [25] . While the NRPS/phevalin function in staphylococcal virulence is debated [20 , 21] , it has been demonstrated to exert effects on host cells . In keratinocytes , addition of pure phevalin or staphylococcal supernatants containing the substance resulted in transcriptional changes which illustrate that a host cell target of the cyclic dipeptide exists [26] . Here we show that supplementation with the cyclic dipeptide phevalin[20 , 25] as well as genetic complementation of a insertional mutant of ausB restored escape in epithelial cell lines and intracellular cytotoxicity of MRSA in epithelial cells as well as human and murine primary macrophages , and that phevalin plays a role in in a pneumonia infection model . Together , our data illustrate a hitherto unrecognized function of the S . aureus NRPS AusAB and its dipeptide product , phevalin , during intracellular S . aureus infection , host-pathogen interaction and in a mouse pneumonia model .
We assessed phagosomal escape of S . aureus with a transgenic HeLa cell line expressing a fluorescent reporter protein , YFP-CWT , which results in the recruitment of the cytoplasmic fluorophore to the staphylococcal cell wall once the endosomal membrane barrier is breached[9 , 12 , 27] . Single gene mutants in the CA-MRSA strain JE2 were obtained from the Nebraska transposon mutant library[18] . We infected the reporter cell line with fluorescently labeled bacteria in multi-well dishes . Four hours post-infection we fixed the samples and compared escape rates of each mutant with that of wild type bacteria by automated microscopy ( S1a Fig ) . We investigated relative escape rates for a set of mutants in known and potential virulence factors and regulators ( S4 Table ) . Whereas most mutants demonstrated only small differences in escape ( ±20% of the wild-type ) , we identified several that showed a drastic decrease in phagosomal escape four hours post-infection ( Fig 1a ) . As expected , these included mutants within the quorum sensing system genes agrA , B , and C ( relative escape rates: 6 . 4 ± 4 . 4% , 15 . 8 ± 1 . 4% , and 13 . 2 ± 8 . 8% , respectively ) , as well as the PSM transporter gene pmtC ( 29 . 3± 18 . 3% ) . Similarly , rsbU and rsbW ( 53 . 3 ± 24 . 8% and 25 . 6 ± 9 . 2% , respectively ) , whose products are involved in regulation of the alternative sigma factor σB , the non-functional homolog of type I signal peptidase spsA ( 48 . 8 ± 11 . 5% ) , as well as the leukocidin subunit lukA ( 35 . 9 ± 13 . 7% ) demonstrated involvement in phagosomal escape ( Fig 1a and S4 Table ) . lukA encodes a subunit of the pore-forming toxin ( PFT ) LukAB ( LukGH ) , which has been implicated in neutrophil cell death[15 , 28] , thus may also possess an intracellular target in epithelial cells . rsbU and rsbW[29] are modulators of the alternative sigma factor σB , which was shown to be required for adaptation of S . aureus to an intracellular environment[30] . SpsA is known as “non-functional”type I signal peptidase[31] , although our assay demonstrates a contribution of SpsA to phagosomal escape . Further experiments will be required to determine the mode of action of LukA and SpsA . Interestingly , both genes of the ausAB operon , which encodes a non-ribosomal peptide synthetase ( NRPS ) [19] were also identified in our screen . For the ausA and ausB mutant relative phagosomal escape rates were reduced to 47 . 3 ± 12 . 4% and 52 . 4 ± 11 . 7% , respectively ( Fig 1a ) . Since the contribution of aureusimines to S . aureus-induced disease is debated [20 , 21] , we more closely investigated the ausAB operon . By Ultra Performance Liquid Chromatography ( UPLC ) we demonstrated the absence of NRPS products , aureusimine A ( tyrvalin ) and B ( phevalin ) , in bacterial culture supernatants of ausA and ausB mutants , respectively ( Fig 1b and S2a Fig ) . A plasmid constitutively expressing the AusB phosphopantetheinyl transferase complemented the ausB mutant as evidenced by the production of the NRPS products , tyrvalin and phevalin ( Fig 1b ) , as well as restoration of the escape phenotype in HeLa ( Fig 1c ) . These observations were independent of the host cell type used , since the same results were obtained within an upper airway epithelial cell line ( S1c Fig ) . Next , we determined that external addition of synthetic phevalin to tissue culture medium restored escape of intracellular aus mutants ( Fig 2a ) . Interestingly , escape rates of the wild type were also enhanced by supplementation of external phevalin ( Fig 2a ) . Externally added phevalin was readily detected by UPLC after thorough washing of host cells , thus demonstrating its cellular association ( S2b Fig ) . Moreover , we detected phevalin produced by intracellular wild type bacteria four hours after infection ( S2c Fig ) . Transcription analysis of the aus operon corroborated the agr dependency of ausA [32] . By contrast , ausB , seems to be agr-independently up-regulated during stationary phase ( P = 0 . 04 ) suggesting alternative transcription initiation sites might exists ( S6 Fig ) [33] . Phevalin treatment of host cells does not cause non-specific rupture of endosomes , since formaldehyde-fixed S . aureus did not translocate to the cytoplasm of reporter cells , regardless of phevalin supplementation ( S3a Fig ) . Thus our data demonstrate that phevalin enhances phagosomal escape of viable S . aureus , compatible with the observation that toxins , such as phenol-soluble modulins , are required for translocation of the bacteria to the host cell cytosol . Phevalin was initially discovered in a screen for calpain inhibitors in a pool of natural products of Streptomyces[25] . Calpains , a family of calcium-activated cysteine proteases ubiquitously present in eukaryotic cells , are known to modulate lysosomal integrity[34] . We therefore investigated , if phevalin activity in phagosomal escape is connected to an inhibition of host cell calpains . We compared Calpain inhibitor I ( Calp I ) and Calpeptin to phevalin and solvent control in phagosomal escape assays . Whereas Calp I and Calpeptin significantly reduced phagosomal escape of S . aureus , phevalin treatment rather increased the rate of phagosomal escape ( Fig 2b ) . This indicated i ) an involvement of the host calpain proteases in translocation of S . aureus to the cytoplasm and ii ) a mode of action for phevalin distinct from calpain inhibition . Phagosomal escape of S . aureus is intimately linked to subsequent induction of host cell death in epithelial cells [12] . We therefore infected epithelial cells with S . aureus wild type , isogenic ausA and ausB mutants , a complemented ausB mutant , as well as mutants within the global regulators agrA and saeR and compared their kinetics of cytotoxicity to uninfected cells by propidium iodide staining and flow cytometry . The agrA mutant did not demonstrate significant detectable host cell death over the course of infection , and saeR , ausA and ausB mutants were significantly diminished in their ability to kill host cells . The ausB deficiency was readily complemented in trans ( Fig 3a ) . Therefore , the absence of aureusimines resulted in drastically attenuated cytotoxicity of intracellular S . aureus . Since culture supernatants of wild-type and mutant bacteria induced similar levels of cell death ( S5 Fig ) and hemolysis was comparable ( S7 Fig ) , we concluded that phevalin-dependent effects on epithelial cell death originated from intracellular bacteria . During bacterial infections , neutrophils are usually the first cells recruited to the site of infection where they get activated and ingest bacteria . Since S . aureus has evolved mechanisms to evade neutrophils[35] , we tested neutrophil killing by the pathogen . At 4 hours p . i . PMN cell death was significantly decreased in the aus mutants when compared to either wild type or the complemented ausB strain ( Fig 3b ) . Similarly , primary human ( Fig 3c and S5b Fig ) and murine ( S5c and S5d Fig ) macrophages were killed in an NRPS-dependent manner even at low multiplicities of infection ( MOI ) of 5 . Bacterial replication in neutrophils was not affected by insertional inactivation within the aus operon , since CFUs increased between 1 and 4 hours after infecting PMN regardless of the strain background ( S8 Fig ) . By contrast , ausA and ausB mutants were as susceptible to killing by macrophages as the agr mutant: recovered CFU counts of mutants in ausA or ausB diminished over the course of co-incubation with macrophages , whereas CFUs of wild-type ( WT ) , complemented ausB mutant ( ausB compl ) , and the ausA mutant supplemented with 10 μM phevalin ( ausA+Phe ) demonstrated increasing CFU values over the course of the experiment . By contrast , phevalin supplementation of the agrA mutant did not lead to increased CFU recovery ( Fig 3d ) , thereby supporting the additional requirement of bacterial toxins for intracellular survival . As bacterial replication in neutrophils was not affected by ausAB mutations , we next investigated the effects of synthetic phevalin on activation of primary human neutrophils and assayed by flow cytometry the calcium flux , a hallmark of PMN activation . Interestingly , pre-incubation of PMN with phevalin led to a significant decrease of calcium flux when the neutrophils were challenged with formyl peptide receptor ( FPR ) 1 agonist fMLF , the FPR2 agonist MMK , or bacterial supernatants irrespective of the presence of phenol-soluble modulins ( Fig 4 ) . Formyl peptide receptors 1 and 2 are G-protein-coupled receptors on a variety of cells such as innate immune cells . Triggering of either FPR can activate the cells . Since phevalin addition inhibits neutrophil activation regardless of the addition of either specific agonist ( MMK1 and fMLP , respectively ) and since phevalin is active on the host cell level in epithelial cells which do not express FPRs , we reasoned that the inhibition of neutrophil activation might take place on the level of the downstream signaling cascade . Further support for the importance of ausAB in virulence of S . aureus was provided by a transposon insertion site ( TIS ) sequencing screen in an animal model of pneumonia . We infected mice intranasally with a pool of transposon mutants of S . aureus 6850[36] and recovered bacteria from animal lungs one day after infection ( S9a Fig ) . A transposon within ausA ( insertion site at nucleotide 149 , 611; GenBank Accession NC_007793 ) demonstrated a 2−2 . 7 fold decrease of read frequencies in the recovered fraction when compared to the inoculum ( per site analysis: P = 0 . 00025 , adj . P = 0 . 024; per gene analysis 2−2 . 15 fold change; adjusted P = 0 . 093; S5 Table; Fig 5a ) . Mutants within guaA , purR , or purM , other genes that had been previously shown to be essential for S . aureus survival in mouse models of infection , were also underrepresented in the lung samples ( S5 Table ) . Upon recovery of the output library we compared its complexity and composition of that of the input , which were found to be highly correlated ( adjusted R2 = 0 . 92 , S9b Fig ) . In all inocula , we consistently recovered insertion sites from 1640 genes out of 2559 genes annotated for S . aureus 6850 [37] . The 919 discrepant genes might include essential genes and some lost due to minor technical bottlenecks arising from dilution of inoculum during infection ( S9c Fig ) . We therefore excluded a major bottleneck effect to account for the depletion as well as growth defects of aus mutants to account for the differential recovery of mutant and wild type bacteria . Taking into account the 1640 consistently recovered gene mutants , we analyzed the differences in reads from individual mutants between the inoculum library and the recovered bacteria . Of these 123 were decreased in transposon insertion site ( TIS ) read abundance including ausA ( S9d Fig and S5 Table ) . Moreover , within this list we found the non-coding RNA SSR42 , a recently identified virulence regulatory RNA which was shown to be important for toxin production and virulence [36 , 38] . Since we excluded growth defects of aus mutants to account for the differential recovery of mutant and wild type bacteria ( S9e Fig ) , our data suggested that the aus operon plays a distinct role in S . aureus-induced pneumonia and is required for efficient survival of the bacteria in mouse lungs . We hence intranasally instilled mice with wild-type bacteria , ausB mutants as well as the ausB complementation strain and recorded disease activity index ( DAI; S6 Table ) over 48 hours of infection ( Fig 5b ) . We observed a linear increase in disease severity over the first 24 hours post infection for all investigated strains . At the 24 hour time point , mice infected with the ausB mutant started to recover , which is supported by significant changes in DAI time course ( P = 0 . 0213 ) as was established by a linear statistical model . After 48 hours , the mice were sacrificed and bacterial CFU from the tissue were determined by plating dilutions of tissue lysate ( S10 Fig ) . Interestingly , the three strains were recovered at similar CFUs . We therefore performed a competition experiment during which mice were infected with a 1:1 mixture of the wild-type strain as well as the insertional ausB mutant . Wild-type S . aureus outcompeted the mutants as was indicated by a slightly , but significantly reduced retrieval of mutants when compared to the total inoculum ( 43 . 55% of total CFU , P = 0 . 037; Fig 5c ) and therefore a competitive index ( CI ) that differed significantly , if slightly , from 1 ( CI = 0 . 96; P = 0 . 0446; Fig 5d ) . These minute changes illustrate that the NRPS AusAB and its dipeptide product phevalin are possibly not major virulence factors of S . aureus during lung infection and thus support previous findings [21] . However , our results illustrate that the production of aureusimines may tip the balance in the tug-of-war between the pathogen and cellular host defenses in some cases ( Fig 5b ) : for instance , we demonstrate that in epithelial cell lines , phevalin is involved in phagosomal escape of the pathogen . The phevalin dependency of cell death in macrophages and neutrophils suggests an additional function of the dipeptide in these immune cells and we are currently actively investigating the potential mode of action of phevalin and if in these cells phagosomal escape and host cell death are linked[12] . Since recent studies suggests that phagocytes are killed by S . aureus without prior translocation to the host cell cytoplasm [16 , 17] and not all cell types succumb to intracellular S . aureus [39] multiple , potentially cell- or tissue-specific intracellular survival strategies of S . aureus and molecular mechanisms of phevalin activity may exist . Previously , the contribution of aureusimines to S . aureus-induced disease was unclear: the initial phenotypes observed by Wyatt et al . [20] were attributable to a secondary site mutation within the virulence regulator SaeRS[21] . In an isogenic background , however , evidence of virulence in infection models was lacking[21] . Both studies used S . aureus Newman , a strain lacking covalent cell wall anchorage of the main adhesins , Fibronectin-binding proteins A and B . S . aureus Newman thus hardly invades epithelial cells[40] . However , our data show that intracellularity of S . aureus is required to cause observable differences in phevalin-dependent phagosomal escape and subsequent epithelial cell death , whereas culture supernatants of wild type and mutant bacteria do not show profound differences in cytotoxicity . This also explains the importance of AusAB in the pneumonia model , in which the pathogen primarily encounters phagocytic cells such as alveolar macrophages and infiltrating neutrophils[41 , 42] . Altered pathogenesis of aus mutants in the animal lung is not caused by α-toxin , a main virulence factor in S . aureus-induced pneumonia[43] , since it is not differentially regulated in aus-deficient mutants[20] . Further , α-toxin is not involved in phagosomal escape of host cells[44] ( S4 Table ) . Interestingly , mere addition of synthetic phevalin inhibited neutrophil activation independent of FPR receptors , thereby suggesting a host target of the pyrazinone . During phagosomal escape phevalin targets a host cellular function which is independent of calpain , although phevalin was first described to comprise a calpain inhibitor[25] . Host targets of phevalin are further supported by a study in which transcriptional changes upon phevalin treatment were observed in human keratinocytes[26] . S . aureus uses several small molecule products for virulence: the carotenoid staphyloxanthin is used to scavenge reactive-oxygen species , siderophores are important for iron acquisition and an autoinducing peptide pheromone is instrumental in quorum sensing . Aureusimines , cyclic dipeptides formed by a non-ribosomal multi-domain assembly line[45] , are small molecule virulence modulators . Whereas such peptides often act as antibiotics and in interspecies competition , our data suggests that S . aureus exploits non-ribosomal dipeptides for an inter-kingdom modulation of host-pathogen interactions in order to survive within the mammalian host .
Escherichia coli was grown in Luria-Bertani broth ( LB; Oxoid ) . Staphylococcus aureus strains were routinely grown on Trypticase soy agar ( TSA ) or in Trypticase soy broth ( TSB ) supplemented with glucose ( Oxoid ) . Media were supplemented with antibiotics where appropriate . For assessment of hemolysis S . aureus was grown on Columbia blood agar base ( Oxoid ) supplemented with 5% defibrinated sheep blood ( Fiebig Nährstofftechnik , Germany ) . Bacterial growth curves were determined with a TECAN infinite Pro 200 plate reader . Triplicates of each culture were used to inoculate 400 μL TSB to OD600 0 . 1 and were grown up to 20 hours in a 48 micro well plate at 37°C with shaking at 180 rpm . Absorbance at 600 nm was recorded in 10 minute intervals . The plasmid p0182 was generated for genetic complementation of the insertional mutant in ausB , NE964 . Briefly , the constitutively active promoter SarAP1 was amplified from S . aureus gDNA by the oligonucleotides SarAP1-F and SarAP1-R . The PCR product was TA-cloned and sequence verified . The SalI/KpnI Fragment with the promoter was generated by a restriction digest and inserted into accordingly opened pmRFPmars [46] resulting in pSarAP1-mRFP . The open reading frame of ausB ( S . aureus USA300_FPR3757; GenBank Accession number NC_07793; Locus ID SAUSA300_00182 ) was amplified from genomic DNA of S . aureus JE2 using the oligonucleotides 0182_for and 0182_rev ( S6 Table ) . The 672 kb insert was cloned in pCR2 . 1 TOPO TA vector ( Invitrogen ) , transformed and propagated in E . coli DH5α . The sequence of ausB was verified by Sanger sequencing ( SeqLab , Göttingen ) . A 669 bp DNA fragment was prepared by restriction and the purified fragment was ligated in the accordingly treated vector pSarAP1-mRFP . The vector was amplified in E . coli DH5α and subsequently was electroporated[47] into S . aureus RN4220[48] , a strain that readily accepts and methylates foreign DNA thereby allowing to bypass the restriction barrier of wild-type S . aureus . From S . aureus RN4220 the methylated plasmid DNA was re-isolated and electroporated into the target strain , S . aureus NE964 ( S4 Table ) . Selection for recombinant bacteria was performed by plating the cultures on TSA containing 10 μg/ml chloramphenicol . Synthetic aureusimine B ( Phevalin; CAS 170713-71-0 ) was obtained from SantaCruz Biotechnology ( Heidelberg , Germany; sc-362711 ) , dissolved in DMSO and stored in aliquots at -20°C . Titration of the biologically active phevalin concentration has to be performed for each new cell type . Phagosomal escape was examined as described previously with modifications[9 , 12] that allowed automation of microscopy . Briefly , HeLa cells ( HeLa 229 , ATCC CCL-2 . 1 ) stably expressing YFP-CWT escape reporter construct[12 , 27] were grown in 24 well plates ( ibidi μ-clear; #82406 ) in RPMI1640 medium ( Invitrogen ) supplemented with 10% FCS and 1 mM sodium pyruvate . The upper airway epithelial cell line S9 [49] was transduced with lentiviral particles stably integrating the escape marker YFP-CWT [12] and was cultivated in DMEM:F12 nut-mix supplemented with 10% FCS and penicillin/streptomycin ( 100 U/ml and 100 μg/ml , respectively ) . S . aureus cultures were grown overnight in TSB ( 37°C , 200 rpm ) . Cultures were diluted to an OD600 of 0 . 4 in 10 ml fresh TSB medium and incubated for another 1h at 37°C . Bacteria were harvested by centrifugation , labelled with 50 μg/ml TRITC ( mixed isomers; MoBiTec ) for 30 min at 37°C and were thoroughly washed to remove unbound dye . Bacteria were enumerated and used to infect HeLa at a multiplicity of infection ( MOI ) of 10 . After a one hour co-cultivation , extracellular bacteria were removed by a 30 min treatment ( 37°C ) with medium supplemented with 20 μg/ml lysostaphin ( AMBI , Lawrence , NY , USA ) and 100 μg/ml gentamicin ( Invitrogen ) . Lysostaphin/gentamicin medium was aspirated and replaced with tissue culture medium containing 100 μg/ml gentamicin . After additional incubation ( as indicated in text and figures ) the cells were washed with 1x PBS and subsequently fixed with 4% paraformaldehyde for 1 hour at room temperature or overnight at 4°C . Next , the samples were recorded with an Operetta Fluorescence Microscope ( Perkin Elmer ) . For each well 10 non-overlapping images ( each at 1360x1024 px; 675 . 3928 μm x 508 . 5311 μm ) were acquired with a 20 x PLAN long working distance objective ( NA 0 . 45 ) . TRITC fluorescence ( S . aureus ) was imaged with the filter set "StdOrange1/Cy3" filter set ( excitation: 520–550 nm , emission: 560–630 nm; 0 . 5 sec exposure ) . Fluorescence of the escape reporter YFP-CWT was recorded with the “SpBlue1/YFP" filter set ( excitation: 490–510 nm , emission: 520–560 nm; 0 . 75 s exposure ) . Image analysis was performed with the built-in software “Harmony” ( Perkin Elmer ) . Host cell cytoplasm and nuclei counts were identified by using the faint cytoplasmic fluorescence of YFP-CWT , which accumulates slightly within the nucleus of host cells . Within the area of host cell cytoplasm , bacteria as well as escape signals were detected and enumerated with “Find spots” in each of the recorded channels ( Method “A” , relative spot intensity 0 . 1 , Splitting coefficient 1 ) . The mean relative escape scores were represented as YFP/CY3 ratios . In all assays , the escape-proficient S . aureus JE2 and its escape-deficient isogenic agrA mutant served as positive and negative controls , respectively . Statistical significance was calculated by Student’s t-test with wild type as the reference . Bacteria culture samples were prepared after a previously established protocol[26]: 25 ml of TSB overnight culture of S . aureus was centrifuged at 4 , 000 rpm for 10 min at 4°C . Supernatant was collected and sterilized by passage through a 0 . 22 μm syringe filter . In glass tubes 2 ml of filtrate were mixed with an equal volume of 100% chloroform by vortexing . Samples were centrifuged at 3 , 000 g for 10 min at 4°C and the organic phase was transferred to a fresh glass tube . Solvent was evaporated in an extractor hood by a continuous flow through of pressurized air . The dried samples were resuspended in 100 μl of 20% DMSO . For detection of aureusimines from epithelial cells , HeLa cells were incubated either in presence of 20 μM phevalin or were infected with bacteria ( see above ) . At the time point of measurement cells were detached using trypsin/EDTA , 1 . 5 ml H2O was added and the sample was transferred to a glass reaction tube . Aureusimines were extracted as outlined above . Samples were analyzed by LC-MS/MS using a Waters Acquity ultra-high-performance liquid chromatography system coupled to a Waters Micromass Quattro Premier triple quadrupole mass spectrometer ( Milford , MA , USA ) equipped with an electrospray interface ( ESI ) . Aureusimines were separated by reversed-phase chromatography using an Acquity BEH C18 column ( 50 x 2 . 1 mm , 1 . 7 μm particle size with a 5 x 2 . 1 mm guard column; Waters; Milford , MA , USA ) and a solvent system consisting of water containing 0 . 1% formic acid ( solvent A ) and acetonitrile ( solvent B ) . The injection volume was 5 μL per sample . A gradient elution was performed at a flow rate of 0 . 25 mL min–1 starting from 1% to 100% solvent B within 5 min and a column temperature of 40°C . Aureusimines were detected by multiple reaction monitoring ( MRM ) , instrument parameters for ionization and collision induced dissociation ( CID ) were optimized by flow injection of phevalin . The electrospray source was operated in the positive electrospray mode ( ESI+ ) at a temperature of 120°C and a capillary voltage of 2 . 75 kV . The cone voltage ( CV ) was adjusted to 40 V and nitrogen was used as desolvation and cone gas with flow rates of 800 L h–1 at 400°C and 10 L h–1 , respectively . Fragmentation was carried out using argon as collision gas at a pressure of approximately 3 x 10–3 bar and a collision energy ( CE ) of 22 eV . For each compound , three specific fragments were monitored ( phevalin: m/z 229 > 214 , m/z 229 > 159 , m/z 229 > 81; tyrvalin: m/z 245 > 230 , m/z 245 > 175 , m/z 245 > 81 ) with a dwell time of 25 ms per MRM transition . HeLa 229 ( CCL-2 . 1 ) were obtained from ATCC . Cells were grown on 12 well plates in RPMI1640 medium ( Invitrogen ) supplemented with 10% FCS ( PAA ) , 1 mM sodium pyruvate ( Invitrogen ) . S . aureus cultures were grown overnight in TSB and the cultures were diluted to an OD600 of 0 . 4 in 10 ml fresh TSB medium and incubated for 1 h at 37°C ( 200 rpm ) . Bacteria were harvested by centrifugation , washed with PBS , resuspended in tissue culture medium and used for infection at a MOI of 10 . Extracellular bacteria were removed by lysostaphin/gentamicin treatment 1 hour post infection as outlined above and were further incubated for 3 hours . At 4 hours post infection , supernatants for each well were collected , cells were detached from the substratum using TrypLE Express ( Invitrogen ) , and trypsinization was stopped by re-addition of the previously collected culture supernatants . Cell suspensions were transferred to reaction tubes and cells were collected by centrifugation at 500 x g for 5 min . The cells were gently resuspended in FACS labelling solution ( 10 mM HEPES pH 7 . 4 , 140 mM NaCl , 5 mM CaCl2 , and 1% [v/v] propidium iodide ) . The reaction was incubated in the dark for 10 minutes at room temperature , cells were diluted 1:5 with labelling buffer , and analyzed immediately using an Accuri C6 flow cytometer ( BD ) . The wild type and non-infected HeLa cells were used as references to compare the cytotoxicity . The mean percentages of PI-positive events were plotted and statistical analysis was performed by fitting the values into a linear model , where the responses were time and infection groups . ANOVA and Tukey’s post hoc analysis were performed to assess individual differences . Primary human polymorphonuclear leukocytes ( PMNs ) were isolated from venous blood of healthy adult volunteers as described[50] , were harvested by centrifugation at 1 , 000 rpm for 5 minutes and resuspended in 1x Hank’s Balanced Salt Solution ( HBSS ) . Neutrophil killing by intracellular S . aureus was determined as described[51] . At indicated times post-infection neutrophils were pelleted by centrifugation . Supernatants were collected and lactate dehydrogenase ( LDH ) was measured using the Cytotoxicity Detection Kit PLUS ( Roche ) . To examine killing of S . aureus by neutrophils , PMN were lysed with sterile water ( pH 11 . 0 ) for 5 minutes . Serial dilutions of the bacterial suspension were plated on TSA and bacterial colonies were counted after overnight incubation at 37°C . Calcium fluxes in PMN were analyzed by loading the cells with Fluo-3-AM ( Molecular Probes ) and monitoring fluorescence with a FACScalibur flow cytometer ( Becton Dickinson ) as described[52] . The synthetic chemoattractants fMLF and MMK were used at final concentrations of 10 nM to stimulate calcium fluxes in PMN , while S . aureus culture supernatants of staphylococcal strains LAC [53] and the PSM-deficient mutant LAC Δαβδ [54] were used in the indicated concentrations . In order to determine a potential effect of the aureusimine phevalin on PMN calcium flux , cells were pre-incubated with concentrations of 5 or 10 μM for 30 min at RT prior to the experiments . Calcium flux of buffer control corrected samples was expressed as relative fluorescence units ( RFU ) from measurements of 2 , 000 events . Isolation and differentiation of primary human macrophages was performed as described before[55] . In brief , Peripheral blood mononuclear cells ( PBMCs ) were collected following centrifugation of whole blood collected from healthy individuals over a Ficoll-Paque ( GE Healthcare ) gradient at 200 x g for 30 min and washing with PBS containing 1 mM EDTA . Centrifugation at each step was performed for 10 min at 300 x g without brakes . PBMCs were resuspended in RPMI medium supplemented with 250 ng/ml phytohemagglutinin ( Sigma Aldrich ) , and incubated at 37°C and 5% CO2 for 24 to 36 hours . Monocytes were allowed to differentiate into macrophages for 6 days , in medium supplemented with 50 ng/ml MCS-F ( Affymetrix ) for the first 3 days followed by 100 ng/ml . Macrophages were infected with S . aureus JE2 wild type , ausA , ausB , agrA mutants as well as an ausB complemented strain at an MOI of 5 , to determine bacterial survival when challenged with macrophages . At each time point cells were washed once with PBS and lysed using alkaline water , pH 11 for 5 min at room temperature to release ingested bacteria . Dilution series of the complete cell lysate were plated on TSA to enumerate bacterial numbers . When cytotoxicity of internalized bacteria strains in macrophages was to be determined , macrophages were infected with an MOI of 20 . Following centrifugation at 1000 rpm for 10 min , phagocytosis was allowed for 30 min at 37°C and 5% CO2 . Medium was then exchanged for medium supplemented with 20 μg/ml Lysostaphin for 30 min to eradicate any residual extracellular bacteria . Medium was exchanged again for medium without antibiotics and cells were incubated at 37°C and 5% CO2 for another 60 min . At that time point 2 x 100 μl of medium were taken off to determine levels of lactate dehydrogenase ( LDH ) using the Cytotoxicity Detection Kit PLUS ( Roche ) . Desired S . aureus strains were revived from frozen stocks by plating on TSA with appropriate antibiotics whenever required . Colonies were picked and grown in TSB overnight at 37°C with shaking at 180 rpm in air . The overnight culture was transferred into a flask containing 50 ml TSB ( OD600 0 . 05 ) and incubated for 3 . 5 hours at 37°C with shaking at 180 rpm in air . The bacteria were harvested by centrifugation at 4°C and resuspended in 20 ml TSB containing 15% glycerol . The bacterial suspension was divided into 2 ml aliquots and stored at -80°C until use . The bacterial stocks were quantified for CFUs and also titrated for lethal and sub-lethal dosage for infection in mice . For infections bacteria from glycerol stocks were thawed , transferred to pre-warmed 50 ml TSB media and incubated at 37°C for 30 minutes . The bacteria were washed twice with 1x PBS and resuspended in 1 ml PBS . The optical density at 600 nm was determined and bacterial numbers were assessed by way of comparison with reference growth curves that were established for each strain . Bacteria were diluted to contain the desired number of CFU in 20 μl of PBS and aliquots were plated on TSB agar to confirm CFUs . Female Balb/c mice aged 6 weeks were purchased from Janvier Labs , ( Saint-Berthevin , France ) and were kept in individually ventilated cages on a normal diet in six groups of 5 . At 8 weeks , groups of 10 mice were infected intranasally with a bacterial suspension containing 2 x 108 CFU in 20 μl PBS with either S . aureus JE2 , an isogenic ausB mutant or the ausB complementation strain in a pneumonia model of infection . After infection , mice were monitored for weight loss and signs of infection or severe disease every 12 hours . A disease activity index ( DAI; see S6 Table ) for each individual mouse was defined based on these observations and was plotted ( Fig 5b ) . We observed collinearity between disease severity and time for wild-type bacteria , which was confirmed by a linear model in R ( r2 = 0 . 7016; F ( 4 , 60 ) = 35 . 27 , P = 3 . 864e-15 ) . When analyzing all DAI scores as a function of infection group and time , the model was significant ( F ( 6 , 188 ) = 61 . 14 , P < 2 . 2e-16 ) . For enumeration of CFU load in lungs , complete lungs were removed 48 hours after start of infection , homogenized in 1X PBS ( GentleMACS , M-tubes , Miltenyi Biotec ) . The lung lysates were serially diluted , plated on TSA and incubated for 24 hours at 37°C . Bacterial colonies were counted and bacterial load per lung was determined . Statistical analysis was performed using one-way ANOVA . mBMDMs were cultivated as previously described [56] . Briefly , hind limbs from 6–8 week old C57BL/6 donor mice were isolated followed by separation of femur and tibia . The ends of the bones were clipped and bone marrow was flushed out with BMM ( RPMI 1640 containing 25 mM HEPES , 10% FCS , 1% penicillin-streptomycin , 1X sodium pyruvate ) . Bone marrow preparations from donor mice were individually processed . The obtained cells were mixed thoroughly and passed through a 70 μm strainer . Cells were washed with 1X Dulbecco’s PBS and resuspended in BMM medium containing 10% of L929 mouse fibroblast-conditioned medium . Cells were seeded at a density of 5 x 107 cells per plate into sterile 15 cm petri dishes and were differentiated for seven days . BMM medium was exchanged every 2–3 days . On day 7 , the cells were washed twice with 1X Dulbecco’s PBS and after addition of ice-cold 1X Dulbecco’s PBS the cells were kept at 4°C in order to detach the cells . For infection with S . aureus , mBMDMs were seeded in 24-well plates at a density of 2 x 105 per well in RPMI 1640 containing 25mM HEPES , 10% FCS , 1X sodium pyruvate to starve the cells for macrophage colony-stimulating factor ( M-CSF ) . On Day 8 , mBMDMs were infected with S . aureus at the indicated MOI for 30 minutes , followed by addition of 20 μg/ml lysostaphin and a 30 minute incubation to eradicate extracellular bacteria . Macrophages ells were washed with 1X Dulbecco’s PBS and were further incubated with RPMI 1640 containing 25mM HEPES , supplemented with 1% FCS , 1X Sodium Pyruvate . At every time point , mBMDM cytotoxicity was analyzed by using Roche Cytotoxicity Detection KitPLUS ( LDH ) , as per manufacturer’s instructions . Regarding in vivo competition experiment , female Balb/c mice ( 18–22 g , Charles River , Sulzfeld , Germany ) were infected with a 1:1 mixture of S . aureus JE2 wt and the insertional ausB mutant . Lungs were harvested 48 hours after start of infection , homogenized and plated in serial dilutions on both TSB and TSB + Erythromycin agar plates . Since only the mutant strain harbors erythromycin resistance , we calculated the ratio of resistant ( ausB ) to sensitive ( wild type ) colonies . Only mice with at least 1000 CFU per lung ( technical detection limit ) were evaluated . Himar1 transposon mutant libraries in S . aureus 6850 [57] were generated as previously described[58] and kept as frozen glycerol stocks . For administering in mice , bacterial stocks were revived as outlined above . Appropriate dilutions containing the desired number of CFUs in 20 μl of PBS and aliquots were plated on TSB agar to confirm CFUs . The mutant library to be used was titrated for lethal and sub-lethal dosage in mice . For screening experiment , groups of 3 female BALB/c mice were administered intra-nasally with 2x108 CFUs/ 20 μl and the same volume was plated on TSB agar to retrieve the input library . After 24 hours post administration , mice were euthanized and bacteria were recovered by plating the homogenized lungs ( GentleMACS , M-tubes , Miltenyi Biotec , Germany ) on TSB agar . Bacteria were harvested from the plates by scraping . Transposon insertion site sequencing was performed as previously described [36] . Shortly , bacterial DNA was prepared from the inoculum library [36] or from bacteria recovered from the lungs of three mice , which served as three biological replicates . The DNA was fragmented , end-repaired and A-tailed . Thereafter , multiplexing adaptors consisting of the annealed oligonucleotides MultiPlex-Y-Adapt_f and MultiPlex-Y-Adapt_r were ligated to the DNA fragments . We next enriched for fragments containing himar1 transposon insertion sites by linear PCR with the primer TnSeq-HimarPCR , which binds to the himar1 . In a second PCR we added oligonucleotides containing Illumina barcodes ( MP-TnSeq_Index , S2 Table ) . The products were purified with AMPure beads and the barcoded libraries were sequenced on an Illumina Hi-Seq 2500 platform ( single read , with index read ) with the transposon-specific oligonucleotide Himar1-Seq . Illumina adapter sequences were removed via cutadapt version 1 . 2 . 1 [59] and were checked for the sequence pattern ‘CAACCTGT’ originating from the transposon end . Only reads containing that specific sequence with maximally one mismatch or gap and a minimum length of 16 nucleotides were used for further analyses . The remaining reads were mapped on the Staphylococcus aureus 6850 genome ( GenBank accession CP006706 ) via Bowtie2 version 2 . 1 . 0[60] . Bacterial mRNA of in vitro shaking cultures was extracted using TRIzol from bacteria grown in TSB for 2 ( exponential phase ) and 8 hours ( stationary phase ) after an inoculation to OD600 0 . 1 from an overnight culture . After determination of RNA concentrations using a NanoDrop spectrophotometer ( Nanodrop Technologies ) DNA digestion ( Turbo DNA-free Kit , Ambion ) as well as reverse transcription ( RevertAid First Strand cDNA Synthesis Kit , Thermo Scientific ) were performed according to manufacturer’s guidelines . Real time PCR was performed on a StepOne Plus Real Time PCR system ( Applied Biosystems ) using SYBR Green PCR master mix . Primers used are listed in S2 Table . Normality of data distribution was checked by Shapiro Wilk’s test and wherever parametric or non-parametric tests for significances were applied . For comparisons of phagosomal escape assays , CFUs from phagocytes , epithelial cells and mouse lung tissue , assays of neutrophil activation , cytotoxicity assays or real-time PCR results , significances were determined by Student’s t-test . For time-dependent epithelial cytotoxicity assays and DAI analysis of mice , we used linear models and ANOVA with time and infection group as factors . Tukey’s post-hoc analysis was performed for deducing individual differences . For macrophage killing assays , CFUs at t0 , i . e . 15 minutes post-infection , were normalized to 100% and fold increase during the further time points were calculated . Wilcoxon rank sum tests were applied to comparison pairs for calculating significance . Mouse survival was assessed by fitting the observations into Kaplan Meier estimation curves . For Tn-seq data analysis , the inoculum libraries were compared to the recovered mutant libraries obtained from the mouse lungs , in biological triplicates . Gene-wise analysis was done by comparing mean read abundances of TIS originating from each gene in input and output . Site-wise analysis was performed comparing the reads originating from individual TIS in input and output . For reduction of background noise , only sites with at least two independent detections have been statistically evaluated . Genes with very low mean normalized read depth ( mnrd< 8 ) were excluded from analysis . Identification of depleted/enriched mutants was performed using a negative binomial regression model ( implemented in DESeq2 version 1 . 6 . 2[61] ) . The P-values were corrected for multiple testing [62] and genes showing an adjusted P-value < 0 . 1 were reported as significantly affected . To exclude bottleneck effects or stochastic loss essential genes were assessed by comparing all the input libraries with each other and only the genes that appeared in all inputs were considered for further analyses . TIS read abundances obtained from each gene in the mutant library inoculum were compared to that of the recovered output from lungs . Both series were fitted into a simple linear model of regression and were found to be highly similar in complexity ( R2 = 0 . 92 , F = 2464 on 1 and 2206 Degrees of Freedom , P < 2 . 2 x 10−16 ) . The differences obtained from the resultant analysis is represented by pie charts , displaying the TIS from each gene being enriched , depleted or unchanged in the Tn-seq screen . Biosecurity ( permission number AZ 50–8791 . 30 . 30 ) as well as the animal studies and protocols ( permission number AZ 2532-2-155 ) were approved by the local government of Lower Franconia , Germany . Animal studies were performed in strict accordance with the guidelines for animal care and experimentation of the German Animal Protection Law and with EU directive 2010/63/EU . Experiments using human blood were approved by the Ethics Committee of the University of Würzburg ( AZ 2015091401 ) . Blood was drawn from healthy adult volunteers , who provided written informed consent . | Staphylococcus aureus is a notorious microbe that causes a variety of diseases in man ranging from skin abscesses to blood poisoning . S . aureus can be internalized by cells of its human host , break out of membrane enclosures , and subsequently kill the cells from within . For these processes the bacterium usually employs an arsenal of partially unknown toxic proteins and peptides . In a search for factors that enable S . aureus to survive inside host cells we employed automated fluorescence microscopy . We thereby found that S . aureus uses a small cyclic dipeptide , phevalin , to interfere with host processes that would lead to destruction of the pathogen . Phevalin is not formed by ribosomes , but by a multi-domain peptide assembly line . Whereas such non-ribosomal peptides often act as antibiotics or in interspecies competition , we show that the production of phevalin not only enhances intracellular survival of S . aureus and increases killing of host cells , but also is involved in lung infection . Since we show that phevalin targets the host cell , this also implies signaling across the domains of life—between bacteria and human cells . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Methods"
] | [
"blood",
"cells",
"cell",
"death",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"viral",
"transmission",
"and",
"infection",
"pathogens",
"immunology",
"cell",
"processes",
"microbiology",
"staphylococcus",
"aureus",
"toxicology",
"epithelial",
"cells",
"genetic",
"elements",
"bacteria",
"neutrophils",
"bacterial",
"pathogens",
"white",
"blood",
"cells",
"genomics",
"animal",
"cells",
"staphylococcus",
"medical",
"microbiology",
"microbial",
"pathogens",
"biological",
"tissue",
"cytotoxicity",
"host",
"cells",
"cell",
"biology",
"anatomy",
"virology",
"genetics",
"transposable",
"elements",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"epithelium",
"macrophages",
"mobile",
"genetic",
"elements",
"organisms"
] | 2016 | Staphylococcus aureus Exploits a Non-ribosomal Cyclic Dipeptide to Modulate Survival within Epithelial Cells and Phagocytes |
Stop-codon read-through refers to the phenomenon that a ribosome goes past the stop codon and continues translating into the otherwise untranslated region ( UTR ) of a transcript . Recent ribosome-profiling experiments in eukaryotes uncovered widespread stop-codon read-through that also varies among tissues , prompting the adaptive hypothesis that stop-codon read-through is an important , regulated mechanism for generating proteome diversity . Here we propose and test a competing hypothesis that stop-codon read-through arises mostly from molecular errors and is largely nonadaptive . The error hypothesis makes distinct predictions about the probability of read-through , frequency of sequence motifs for read-through , and conservation of the read-through region , each of which is supported by genome-scale data from yeasts and fruit flies . Thus , except for the few cases with demonstrated functions , stop-codon read-through is generally nonadaptive . This finding , along with other molecular errors recently quantified , reveals a much less precise or orderly cellular life than is commonly thought .
In the standard genetic code , three ( TAA , TAG , and TGA ) of the 64 codons are stop codons , which , unlike sense codons , do not have corresponding tRNAs . These stop codons are instead recognized by release factors , causing the translating ribosome to terminate peptide synthesis and be released from the transcript . Occasionally , however , the ribosome may incorporate a standard or specialized amino acid at the stop codon and translate into the normally untranslated region ( UTR ) of the transcript until encountering the next stop codon [1] . This phenomenon is known as stop-codon read-through . Because stop-codon read-through extends the C-terminus of a protein , it can alter the protein function , which could be beneficial in some cases . For instance , stop-codon read-through is a common strategy of viruses to encode proteins with an extended C-terminus [2] . A well-described example is the gag/pol translational read-through in retroviruses , where a ~5% probability of read-through of the gag UAG stop codon is required to form the gag-pol polyprotein necessary for virion assembly [3] . Stop-codon read-through is also known in eukaryotes [4] and can influence protein localization by adding a signal peptide [5–7] . Stop-codon read-through had been thought to be rare until recent ribosome-profiling experiments that showed otherwise [8 , 9] . These experiments sequence all mRNA segments protected by ribosomes in a transcriptome at a given moment , revealing each mRNA segment that is being translated as well as the relative number of ribosomes that are translating the segment [10] . For instance , in fruit flies , hundreds of genes have 3’ UTRs protected by ribosomes , revealing widespread stop-codon read-through [9] . In addition , the set of genes subject to stop-codon read-through varies among fruit fly tissues/cell types , and read-through regions exhibit slightly but significantly higher sequence conservation than their downstream untranslated regions [9] . These observations led to the assertion that stop-codon read-through is an important , regulated mechanism for generating proteome diversity [9] , a view shared by other researchers of stop-codon read-through [11–13] . Furthermore , certain stresses induce stop-codon read-through , creating altered protein functions that could be advantageous in stressful environments [14 , 15] . Hence , stop-codon read-through is thought to have been selectively maintained in evolution as a mechanism promoting evolvability [16] . Notwithstanding , the possibility exists that stop-codon read-through , like many other processes that can generate transcriptome and proteome diversities ( e . g . , RNA editing and alternative polyadenylation ) , primarily reflects molecular errors and are nonadaptive [17–22] . In this work , we test the error hypothesis using ribosome-profiling data from the yeast Saccharomyces cerevisiae and the fruit fly Drosophila melanogaster along with other genomic data . We show that the error hypothesis makes multiple distinct predictions that are all supported by the data analyzed .
Stop-codon read-through is expected to be mostly deleterious if it largely originates from molecular errors . The potential deleterious effect could arise from ( i ) a reduction in the fraction of protein molecules with normal functions , ( ii ) a waste of cellular resource and energy in protein synthesis , and ( iii ) a gain of protein molecules with toxicity . Let the rate of stop-codon read-through be the probability that a ribosome reads through the stop codon in a run of translation . Given this rate , the deleterious effect from ( ii ) and ( iii ) rises with the number of protein molecules synthesized . Hence , natural selection against stop-codon read-through in a gene intensifies with the mRNA concentration of the gene . Consequently , the above defined rate of stop-codon read-through should decrease with the gene expression level . By contrast , the adaptive hypothesis of stop-codon read-through does not predict this negative correlation a priori , because , under this hypothesis , the rate of stop-codon read-through in a gene should depend on the specific function of the elongated protein . To differentiate between the error hypothesis and the adaptive hypothesis , we first examined yeast and fruit fly genes reported by Dunn et al . to undergo stop-codon read-through based on ribosome profiling [9] , because different studies used different protocols such that the read-through rates and expression levels estimated in different studies are not directly comparable . We measured the rate of stop-codon read-through in a gene by the number of ribosome-profiling reads per kilobase per million mapped reads ( RPKM ) in the segment between the canonical stop codon and the following in-frame stop codon in 3’ UTR , relative to that in the coding region . We quantified the expression level of a gene using RPKM in the coding region on the basis of mRNA sequencing . Indeed , the rate of stop-codon read-through is negatively correlated with the gene expression level in both the yeast ( Spearman’s ρ = -0 . 49 , P = 0 . 01; Fig 1A ) and fruit fly ( ρ = -0 . 45 , P < 10−14; Fig 1B ) . The yeast result confirms that from a recent , independent ribosome-profiling experiment [23] . Note that because the rate of read-through is computed using ribosome profiling data while the gene expression level is computed using mRNA sequencing data , the above correlation is not an artifact of statistical non-independence that has been found to affect a number of gene expression analyses [24] . Nevertheless , because the detectability of stop-codon read-through increases with the mRNA concentration , both low and high rates of read-through are detectable in highly expressed genes while only high rates of read-through may be detected in lowly expressed genes . Thus , a negative correlation between the read-through rate and gene expression level could have resulted simply from this potential detection bias . To rectify this problem , we considered all genes instead of only those reported to undergo stop-codon read-through . Genes were ranked by the expression level and then divided into 10 bins such that each bin contained the same total expression level measured by RPKM from mRNA sequencing . We then computed the overall stop-codon read-through rate of each bin by considering all genes in the bin together as a “supergene” ( instead of averaging the read-through rates of individual genes in the bin ) . The uniformity of the total expression level among bins eliminates the detection bias aforementioned . Yet , we still found that the read-through rate of a bin decreases as the average gene expression level of the bin rises in both the yeast ( ρ = -0 . 78 , P = 0 . 01; Fig 1C ) and fruit fly ( ρ = -1 , P < 10−300 , Fig 1D ) . These observations support the error hypothesis of stop-codon read-through . Manipulative experiments showed that the rate of stop-codon read-through depends on the specific stop codon and its flanking sequence [4 , 12 , 25–27] . Here , we investigate the frequencies of motifs TGACA and TGACT ( stop codons underlined ) , which are highly susceptible to stop-codon read-through [12 , 26] . The error hypothesis predicts that read-through motifs should be selected against , especially in highly expressed genes , due to the larger harm of stop-codon read-through in more highly expressed genes . By contrast , the adaptive hypothesis does not predict a priori an underrepresentation of read-through motifs in highly expressed genes . We found the expression levels significantly lower for yeast genes containing TGACA ( P = 0 . 004 , Mann-Whitney U test; Fig 2A ) or TGACT ( P = 0 . 0006; Fig 2B ) than those without such motifs . Similar results were obtained in the fruit fly ( P = 0 . 008 and 0 . 006 , respectively; Fig 2C and 2D ) . Thus , the read-through motifs are underrepresented in highly expressed genes when compared with lowly expressed genes . Consistently , when genes are divided into three equal-size bins with low , medium , and high expressions , the frequencies of the read-through motifs generally decrease as the expression increases ( bars in Fig 2E–2H ) . Two mechanisms might account for the underrepresentation of read-through motifs in highly expressed genes . First , each component ( i . e . , stop codon TGA , C after the stop codon , and A/T at the next position ) of the read-through motifs may be underrepresented . Second , the combinatory use of the three components may be underrepresented relative to the expectation from the frequencies of the three components . There is clear evidence for the first mechanism . For instance , in both the yeast and fruit fly , the 20% most highly expressed genes use the stop codon TGA significantly less often than the rest of the genes ( P < 0 . 005 , Fisher's exact test ) . A systematic analysis shows that the frequency of each of the three components is lower in the high-expression bin than in the low-expression bin in the yeast and fruit fly , and all these differences are statistically significant except for the second position after stop codon in the yeast ( Fig 2I–2K ) . Consequently , the expected frequency of a motif , computed from the product of the frequencies of the three components of the motif in a bin , is lower for the high-expression bin than the low-expression bin for each motif in each species ( dots in Fig 2E–2H ) . To probe the second mechanism , we compared the observed frequency of a read-through motif with the above computed expected value . There is some evidence for a significant deficiency of the observed frequency relative to the expected frequency for motif TGACA in the yeast ( Fig 2E ) and motif TGACT in the fruit fly ( Fig 2H ) , demonstrating the presence of the second mechanism . The absence of read-through motifs with higher-than-expected frequencies is inconsistent with the contention that stop-codon read-through is selectively favored . Because the above findings are based on genome sequences , they complement the results from ribosome-profiling data that are limited by the condition or cell type used in the experiments . If stop-codon read-through mostly results from molecular errors , the post-stop-codon translated region should not be evolutionarily more conserved than comparable regions that are untranslated . By contrast , the adaptive hypothesis predicts that the translated region should be more conserved . Below , we examine these contrasting predictions by interspecific comparisons between S . cerevisiae and S . paradoxus and between D . melanogaster and D . simulans . We restricted our comparison to closely related species because interspecific conservation of stop-codon read-through is limited [8] . Specifically , let region 1 be the transcript segment between the canonical ( i . e . , first ) stop codon and the next ( i . e . , second ) in-frame stop codon , and let region 2 be the transcript segment between the second and third in-frame stop codons . Region 1 is translated in genes subject to stop-codon read-through but not in other genes , whereas region 2 should be untranslated except in the rare case of double read-through . Because genes with and without stop-codon read-through may differ in aspects other than stop-codon read-through , respectively comparing their sequence conservations for region 1 and region 2 allows testing signals of sequence conservation specifically associated with the read-through . Sequence conservation is measured by percent nucleotide sequence identity at aligned non-gapped sites . In the following analyses , we considered yeast and fly genes previously reported on the basis of ribosome-profiling to undergo read-through [8 , 9] . We combined the read-through genes of yeast from two studies [8 , 9] to increase the statistical power . Between the two yeasts , region 1 is more conserved in genes reported to undergo read-through than other genes , but region 2 exhibits a similar trend ( Fig 3A ) . Thus , the higher sequence conservation of read-through genes than non-read-through genes in region 1 may not be related to the read-through . Similar results were obtained in the two fruit flies ( Fig 3B ) . In the fruit flies , because the excess in sequence conservation of read-through genes looks greater for region 1 than region 2 , we further examined the conservations of three codon positions respectively . If the sequence conservation in these regions is due to any protein-level function , we expect first two codon positions to be more conserved than third codon positions because mutations are less likely to be neutral at first two codon positions than at third codon positions [28] . However , we found that first two codon positions are no more conserved than third codon positions in region 1 , regardless of whether the genes are subject to stop-codon read-through ( P = 0 . 42 ) or not ( P = 0 . 088 ) . The same pattern applies to region 2 ( P = 0 . 48 and 0 . 11 , respectively ) . The above P-values were determined by bootstrapping relevant genes 1000 times and computing the fraction of bootstrap samples where first two codon positions are less conserved than or equally conserved as third codon positions . It is worth noting that , in both yeasts and fruit flies , read-through genes are more conserved than non-read-through genes in all three regions examined ( coding region , region 1 , and region 2 ) ( Fig 3A and 3B ) . This is probably because read-through genes tend to be relatively highly expressed as a result of the detection bias aforementioned and because sequence conservation tends to be greater in more highly expressed genes at least for coding regions [29] and it may also be true for 3’ UTRs due to evolutionary constraints of regulatory sequences . To compare read-through genes with non-read-through genes of similar expression levels , we ranked all genes based on their expression levels . For each read-through gene , we picked two non-read-through genes as controls , one immediately behind and one immediately ahead of the read-through gene in the ranking , and computed the mean between-species sequence conservation of the two controls . We found no significant difference in the sequence conservation of region 1 between the yeast read-through genes and non-read-through genes of similar expression levels ( P = 0 . 93 , paired t-test ) . In the fruit fly , read-through genes are significantly more conserved than non-read-through genes of similar expression levels ( P = 0 . 024 ) , but the significance disappears ( P = 0 . 053 ) upon the exclusion of only three genes ( FBgn0036994 , FBgn0043010 , and FBgn0016926 ) , suggesting that , for the vast majority of read-through genes , there is no enhanced conservation of region 1 . After investigating the aligned non-gapped sites , we turned to insertions/deletions ( indels ) in regions 1 and 2 . If post-stop-codon translation is functional , frame-shifting indels ( i . e . , not of multiples of 3 nucleotides ) should be deprived in translated regions , while no such trend is expected under the error hypothesis . We found no significant differences in the proportion of frame-shifting indels between genes with and without read-through in either region 1 or 2 of either species pair examined ( Fig 3E and 3F ) . These results are consistent with the error hypothesis , but are inconsistent with the adaptive hypothesis that predicts an underrepresentation of frame-shifting indels specifically in region 1 of the read-through genes . The adaptive hypothesis further predicts conservation of the length of region 1 in genes undergoing read-through , while no such prediction is made by the error hypothesis . We first computed the absolute interspecific length differences of region 1 for genes with and without read-through . Between the two yeasts , the average length differences are 2 . 67 and 2 . 30 nucleotides ( nt ) for read-through and non-read-through genes , respectively ( P = 0 . 014 , two-tailed Mann-Whitney U test ) . This result is opposite to the prediction of the adaptive hypothesis . Between the two fruit flies , the average length differences are 1 . 56 and 2 . 10 nt for read-through and non-read-through genes , respectively ( P = 0 . 14 ) . While in the direction predicted by the adaptive hypothesis , this disparity is not statistically significant . We noticed that region 1 is on average longer in read-through genes ( 65 . 42 nt in S . cerevisiae and 62 . 31 nt in D . melanogaster ) than non-read-through genes ( 46 . 69 nt in S . cerevisiae and 52 . 37 nt in D . melanogaster ) . This observation is likely due to detection bias , because read-through is more detectable by ribosome profiling when region 1 is longer . Hence , it may not serve as evidence for the functionality of read-through regions . To correct for this bias in the comparison of interspecific length differences , we computed the relative length difference of region 1 between species for genes with and without read-through , respectively . The relative length difference is the absolute value of ( LA−LB ) / ( LA + LB ) , where LA and LB are the lengths of the orthologous region 1 in the two species compared . In neither the yeasts ( P = 0 . 15 , Mann-Whitney U test; Fig 3E ) nor fruit flies ( P = 0 . 15; Fig 3F ) was the relative length difference significantly different between genes with and without read-through . Thus , regardless of whether the absolute or relative length difference between species is considered , read-through genes show no increased region 1 length conservation than non-read-through genes . Together , the above analyses strongly support the hypothesis that stop-codon read-through mostly results from molecular errors . Recent experiments in nematode and human cells showed that artificially-made fusion proteins corresponding to the coding region and region 1 combined tend to be unstable and degraded when compared with the proteins corresponding to the coding region only [30] , a clear indication that read-through would be deleterious . The experiments , however , did not focus on genes with natural stop-codon read-through . Thus , the results suggest that stop-codon read-through in these genes , which probably have low read-through rates naturally , is deleterious . The study also found that the deleterious effect of translation of region 1 increases with the hydrophobicity of the peptide corresponding to region 1 [30] , presumably because the hydrophobic residues translated from region 1 interfere with protein folding . Given these observations , we predict that natural selection minimizing the harm of stop-codon read-through may result in a lower hydrophobicity of the peptide corresponding to region 1 of read-through genes than that corresponding to non-read-through genes , either because natural selection drives the decrease in hydrophobicity of the extended peptide or because natural selection disfavors the read-through of genes where the extended peptide would be highly hydrophobic . To this end , we first compared the hydrophobicity of the coding region and region 1 of non-read-through genes . We found the former to be significantly lower than the latter in both the yeast ( Fig 4A ) and fruit fly ( Fig 4B ) , confirming that functional proteins are required to have a lower hydrophobicity than the random expectation , which is represented by region 1 of non-read-through genes . In support of our prediction , the hydrophobicity of region 1 is indeed lower for read-through genes than non-read-through genes , although the trend is significant in the fruit fly ( Fig 4B ) but not in the yeast ( Fig 4A ) . This difference between the fruit fly and yeast may be related to the fact that the read-through rate is overall much lower in the yeast than in the fruit fly ( Fig 1 ) . The deleterious effects of molecular errors could be alleviated by global solutions such as a reduction in the overall read-through rate via ribosomal improvements or by local solutions such as a reduction in the hydrophobicity of the read-through peptide of a given gene [31] . It is possible that more global solutions have evolved in yeast while more local solutions have appeared in the fruit fly . Theory predicts that both solutions are possible in the yeast and fruit fly due to their huge effective population sizes [31] . At any rate , our finding of a lowered hydrophobicity of region 1 of read-through genes reveals natural selection minimizing the harm of stop-codon read-through and hence further supports the error hypothesis . As a control , we also examined region 2 , which is translated in neither read-through nor non-read-through genes . In the yeast , regions 1 and 2 behave similarly in hydrophobicity for both read-through and non-read-through genes , consistent with the above interpretation . In the fly , region 2 of both read-through and non-read-through genes has a high hydrophobicity , similar to that of region 1 of non-read-through genes , as expected .
In this work , we used ribosome-profiling data from the unicellular model fungus S . cerevisiae and multicellular model animal D . melanogaster and genome sequences from these and other species to test the hypothesis that stop-codon read-through results largely from molecular errors and is generally nonadaptive . In both the yeast and fruit fly , we observed that ( i ) the read-through rate decreases with the level of gene expression , ( ii ) sequence motifs conducive to read-through are underrepresented among highly expressed genes , and ( iii ) read-through regions do not exhibit increased sequence conservation , avoidance of frame-shifting indels , or resistance to length changes . Furthermore , it was previously reported that most read-through events are not conserved between species [8] . Together , these observations strongly support the error hypothesis and reject the assertion that most read-through events are functional and adaptive . To estimate the fraction of read-through events that are deleterious , we followed two recent studies [21 , 32] . Stop-codon read-through is minimally selected against in lowly expressed genes because the waste of energy and production of toxic products are minimal . Hence , the read-through rate in lowly expressed genes may be considered the intrinsic read-through rate without selective minimization . Following the same logic , stop-codon read-through in highly expressed genes is comparatively costly so has been selectively minimized . Hence , the read-through rate observed in highly expressed genes reflects the rate of non-deleterious read-through upon the selective removal of deleterious read-through . So , the amount of read-through in highly expressed genes that has been removed by natural selection can be estimated from the difference in read-through rate between lowly and highly expressed genes . The read-through rate is 0 . 0398 in the leftmost bin and 0 . 0111 in the rightmost bin in Fig 1C . Hence , we estimate that ( 0 . 0398–0 . 0111 ) /0 . 0398 = 72 . 1% of read-through has been removed by natural selection in highly expressed yeast genes . The corresponding value is ( 0 . 04069–0 . 01318 ) /0 . 04069 = 67 . 6% in highly expressed fruit fly genes . That a slightly larger fraction of read-through has been selectively removed in the yeast than the fruit fly is expected , because the effective population size is larger in the yeast , so the efficacy of natural selection is greater in the yeast than in the fruit fly [33 , 34] . The above percentages are conservative estimates of the fraction of deleterious read-through events , because slightly deleterious read-through may not have been fully removed by selection in highly expressed genes and because some strongly deleterious read-through may have been removed by selection even in lowly expressed genes . If the probability that stop-codon read-through is harmful is the same in lowly and highly expressed genes in spite of a difference in the magnitude of the harm , we would conclude that at least 72% and 68% of read-through events are deleterious in yeast and fruit fly , respectively . In light of our finding , it is worth reexamining evidence previously thought to support the adaptive hypothesis of stop-codon read-through . First , the read-through rate is known to increase under some stresses , which could be advantageous when new proteins able to cope with the stresses are needed [14 , 15] . One mechanism of the stress-induced read-through in yeast is the conversion of the release factor Sup35 from its normal folding to an aggregated amyloid conformation known as the prion state , which induces more Sup35 molecules to convert to the prion state , causing rampant stop-codon read-through [35 , 36] . Although such an elevation in the read-through rate may be an active response to stress , it could also be a passive , deleterious consequence because cells are not in their optimal physiological state under stress . Interestingly , comparing the growth rates between two yeast strains of the same genetic background , one with a higher read-through rate than the other , across multiple stressful environments showed that high read-through is advantageous in some environments but disadvantageous in some other environments [35] . Further , a simulation study showed that selection against the Sup35 prion appearance is substantial [37] . Some researchers contended that , although stop-codon read-through may not be particularly beneficial in the current environment , it may eventually lead to higher fitness in the long run because it reveals cryptic protein-coding sequences in the UTR under stresses [31] . However , experimental evolution showed that the rate of yeast adaptation to new environments is not necessarily higher with the Sup35 prion than without the prion [38] . Furthermore , even if stop-codon read-through improves evolvability , read-through could still be errors , analogous to genetic mutations , which are errors that could increase evolvability . As mentioned , another line of evidence supporting the adaptive hypothesis was the observation that region 1 is significantly more conserved than region 2 among fruit fly read-through genes [9] . Our analysis showed that while this is true in fruit flies ( Fig 3B ) , it is not true in yeasts ( Fig 3A ) . Furthermore , in fruit flies , region 1 is more conserved than region 2 even in non-read-through genes ( Fig 3B ) , suggesting the possibility that the excess conservation of region 1 is not related to read-through . Indeed , in both yeasts and flies , we found no excess in region 1 sequence conservation for all or the vast majority of read-through genes when compared with non-read-through genes of similar expression levels . In addition , the analyses of frame-shifting indels and sequence length evolution support that , in both yeasts and fruit flies , region 1 does not have stronger selective constraints in read-through genes than non-read-through genes . In this context , it is worth mentioning that some authors use sequence conservation of region 1 to identify potential read-through genes under the premise that the conservation would imply functional read-through [39 , 40] . Our finding that sequence conservation of region 1 may not be related to read-through cautions against this practice . It is notable that only 15% of fruit fly read-through events predicted by sequence conservation of region 1 were confirmed in ribosome profiling ( although this could be due to limited sampling of tissues or developmental stages ) , while only 12% of read-through events observed in ribosome profiling were predicted from sequence conservation [9] . These small overlaps are consistent with our conclusion that most read-through regions are not conserved and most conserved region 1 sequences are not subject to read-through . Following previous transcriptome-wide studies of stop-codon read-through [8 , 9] , we used ribosome profiling to identify such events . Although not every ribosome footprint indicates translation , a reasonably high fraction of read-through events identified by ribosome profiling are verifiable at the protein level [9] . Furthermore , even if ribosome profiling produces some false read-through signals , such errors cannot explain our observation of a negative correlation between gene expression level and read-through rate ( Fig 1 ) , unless transcripts of lowly expressed genes have more ribosome protections than those of highly expressed genes . To the best of our knowledge , no such bias has been reported or is expected . That most read-through events are nonadaptive does not preclude the possibility that a small proportion of such events have been co-opted in evolution for certain functions . It will be of interest to identify such functional cases from the sea of largely functionless read-through events . We suggest that such functional cases are likely conserved among multiple species , have high read-through rates , and show multiple signals of functional constraints in region 1 such as reduced sequence variation among species and avoidance of frame-shifting indels . Candidates for adaptive read-through can be experimentally verified by measuring the functional and/or fitness effect of altering the read-through rate , for example , by modifying the read-through motif . While laborious , this approach can provide definitive evidence for adaptive read-through . Our results support the hypothesis that most stop-codon read-through events are one type of translational error , which also includes the incorporation of erroneous amino acids in protein synthesis ( i . e . , mistranslation ) . It has been estimated that the mistranslation rate ranges from 10−5 to 10−2 per codon , depending on the type of error [41 , 42] . We found the stop-codon read-through rate between 10−4 and 10−2 in the yeast and fruit fly , depending on the gene expression level . Thus , the read-through rate is generally consistent with the mistranslation rate , and both of them are higher than the rate of transcriptional error [43] . Several recent studies showed that a number of cellular processes that are widely thought to be beneficial for generating transcriptomic and proteomic diversities , such as alternative transcriptional initiation , alternative splicing , alternative polyadenylation , and various RNA modifications , result largely from molecular errors and are generally nonadaptive [17–21 , 32] . That random errors are not uncommon even in the seemingly exquisitely regulated and optimized processes of RNA and protein synthesis reminds us of the inherent stochasticity and imprecision of the cellular life . It also cautions against assuming adaptive values of any phenomenon without critical evaluation , even if the phenomenon is common at the genomic scale .
The genome and gene sequences of Drosophila and Saccharomyces species were downloaded from the publicly available SGD [44] , FlyBase [45] , and Ensembl [46] databases . The ribosome-profiling data and read-through rates of D . melanogaster and S . cerevisiae genes were from Dunn et al . [9] . The list of 350 read-through genes in D . melanogaster was from the same study [9] . When evaluating the general properties of read-through genes , a list of 172 read-through genes in S . cerevisiae was used , which is the union of the read-through genes from two previous studies [8 , 9] . Gene expression data of D . melanogaster were downloaded from FlyBase ( FB2016_04 ) [45] . The mean expression level of a gene across multiple growth stages was used as a proxy for its overall expression level [47] . When calculating the log2 ( RPKM ) for individual genes in Fig 2C and 2D , we added 1 to the RPKM of all genes to avoid undefined log2 ( RPKM ) when RPKM = 0 . Gene expression levels in S . cerevisiae were from the study by Nagalakshmi et al . [48] . Read-through cannot be detected in lowly expressed genes unless the read-through rate is high , creating an artifactual negative correlation between gene expression level and read-through rate . We designed the following method to rectify this problem . We first ranked all genes by their expression levels measured by the RPKM of the coding region from mRNA sequencing data mentioned in the above section . Based on the ranking , we then grouped these genes into ten bins , requiring the total RPKM for each bin to be equal . We computed an overall read-through rate of each bin by the sum of RPKM of region 1 divided by the sum of RPKM of coding regions from the ribosome-profiling data . The standard error of the read-through rate was calculated by bootstrapping genes in each bin 1 , 000 times . The yeast and fly RPKM data from both ribo-seq and mRNA-seq were from Dunn et al . [9] . All genes with measured RPKM in CDS and region 1 ( including 0 RPKM ) were included in this analysis . From each species considered , we respectively estimated the numbers of two read-through motifs , TGACA and TGACT , from all genes in the genome . To understand the underlying mechanisms for the underrepresentation of the motifs in highly expressed genes , we separately shuffled the three motif components ( the stop codon and the two nucleotides after the stop codon ) among all genes in the same expression bin and respectively counted the numbers of genes with TGACA and TGACT motifs upon the shuffling . This process was repeated 10 , 000 times to test if the actual number of motifs differs significantly from that expected under a random combinatory use of the three motif components . We focused on one-to-one orthologs of S . cerevisiae and its sister species S . paradoxus , and those of D . melanogaster and its close relative D . simulans . For genes with multiple alternative transcripts , the longest transcripts were analyzed . The sequence alignment included regions from 50 nucleotides upstream of the stop codon to 300 nucleotides downstream of the stop codon . To ensure the quality of subsequent analyses , we retained only those alignments for which the stop codon was aligned correctly and the last 36 nucleotides in the alignment had at least 88% sequence identity . We define region 1 by the segment between the canonical ( i . e . , first ) stop codon and the next ( i . e . , second ) in-frame stop codon in the 3’ UTR , and region 2 by the segment between the second and third stop codons in the 3’ UTR based on the sequences in S . cerevisiae or D . melanogaster . For region 1 or 2 that extends over the 300-nucleotide length limit , we considered only up to the 300-nucleotide region . We calculated the fraction of hydrophobic sites for the last 16 amino acids of each protein , region 1 , and region 2 for 6 , 517 and 11 , 683 genes in S . cerevisiae and D . melanogaster , respectively . The following amino acids were considered hydrophobic: G , A , V , I , L , M , F , Y , and W . | The stop codon gives the translating ribosome the signal for the termination of peptide synthesis , but occasionally the ribosome goes past the stop codon and continues translating into the otherwise untranslated region of a transcript . Stop-codon read-through generates an elongated peptide , which could be beneficial under certain circumstances . Although stop-codon read-through was thought to be rare , recent ribosome-profiling experiments in eukaryotes discovered hundreds of genes that undergo stop-codon read-through at a detectable rate . It is unclear whether most of these observed read-through events have biological functions or reflect cellular errors . The error hypothesis makes a set of distinct predictions about the probability of read-through , frequency of sequence motifs for read-through , and conservation of the read-through region . Our analysis of genome-scale data from yeasts and fruit flies verifies each of these predictions , suggesting that most stop-codon read-through events are nonadaptive cellular errors . These and related findings of various molecular errors in transcription and posttranscriptional modification paint a much less precise or orderly cellular life than is commonly portrayed . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"animals",
"invertebrate",
"genomics",
"animal",
"models",
"fungi",
"drosophila",
"melanogaster",
"model",
"organisms",
"experimental",
"organism",
"systems",
"sequence",
"motif",
"analysis",
"cellular",
"structures",
"and",
"organelles",
"drosophila",
"research",
"and",
"analysis",
"methods",
"saccharomyces",
"sequence",
"analysis",
"bioinformatics",
"animal",
"studies",
"gene",
"expression",
"insects",
"animal",
"genomics",
"ribosomes",
"yeast",
"arthropoda",
"biochemistry",
"eukaryota",
"cell",
"biology",
"natural",
"selection",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"saccharomyces",
"cerevisiae",
"genomics",
"evolutionary",
"biology",
"evolutionary",
"processes",
"organisms"
] | 2019 | Stop-codon read-through arises largely from molecular errors and is generally nonadaptive |
We present a method to calculate the propensities of regions within a DNA molecule to transition from B-form to Z-form under negative superhelical stresses . We use statistical mechanics to analyze the competition that occurs among all susceptible Z-forming regions at thermodynamic equilibrium in a superhelically stressed DNA of specified sequence . This method , which we call SIBZ , is similar to the SIDD algorithm that was previously developed to analyze superhelical duplex destabilization . A state of the system is determined by assigning to each base pair either the B- or the Z-conformation , accounting for the dinucleotide repeat unit of Z-DNA . The free energy of a state is comprised of the nucleation energy , the sequence-dependent B-Z transition energy , and the energy associated with the residual superhelicity remaining after the change of twist due to transition . Using this information , SIBZ calculates the equilibrium B-Z transition probability of each base pair in the sequence . This can be done at any physiologically reasonable level of negative superhelicity . We use SIBZ to analyze a variety of representative genomic DNA sequences . We show that the dominant Z-DNA forming regions in a sequence can compete in highly complex ways as the superhelicity level changes . Despite having no tunable parameters , the predictions of SIBZ agree precisely with experimental results , both for the onset of transition in plasmids containing introduced Z-forming sequences and for the locations of Z-forming regions in genomic sequences . We calculate the transition profiles of 5 kb regions taken from each of 12 , 841 mouse genes and centered on the transcription start site ( TSS ) . We find a substantial increase in the frequency of Z-forming regions immediately upstream from the TSS . The approach developed here has the potential to illuminate the occurrence of Z-form regions in vivo , and the possible roles this transition may play in biological processes .
DNA often occurs in an underwound , negatively superhelical topological state in vivo . In bacteria , gyrase enzymes act to generate negative supercoils , while topoisomerases dissipate them . The dynamic balance between these two processes determines a basal level of superhelicity that can change according to the environmental or nutritional state of the organism [1] . In addition , RNA polymerase translocation leaves a wake of negative supercoils and generates a bow wave of positive supercoils [2]–[4] . Together these effects induce substantial amounts of superhelicity in the topological domains into which bacterial genomes are subdivided . A variety of regulatory processes in prokaryotes , including the initiation of transcription from specific genes , are known to vary with the level of superhelicity experienced by the DNA involved [5] . It has long been thought that unconstrained superhelicity was not a factor in eukaryotic genomic regulation . Eukaryotes do not commonly have negatively supercoiling gyrases while they do have relaxing topoisomerases . Also nucleosomal winding both stabilizes supercoils and could inhibit the transmission of unconstrained superhelicity . However , it is now known that substantial amounts of transcriptionally induced negative superhelicity occur upstream ( i . e . 5′ ) of RNA polymerases in the human genome [6] , [7] . A superhelix density of is achieved there by a single transcriptional initiation event , while divergently oriented transcription can produce superhelix densities of in the region between the polymerase complexes . This superhelicity extends over at least kilobase distances , hence must be transmitted either through or around nucleosomes . Kinetically , this transcription driven superhelicity is generated faster than topoisomerases act to relieve it , so it abides long enough to be able to affect subsequent regulatory processes . The levels of negative superhelicity achieved in both prokaryotes and eukaryotes are sufficient to drive in vivo structural transitions to alternative DNA conformations [7] , [8] . The most studied DNA transition is superhelically induced duplex destabilization ( SIDD ) , which facilitates or creates local sites of strand separation . SIDD has been implicated in a wide variety of regulatory processes , including the initiation of transcription from specific promoters in both prokaryotes and eukaryotes [9]–[17] . Here we focus on the transition from B-form to Z-form , a left-handed double helix . When the discovery of Z-DNA was announced this transition was predicted to occur at physiologically attained levels of negative superhelicity [18]–[20] . Z-DNA has been experimentally detected at inserted Z-susceptible sites in bacterial genomic DNA both in vitro and in vivo [21]–[26] . The study of alternate DNA structures in eukaryotes is more challenging , in part because DNA superhelicity in these organisms seems not to be stable , but rather is a transient state driven by transcriptional activity . However , there is substantial indirect evidence that Z-DNA also can occur in vivo in eukaryotes . Z-DNA has been implicated in a variety of regulatory events relating to replication , transcription , recombination , and other biological processes [27] . For example , it has been shown that the negative torsional stress induced by polymerase translocation during transcription can stabilize Z-DNA near transcription start sites [28] . The amount of Z-DNA found in these experiments was directly related to transcriptional activity , and thus to the level of transcription-driven superhelicity . Another set of experiments studied the formation of Z-DNA in the 5′ flank of the human c-myc gene [29] , [30] . Three Z-susceptible regions were identified near the promoters of this gene . These experimental results suggest that the regions involved transform to Z-form during c-myc transcription , but revert to B-form when transcription is inhibited . These experiments indicate that transcriptionally driven superhelical stresses can drive B-Z transitions in mammalian cells . Many attempts have been made to identify proteins that bind selectively to Z-DNA . A powerful method developed by Herbert [31] led to the isolation of double-stranded RNA adenosine deaminase ( ADAR1 ) [32] , a Z-DNA binding enzyme , as well as other Z-binding proteins . It has been shown that E3L , a Z-DNA binding protein found in poxviruses , inhibits the host cell's ability to perform transcription or mount an anti-viral response when it is bound to Z-DNA near transcription start sites [33] . On this basis it was suggested that an inhibitor of E3L binding might protect against poxviral infection . Although there are some indications that Z-binding proteins may be involved in gene regulation , this remains an active area of research [27] . The Z-form helix has dinucleotide repeat units , one of which must be in the syn- and the other in the anti-conformation , with helicity of −12 base pairs per turn [34] . ( The minus sign indicates the left-handedness of the helix . ) The free energy required for the B-Z transition under low salt conditions has been determined for each of the ten dinucleotides [21] , [35]–[39] . The Z-form is energetically most accessible for certain alternating purine-pyrimidine sequences , the most favored being , with guanine in the and cytosine in the conformations . Z-formation has also been observed in sequences , although transitions there are almost twice as costly as at GC runs . The remaining alternating purine/pyrimidine sequence , , has a very high transition energy and is not normally found in Z-form . Perturbations which break the purine/pyrimidine alternation , although energetically costly , have also been observed in Z-DNA , as will be discussed below . The substantial nucleation energy for initiating a run of Z-DNA , which may be regarded as the cost of generating two junctions between B-form and Z-form , also has been determined [21] , [40] . Soon after the discovery of Z-DNA several simple theoretical analyses of superhelical B-Z transitions were developed . These all assumed the simplest conditions of a single , uniformly Z-susceptible site embedded in an entirely Z-resistant background . The first such analysis simply predicted that physiological levels of negative superhelicity could drive B-Z transitions [18] . This approach was subsequently used to investigate the basic properties of these transitions , and to assess how the B-Z transition might compete with others in simple paradigm cases [19] , [36] , [41]–[43] . Finally , these simple theoretical approaches were applied to determine the energy parameters of the transition from experiments in which a single uniform insert ( commonly ) placed within a superhelical plasmid was observed to undergo transition [21] , [36] , [40] . In this paper we present the first method to analyze the superhelical B-Z transition in its full complexity . This method , which we call SIBZ , can calculate the B-Z transition behavior of multi-kilobase length genomic DNA sequences under superhelical stress . It specifically includes the competition for transition among all sites within the sequence . SIBZ analyzes the states available to the entire sequence , where each base can be found in either the B-conformation or as a part of a Z-form dinucleotide pair . It then uses statistical mechanics to determine the equilibrium distribution among these states . Specifically , it calculates the probability of B-Z transition for each base pair in the sequence under the given conditions . In this way it identifies the Z-susceptible regions within the sequence , and assesses how they compete at any given level of superhelicity . SIBZ was developed by modifying the SIDD algorithm to treat the B-Z transition , as described in the following section . Several other theoretical strategies have been developed or proposed for analyzing superhelical DNA transitions , which also might have been modified for this purpose . Although a formally exact method has been suggested based on recursion relations , it was found to be too computationally inefficient to warrant development [43] , [44] . So an approximate algorithm was presented in the same paper that could make base pair-specific calculations . This method has not been made available for public use or evaluation . An alternative exact algorithmic strategy also has been developed and presented [45] . Although this approach could compute transition profiles ( i . e . transition probabilities for each base pair ) , it too was found to be too computationally cumbersome to be practical . So a more efficient approximate method based on its approach was also presented . To create SIBZ we chose to modify the SIDD approach because it has been extensively developed , optimized and implemented in this group , and it features an attractive combination of high accuracy and computational efficiency . There have been three previous theoretical methods implemented that analyze DNA sequences to identify potential Z-DNA forming regions [35] , [46]–[48] . The first method , developed by the Jovin group , seeks to identify Z-susceptible sites based solely on their sequence characteristics [46] . The energetics of transition were not considered in this approach . Another method , called Z-Catcher , performs a mechanical calculation , but does not consider the thermodynamic equilibrium of the system [47] . Z-Hunt [35] , [48] uses statistical mechanics , but only calculates the propensity of each fixed region within the sequence to form a Z-helix in isolation . Since the superhelical stresses that drive B-Z transitions couple together the transition behaviors of all base pairs that experience them , these approaches do not give information about how these competitive transitions behave in situ .
There are possible states available to a sequence of base pairs that is subject to a monomeric two-state transition ( that is , one in which any individual base pair can either be in the B-form or in the alternate state ) . This number does not depend on whether the sequence is linear or circular . This is the situation for the strand separation transition , in which the repeat units of both states are monomeric . However , it does not hold for the B-Z transition because the repeat unit of Z-DNA is two base pairs ( dimeric ) , while that of the B-form is a single base pair ( monomeric ) . We will first derive an expression for the number of Z-form states available to a linear molecule of base pairs , and then use this result to determine the number of states of a circular molecule having the same length . Let denote the number of states available to a linear molecule comprised of base pairs experiencing the B-Z transition . In any given state each base pair in the sequence is either a monomer ( i . e . in B-form ) or part of a dimeric pair with one of its neighbors ( i . e . in Z-form ) . There are two possible arrangements for the first base pair in the sequence . It can be a monomer ( i . e . in B-form ) , in which case there are ways that the rest of the sequence can be arranged . Otherwise , it can be in a dimeric unit with the second base pair ( i . e . in Z-form ) . In this case the disposition of the first two base pairs is determined , so there are ways to arrange the rest of the sequence . Because these two alternatives are mutually exclusive and exhaust the possibilities , it follows that ( 1 ) Using the fact that and , one can calculate for any length from Eq . ( 1 ) . This recursion relation together with these initial conditions show that , the st Fibonacci number . An excellent approximation to this number for all is given by ( 2 ) This shows that the number of possible state in the linear B-Z transition grows exponentially with sequence length , but with base equal to the golden ratio , rather than base 2 as holds for transitions in which both states have monomer units . All the states found above for a linear molecule also are available to a circular molecule of the same length . However , in this case it is also possible that base pairs 1 and can form a dimeric unit , provided neither is already dimerized with its other neighbor . The number of states of the linear molecule in which neither base pair 1 nor base pair are dimerized is . So this is the number of states of the circular molecule in which base pair 1 dimerizes with base pair . It follows that the number of states available to the circular molecule is ( 3 ) In principle all states available to the molecule compete for occupancy . Once the free energy associated to each state has been evaluated , the partition function may be calculated as ( 4 ) where the sum is over all states , and , where is the Boltzmann constant and is the temperature . At thermodynamic equilibrium the available states are weighted according to the Boltzmann distribution . That is , in the equilibrium distribution each state occurs with relative frequency ( 5 ) This means that the occupancy of states decreases exponentially as their free energies increase , so only the relatively low energy states are significantly occupied . At equilibrium the ensemble average value of a parameter , that has value in state , is given by ( 6 ) The equilibrium probability of transition for base pair is found by averaging the parameter according to the above equation , where in any state where base pair is transformed , and in all other states . The transition profile is the graph of vs . It shows the probability of transition for each base pair in the sequence under the assumed conditions . As will be shown below , this profile can change significantly as the imposed superhelix density changes . We consider a DNA molecule containing base pairs of defined sequence , on which a superhelical density is imposed . Here , where bp/turn is the helical twist rate for the B-form . A state of this molecule assigns to each base pair one of two conformations , either B-form or Z- form . This is done in a manner consistent with the dinucleotide repeat unit of Z-DNA , as described below . The residual superhelicity in that state is the linking difference remaining to stress the molecule after the change of twist consequent on transition . This includes the untwisting of the transformed base pairs from the right-handed B-form to the left-handed Z-form , together with a small amount of untwisting of the two stands at each B-Z junction [21] . Thus , in a state where bases pairs are in the Z-form the residual superhelical linking difference is given by ( 7 ) Here bp/turn is the twist rate for the B-helix , bp/turn is the twist rate of the Z-helix , and is the number of runs of Z-form DNA . ( A run of transformed base pairs is defined as a maximal segment in which all base pairs are in the non-B structure . ) The twist at a B-to-Z junction has been measured to be turns [21] . In our current applications the superhelicity is regarded as remaining constant . Next we determine the free energy of each state of this molecule . This is comprised of the energy cost of the transition , plus the energy of accommodating the residual superhelicity . The quadratic free energy associated to residual superhelicity [52] is given by ( 8 ) Here [53] , [54] , is the gas constant , and is the absolute temperature . The energy cost of the transition includes two factors . First , a nucleation energy for each run of Z-DNA is required to form the junctions between the B-form and the Z-form . This has been measured to be approximately 5 . 0 kcal/mol/junction , so the nucleation energy for each Z-DNA run is kcal/mol [21] , [36] , [40] . Second , the energy to transform the specified base pairs into the Z-DNA conformation is also needed . The published values of the B-Z transition free energies for all 10 dinucleotide pairs are given in the second and third columns of Table 1 [35] . The bases in each dinucleotide pair must alternate , one in anti and the other in syn conformation . As the values in the table show , the transition energy of a particular dinucleotide depends strongly on whether it is AS ( 5′ anti 3′ syn ) or SA ( 5′ syn 3′ anti ) . A Z-Z junction occurs when adjacent dinucleotides have different anti-syn alternations , either ( AS ) ( SA ) or ( SA ) ( AS ) . This violation is energetically costly , as shown in the last column of the Table . Some of the energy values shown in Table 1 are calculated estimates , while others were measured experimentally [21] , [35] , [37]–[39] . The energies shown can be evenly divided between the two base pairs involved , whether in a dinucleotide or in a Z-Z junction , to determine the transition free energy associated with each base pair . It is energetically most favorable for purines and pyrimidines in the Z-form to be in the syn and the anti states , respectively . There is a substantial energy cost for a base pair to have the opposite conformation . Although in principle every dimeric sequence can be driven into Z-form , four ( CG , GC , CA = TG , and AC = GT ) have substantially lower transition energies than do the others . The equilibrium distribution will be dominated either by the untransformed state or by states in which transition occurs at sequences composed of the energetically most favored dinucleotides . The total free energy of a state that has specified base pairs comprising dinucleotide repeat units in runs of Z-form is given by ( 9 ) The precise manner in which the base pair transition energies are determined from Table 1 is described below . We evaluate the equilibrium B-Z transition in a negatively supercoiled DNA molecule by appropriately modifying the previously developed SIDD method , which has been described elsewhere [45] , [49] , [51] , [55] . Briefly , one first finds the lowest energy state of the system , whose free energy is denoted by . Then one sets an energy threshold , and finds all states whose free energy does not exceed . This is done by an exhaustive enumeration procedure [45] , [49] . From these states one calculates an approximate partition function and the equilibrium ensemble average values of all quantities of interest . Since the population of a state at equilibrium decreases exponentially as its free energy increases , the states that are neglected because their energies exceed the threshold are occupied with very low frequencies . If , for example , the threshold is set at kcal/mol , as is used below , the neglected states are occupied no more than times the frequency of the lowest energy state at K . A careful density of states calculation was performed to assess the aggregate influence of the neglected high energy states , and to approximately modify the calculated ensemble averages to account for them [49] , [51] . Although this correction could not be performed on the transition probabilities of individual base pairs , this approach showed that all other ensemble averages were accurate to between four and five significant figures . Subsequently , we developed an exact , but very slow algorithm that performs these calculations [50] . By comparing its results with those of SIDD at different energy thresholds it was determined that , when thresholds in the range 10 to 12 kcal/mol were used , the accuracy of all calculated parameters , including the transition probabilities of individual base pairs , exceeds four significant digits [45] . Since there is a substantial nucleation energy kcal/mol associated with opening each run [21] , [36] , [40] , states with numerous Z-runs will be correspondingly less populated . This allows us to impose a cutoff on the number of runs that may occur . In the SIDD analysis of strand separation it was found that three run states were the most that were encountered at any reasonable energy threshold and superhelix density . However , sequences that are energetically most susceptible to the B-Z transition tend to be shorter than the A+T-rich regions that favor denaturation . As shown by the energies in Table 1 , there are many very costly types of “imperfections” which may hinder the extension of a Z-DNA run . So in the B-Z transition it can be energetically less expensive to initiate a new run at another favorable site than to extend an existing run into an energetically unfavorable region . Extensive sample calculations performed in tuning the SIBZ algorithm have shown that limiting consideration to states with a maximum of four runs is sufficient for high accuracy at the superhelical densities and sequence lengths of interest in this paper . One important step in the SIBZ algorithm is the assignment of transition energies to the dinucleotide repeat units in each Z-run . This is complicated because the transition energy associated to each unit can have any of three significantly different values depending on the anti and syn characters of the base pairs in that unit and in its neighbors . Briefly , within each unit one base pair must be in syn and the other in anti . Also there is a significant energy penalty assessed in cases where there are Z-Z junctions with one or both neighbors . In the SIBZ algorithm we use the following procedure to assign the energetically most favorable anti or syn conformations to all base pairs in a potentially Z-forming run . First , since the unit cell of Z-DNA is a dinucleotide , we allow only an even number of base pairs in any Z-run . We assign all purines in the run to be syn and all pyrimidines to be anti , as the dinucleotides with the lowest transition energies have this character . ( See Table 1 . ) Since in most cases forming a Z-Z junction is more costly than flipping a base pair into its non-favorable conformation , we next change the conformation of a base pair if it has the same anti or syn character as both of its nearest neighbors . This procedure eliminates any repetition of the same conformation ( anti or syn ) longer than two base pairs . Then we search for quartets of the form AASS or SSAA . In these we flip the central two bases so that an alternating syn-anti character is obtained . Finally , when two bases within a dinucleotide unit have the same conformation , which is not permitted in Z-DNA , we flip the base pair which yields the minimum Z-Z junction energy with its neighbor . There is an ambiguity in this procedure for internal runs of even length at least four , such as ASSSSA , due to the order in which they are flipped . However , the states involved are always high energy because they will have at least one Z-Z junction as well as at least two unfavorable dinucleotides . To determine the B-Z transition energies of a region , we scan its sequence by dinucleotide units , adding energies of syn-anti or anti-syn pairs according to Table 1 . Whenever the alternating anti-syn character is disturbed we add the appropriate Z-Z junction energy . Also , we set the minimum length of a Z-run to be eight base pairs , as shorter regions have not been seen experimentally to form Z-DNA [21] . In this way we assign a B-Z transition energy to each segment in the DNA sequence of even length between 8 and 250 base pairs , which is a reasonable maximum cutoff for a Z-run length . The algorithmic strategies for finding the lowest energy state , identifying all states that satisfy the threshold condition , and analyzing them to determine equilibrium values of parameters of interest are the same as those developed previously in the SIDD algorithm [45] , [49] . We treat circular sequences as described there . A linear sequence is circularized by joining its end to its start with 50 T bases , and the resulting sequence is treated using the same method . In this case we do not report the information for the augmenting segment . These issues and methods have been described previously [45] , [49] , [51] . To assess its performance characteristics , we used SIBZ to analyze the pBR322 plasmid ( bp ) using an energy threshold of kcal/mol . At superhelical density this analysis took 0 . 12 minutes to run on a MacBook Pro with dual Intel processors . On average there were 23 . 9 Z-form base pairs and 2 . 3 runs of transition . A total of 15 , 829 , 349 states were found to satisfy the energy cutoff condition . At superhelical density this analysis took 2 . 25 minutes to run on the same machine . In this case there were averages of 44 . 2 Z-form base pairs and 3 . 9 runs of transition , and 1 , 047 , 067 , 293 states satisfied the energy cutoff condition . We find that the execution time is almost constant at superhelix densities , and increases quadratically thereafter . The algorithm scales approximately linearly with sequence length . These performance characteristics suggest that the SIBZ analysis of the complete human genome at would take approximately 12 hours on a 100 CPU cluster of slightly faster ( viz . Opteron ) processors , if the sequence was partitioned into 5 kb segments that were analyzed individually . A similar analysis at would take approximately ten days . We note that there is substantial variability of execution speed depending on the attributes of the sequence being analyzed . SIBZ executes quickly on sequences that have one dominant Z-susceptible site . However , the analysis under identical conditions of a sequence in which there is substantial competition among numerous sites can take up to ten times longer . Z-Hunt was the first algorithm to predict Z-forming regions in DNA sequences based on energy considerations [35] . Although the original version only accepted sequences shorter than 1 Mbp , recently Z-Hunt was implemented to identify potential Z-forming regions in longer sequences , and specifically in the human genome [48] . In both versions of Z-Hunt a series of fixed length segments within a sequence are separately tested for their Z-forming potential . This is done by inserting the segment in a standard background , which is a circular plasmid in which the inserted segment is the only site that can undergo a structural transition . Z-Hunt then calculates the propensity of the segment to form Z-DNA under these standardized conditions . A Z-score is assigned to each segment by comparing its ability to adopt Z-form with those of a collection of randomly generated sequences . Unlike in SIBZ where we assign a superhelical density , Z-Hunt bases its Z-score on the superhelix density at which onset of transition occurs in this standard background . So there is no direct relationship between a segment's Z-score and its probability of transition at a specific superhelix density . Z-Hunt also provides no information about the competition among multiple Z-susceptible regions within the sequence . Z-Catcher uses a different approach to identify sites with Z-forming potential [47] . This algorithm includes a superhelix density as one of its inputs [47] . It treats the B-Z transition as a simple binary , “on-off” process at a single site . A critical threshold superhelix density is calculated for each individual segment of the sequence being analyzed , at which the energy required by the B-Z transition of that site exactly balances the stress energy released from this transition when it occurs alone in a standard background . If the input superhelix density is more negative than this critical , the region is said to be Z-forming . Its output is a list of predicted Z-forming sites , with no weight or probability assigned to them . Z-Catcher analyzes individual sites as though complete transition at that site is the only possibility . No consideration is given to how each site competes with all other sites having Z-forming potential within the rest of the sequence . This algorithm is purely mechanical; it does not analyze the equilibrium behavior of the sequence . SIBZ is the only method developed to date that analyzes the fully competitive B-Z transition behavior of DNA sequences in situ at thermodynamic equilibrium under any level of negative superhelicity . It is the only approach that calculates the equilibrium probabilities of transition for each base pair under the given conditions . This provides a more realistic and rigorous analysis , and enables more direct comparisons to be made between its predictions and experimental results than are possible with either Z-Hunt or Z-Catcher .
The B-Z transition behavior of susceptible regions within a DNA sequence can vary in complicated and highly interdependent ways . This complexity arises because superhelical stresses globally couple together the transition behaviors of all base pairs that experience them . When one region undergoes transition , the change of twist involved fractionally relaxes the level of stress experienced by all other base pairs in the domain . This can be seen from Eq . ( 7 ) , where a change in the number of transformed base pairs causes a corresponding change in the residual superhelicity experienced by the entire domain . In consequence , the transition behavior of each base pair is affected by the transformation of any other base pair . This global coupling is the primary reason why superhelical transitions cannot be understood by studying individual sites in isolation , but must be considered in their actual context . This competition between different Z-forming regions within a sequence can lead to a rich repertoire of complex , interactive behaviors . We illustrate this with sample calculations on a designed sequence containing two regions susceptible to Z-DNA formation . This sequence consists of 5000 T base pairs , into which we insert two Z-susceptible regions at distant locations . The thymidine background is chosen to insure that only our inserted segments are susceptible to Z-formation . The first insert is a segment while the second is a segment that contains six Z-Z junctions . The segment is less costly to transform , because the Z-Z junctions in the segment are energetically expensive . However because it is shorter , transition at the segment also relieves less superhelicity . We used SIBZ to calculate the probability of transition of each of these regions over a range of negative superhelix densities . Plots of these probabilities as a function of are shown in Fig . 1 . Just beyond the onset of transition where is still small , the superhelical free energy of the untransformed state also is relatively small . Under these circumstance the energy relief afforded by transition is less than it is at more extreme superhelicities . So in this regime the magnitude of the transition energy is the dominant factor in determining which regions transform . This is why the shorter but energetically less costly Insert 1 is the first to transform , as shown in the figure . As increases the superhelical free energy becomes quadratically larger . Now transitions at longer sequences become more desirable because they relieve more superhelical stress energy . Under these circumstances the difference in transition free energy due to the ZZ-junctions becomes less important than the benefit afforded by transforming a substantially longer segment . For this reason a coupled transition-reversion event occurs around , in which transition of Insert 2 is coupled to the reversion of Insert 1 back to B-form . In the range it is energetically too costly for both segments to transform to Z-DNA simultaneously , so such states occur infrequently at equilibrium . Transition of the long Insert 2 has caused substantial relaxation , which decreases the residual superhelicity felt by Insert 1 below the value that would drive it to transform . So at these stress levels the probability of transformation of Insert 1 drops to near zero . As increases beyond the point where Insert 2 has a high probability of being entirely in Z-form , the additional stress accumulates as negative residual superhelicity . When this reaches a sufficient level Insert 1 again transforms to Z-DNA . Beyond both inserts have high probabilities of simultaneously being in Z-form . One sees that there are specific intervals of superhelicity within which 1 ) neither insert transforms , 2 ) the first transforms but not the second , 3 ) the second transforms but not the first , or 4 ) both inserts transform simultaneously . The transition in this example experiences every logical possibility . When Z-Hunt is applied to this sequence it assigns Z-score of to Insert 1 , and to Insert 2 . The above analysis shows that when the competition between sites is considered there are circumstances when a region with a lower Z-score may transform while one with a higher Z-score does not . The analysis of individual sites in isolation simply does not capture the complexity of behavior that can occur in stress-driven transitions . We have analyzed the B-Z transition properties of three circular DNAs - the pBR322 plasmid , and the X174 bacteriophage and Bdellovibrio phage MH2K genomes . Fig . 2 shows the B-Z transition probability profiles of these sequences calculated at superhelical density . In each case the B-Z transition is substantially confined to a small number of sites where it is energetically most favorable , although there are several additional locations that have smaller , but still significant , transition probabilities . All sites with transforming potential are seen to be relatively short . The longest Z-forming regions found in any of these three sequences at contains 14 base pairs . In each of these sequences there is at least one predominant region whose probability of forming Z-DNA exceeds 70% . In pBR322 the largest peak has probability near 70% , and there are three other sites whose probabilities exceed 25% . Phage X174 contains a single region , slightly longer than that in pBR322 , whose transition probability is close to unity . Because this region is so dominant at this superhelix density , other portions of this sequence have only low probabilities of Z-formation . This dominance is a consequence of this site having a highly favorable transition energy over a sufficiently long region . We compared the performance of SIBZ with those of Z-Hunt and Z-Catcher when run on these sequences [35] , [47] , [48] . The energies from Table 1 were used in all three programs . When Z-Hunt was applied to the pBR322 sequence it found one 15 bp long segment at location 1448 with a Z-score of 2444 , and a 17 bp long segment at position 1407 with a Z-score of 1845 . As shown in Fig . 2a , SIBZ also finds these two peaks to be the most dominant , with the segment at location 1448 having the higher transition probability . This agrees with the relative Z-score rankings provided by Z-Hunt . However , the relative probabilities of these two regions are not proportional to their Z-score . Moreover , SIBZ documents several other regions that also have significant transition probabilities that Z-Hunt does not identify . One sees that , although the sites found by Z-Hunt agree with the major sites found by SIBZ , the latter provides more information regarding other Z-susceptible regions . The results from SIBZ , because they are expressed as transition probabilities of the fully competitive transition at the assumed superhelix density , are both more precise and more easily interpretable than are Z-scores . Z-Catcher does not identify any Z-susceptible regions in pBR322 at superhelix density . At superhelix density it finds the two dominant segments at locations 1407 and 1448 . It also finds two other Z-segments at positions where no Z-forming potential is seen by the other algorithms . The segment at position 1448 is found to comprise 28 base pairs , which is significantly longer than predicted either by Z-Hunt or by SIBZ . Thus , the predictions of Z-Catcher seem to differ considerably from those of either Z-Hunt or SIBZ . The behavior of a B-Z transition varies significantly as the negative superhelical density is modified . In general , as is increased , larger numbers of transformed base pairs are required to relieve torsional stresses . If the most energetically susceptible region is sufficiently long , the most favored way to do this would be by extending the transition to encompass increasing amounts of this region . However , the most Z-susceptible regions in natural DNA sequences are usually relatively short , as is seen in these three sequences . In that case what commonly happens is that , as ( and hence also the level of imposed stress ) increases , successively more energetically costly distinct Z-forming regions are transformed ( possibly coupled to reversion of other regions as shown above ) . In either of these strategies the average number of base pairs adopting the Z-form increases with negative superhelical density . This is demonstrated in Fig . 3 , which shows the average number of Z-DNA base pairs as a function of for pBR322 and for X174 . One sees that in both sequences there is a threshold for the onset of transition around , and the expected number of transformed base pairs increases approximately linearly thereafter . ( We note that the onset of transition in Fig . 1 occurs at a slightly less extreme superhelix density than is seen in Fig . 3 . This occurs because according to Table 1 the shorter Z-susceptible insert in the constructed sequence has the lowest possible transition energy . This is not true of the most Z-susceptible site in either X174 or pBR322 . ) Z-DNA forming regions have been observed with two-dimensional gel electrophoresis in plasmids engineered to contain Z-susceptible inserts [37] , [56] . The susceptibility of a region to be driven into Z-form was found to depend on its base sequence and on the level of supercoiling of the plasmid . In one experiment Peck et al . inserted a sequence ( here called Sequence 1 ) into the BamHI site of pBR322 [56] . This is the most energetically susceptible sequence to Z-DNA formation , as shown in Table 1 . In another set of experiments pBR322 derivatives were analyzed , each of which contains an insert whose sequence includes “imperfections” relative to the optimal Z-forming segments [37] . Here we focus on two of these engineered plasmids , one with ( Sequence 2 ) inserted into the BamHI site of pBR322 and the other with ( Sequence 3 ) inserted into Pvu II site of pBR322 . The Z-forming insert sequences 1 and 2 both have the same length , each containing 16 dinucleotides ( i . e . 32 base pairs ) . This means they have the same ability to relieve superhelical strain . However , Sequence 2 contains a Z-Z junction consisting of GG bases , which breaks the alternating syn-anti pattern and costs an extra 4 kcal/mol to form ( see Table 1 ) . So the onset of transition in Sequence 2 is expected to occur at a more extreme superhelix density because it has a higher total B-Z transition energy . Transition at Sequence 3 has two disadvantages relative to the others . It is shorter in length , containing 13 Z-forming dinucleotides instead of 16 , and it has two energetically costly “imperfections” . The GA and TC nucleotides in the anti-syn conformation cost 3 . 4 kcal/mol each , resulting in an additional transition cost of 6 . 8 kcal/mol relative to a perfect alternating CG sequence . Therefore , the critical superhelix density for complete Z-formation of this region is expected to be substantially higher than for either Sequence 1 or 2 . Fig . 4 shows the probabilities predicted by SIBZ of the inserted sequences transforming to Z-DNA in each of these three pBR322 derivatives , plotted as a function of superhelical density . The curves labeled Sequence 1 , Sequence 2 and Sequence 3 refer to the plasmids with those as their inserts . Experimentally measurements have been made of the critical superhelical density required to flip the entire inserted sequence into Z-DNA [37] , [56] . These analyses regarded the transition as two-state , analogous to an “on-off” switch , and determined the critical superhelicity at which these inserted segments were switched on . These critical densities are shown in column 3 of Table 2 . It is difficult to make exact comparisons between our theoretical predictions and these experimental results because they do not involve entirely comparable quantities . The experiments measure a critical for completion of an “on-off” , two-state B-Z transition , while SIBZ calculates the equilibrium probability of the plasmid experiencing any amount of transition . However , the trend observed in the SIBZ results of Fig . 4 closely agrees with experimental data . Transition occurs at the least extreme superhelix density in the plasmid with the Sequence 1 insert , later in the plasmid with Sequence 2 insert , and lastly in the plasmid with the Sequence 3 insert . Moreover , the horizontal displacement between these curves is about 0 . 0025 between Sequence 1 and Sequence 2 , and about 0 . 008 between Sequence 2 and Sequence 3 . These agree closely with the experimentally measured differences . In order to directly compare the experimental finding with the SIBZ results we must decide what level of transition corresponds to the “on-off” switch having been thrown . We define this to occur when the probability of Z-form is 80% . Column 4 in Table 2 shows the superhelix densities at which this occurs for each of the three inserts , as determined from the curves in Fig . 4 . At this level SIBZ results are seen to agree closely with the experimentally measured values . We next compared the predictions of SIBZ with the results of antibody binding experiments that identified Z-forming regions in the human c-myc gene [29] , [30] . Here the formation of Z-DNA is driven by the negative superhelicity that is generated when the gene is transcribed . This was confirmed by the observation that Z-formation was suppressed when transcription was inhibited . The first experiment used anti-Z antibodies to isolate regions of Z-DNA formation [29] . They found three restriction fragments from the c-myc gene region that showed antibody reactivity when the gene was being transcribed . These three fragments are all located in the upstream region of the gene in proximity to its three promoters . Fig . 5 shows the SIBZ transition profile of a 5kb region around the c-myc gene that spans these locations . This profile was calculated at superhelical density , a reasonable value for transcriptionally driven superhelicity [6] , [7] . The three large , gray horizontal bars labeled Z1 , Z2 , and Z3 identify the segments that were found in this experiment to contain Z-forming regions [29] . Five of the six sites identified by SIBZ as having the highest Z-forming probabilities occur within these three segments . The peak that is not within any of the three experimentally identified fragments is located around position 2100 in Fig . 5 . Perhaps this region was missed because of its proximity to the Z2 fragment . A second experiment identified the exact locations of the Z-forming sites within these three segments Z1 , Z2 , and Z3 [30] . This was done by isolating the fragments and inserting each individually into the circular pDPL6 plasmid [22] . Regions of Z-DNA were detected by measuring diethyl pyrocarbonate reactivity within these negatively superhelical constructs . One Z-form region was found experimentally in each of the Z1 and Z3 segments , and two such regions were found in Z2 . The experimentally determined locations of these regions are shown in Fig . 5 by small , blue horizontal bars located below each larger , labeled segment . These regions coincide precisely with the strongest Z-forming sites predicted by SIBZ . In each segment all the sites that are predicted to be most Z-susceptible are found experimentally to actually be in Z-form . SIBZ correctly identifies all sites within the three segments that occur in Z-form , with no false positives . This shows that our theoretical model produces results that agree precisely with those obtained by experiments . The experiments described above on the c-myc gene suggest that Z-forming regions may occur in proximity to transcription start sites . The accuracy of SIBZ in identifying these regions allows us to use it to address this question . To this end we analyzed 5 kb regions around the transcription start sites ( TSSs ) of 12 , 841 mouse genes . We oriented all genes to read left to right , and aligned them so their TSSs were at position 2500 . We calculated the probability of B-Z transition at each of the 5 , 000 positions in each sequence . We then averaged the transition probabilities at each position that were calculated for all the sequences . This analysis was performed at two superhelix densities , and . The results are shown in Fig . 6 . A substantial enrichment of predicted Z-form sites is observed immediately upstream of the TSS at both superhelix densities , with the number of these sites increasing with . The number of predicted sites immediately downstream from the TSS is approximately half the maximum number immediately upstream . The density of predicted Z-form regions in the far upstream , inferentially intergenic portions of the sequences approximately equals that in the far downstream , inferentially transcribed regions , and is approximately 30% of the maximum at both superhelix densities . To analyze this data further we define a region within an individual sequence as Z-forming if its probability of B-Z transition exceeds 80% . At superhelix density a total of 2519 of the 12841 genes ( 19 . 6% ) are found to have one or more Z-forming regions within 1 , 000 bp upstream of ( i . e . 5′ to ) their TSS . At the more extreme superhelix density of this number increases to 4269 ( 33 . 2% ) . There is a clear enrichment of Z-forming regions directly upstream of TSSs relative to downstream . At there are 1083 genes that have one or more predicted Z-forming regions within 50 base pairs upstream of the TSS , and 572 genes with such a site within 50 base pairs downstream , a nearly two-fold change . At the corresponding numbers are 2077 genes and 1304 genes , respectively . We also find that the majority of these 12 , 841 mouse genes contain Z-susceptible regions somewhere within their 5 kb regions . At we observe that 32 . 7% of these genes do not contain a Z-forming region ( at the 80% level ) anywhere in their sequence . This percentage drops to 12 . 7% when . A similar analysis was performed by Droege using Z-Catcher to analyze a large collection of human genes [47] . That work documented a similar enrichment of predicted Z-susceptible regions in the 5′ flanks of genes . Our results confirm and reinforce theirs , although the methods used in the two analyzes are not equivalent .
In this paper we have developed the first statistical mechanical method to analyze the competitive B-Z transition within long superhelical DNA domains of specified base sequence . The output of this method is the probability of transition calculated for each base pair in the sequence , rather than simply a list of sites or a Z-score . We have demonstrated the essentially competitive character of these transitions , and shown how the transition behavior can change in complicated ways with the level of imposed superhelicity . We find that there are regions with clear Z-forming potential in genomic DNA sequences . Our results agree in detail with experimental measurements of both the onset of transitions and the locations of Z-forming regions . Our analysis of 12 , 841 mouse genes documents a substantial increase in the occurrence of potential Z-forming regions immediately upstream from transcription start sites . At a superhelical density of we find that 33 . 2% of these genes have one or more Z-forming regions within 1 , 000 bp upstream of their TSS . Approximately half of these have such a site within 50 bp 5′ of the TSS . We note that in eukaryotes this superhelical density is attained in these regions through transcription-driven superhelicity [7] . This suggests that Z-DNA could play roles in transcriptional regulation . However , the fact that less than half the genes have Z-forming regions in their immediate 5′ flanks ( i . e . within 1 kb of the TSS ) suggests that there may be distinct classes of genes , some of which use such sites for regulatory purposes while other do not . Also , since Z-forming regions are relatively short , around 14 base pairs , there is room for them to be interspersed with other motifs , including A+T rich regions . These issues will be addressed more fully in a subsequent paper . When we compare the results of SIBZ with those of Z-Hunt and Z-Catcher we find that Z-Hunt and SIBZ agree in identifying the most dominant sites . However , SIBZ also identifies several sites where the probability of transition remains significant , that neither of the other methods find . Z-Catcher seems to be less sensitive than either of the others , only identifying the most susceptible sites when its input superhelix density is relatively large . Neither Z-Hunt nor Z-Catcher analyze the actual B-Z transition behavior of a superhelical DNA sequence , which is competitive and can vary in highly complex ways with the superhelix density . Instead they seek only to identify those individual sites within the sequence that have the greatest potential to form Z-DNA . They say nothing about how these sites compete , and provide no information about transition probabilities that can be directly compared with experimental results . We note , however , that Z-Hunt and Z-Catcher still may prove useful for specific purposes . In particular , in our experience each is substantially faster than SIBZ . It is difficult to compare their relative speeds , especially when they are only accessible through websites . However , both Z-Hunt and Z-Catcher appear to return results on 5 kb sequences almost instantaneously , whereas SIBZ takes from ten seconds to three minutes depending on conditions and sequence characteristics . Since it seems to identify the major sites reasonably well , Z-Hunt in particular may serve as an initial screen for such sites , with SIBZ used to perform full analyses as needed . The methods presented here can be applied to any two-state transition , provided the geometry , deformability , and transition energetics of the states are known . In this regard the best characterized DNA transition is strand separation . Both enthalpies and entropies of denaturation have been measured for every base pair and every choice of its nearest neighbors . The dependence of the free energy on ionic strength also is known . Together these allow one to predict how this transition behavior will vary with changing ionic conditions and temperature , as well as with superhelicity . At present the energetics of the B-Z transition are not so well characterized . It is known that specific alternating purine-pyrimidine sequences are substantially favored for Z-formation , and their transition free energies have been measured . But no quantitative information is currently available regarding how these free energies partition between entropy and enthalpy , nor about their ionic strength dependences . However , it has been reported that the B-Z transition shows little temperature dependence in the range between C and C [57] . This suggests that entropy changes are much smaller for B-Z transitions than for denaturation , where the DNA is disordered and its interactions with the solvent are thereby substantially altered . The extremely close accord documented here between the predictions of SIBZ and experimental results suggests that the transition energetics we use are accurate . We note that this accuracy is achieved without having any tunable parameters in our model . Although SIBZ is effective at analyzing the B-Z transition behavior of a supercoiled DNA molecule , it still focuses on only part of the complete picture . It is well known that local strand separation at A-T rich regions also occurs in negatively supercoiled molecules . To enable the accurate analysis of the full transition behavior of superhelical DNA one must include both types of transitions in a unified model . This would permit all sites susceptible to SIDD and/or to B-Z transitions to compete . We are working to develop such a model . A website is available ( http://benham . genomecenter . ucdavis . edu ) where the members of the scientific community may submit sequences of interest to them for analysis by the SIBZ algorithm . The sequence must be either in FASTA format or in a file that contains sequence characters exclusively . Sequences of any length up to 10 kb may be submitted , although sequences of length around 5 kb are preferred . This site may also be used for SIDD analysis of the same sequences . In the near future we hope to analyze the SIBZ characteristics of a large number of genomic sequences , up to and including complete genomes of model organisms . The results for each sequence will be posted in a database on the same Web site as they are completed . | We present the SIBZ algorithm that calculates the equilibrium properties of the transition from right-handed B-form to left-handed Z-form in a DNA sequence that is subjected to imposed stresses . SIBZ calculates the probability of transition of each base pair in a user-defined sequence . By examining illustrative examples , we show that the transition behaviors of all Z-susceptible regions in a sequence are coupled together by the imposed stresses . We show that the results produced by SIBZ agree closely with experimental observations of both the onset of transitions and the locations of Z-form sites in molecules of specified sequence . By analyzing 12 , 841 mouse genes , we show that sites susceptible to the B-Z transition cluster upstream from gene start sites . As this is where stresses generated by transcription accumulate , these sites may actually experience this transition when the genes involved are being expressed . This suggests that these transitions may serve regulatory functions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"physics",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology",
"mathematics/statistics",
"biophysics"
] | 2011 | Theoretical Analysis of the Stress Induced B-Z Transition in Superhelical DNA |
In eukaryotes , sphingolipids ( SLs ) are important membrane components and powerful signaling molecules . In Leishmania , the major group of SLs is inositol phosphorylceramide ( IPC ) , which is common in yeast and Trypanosomatids but absent in mammals . In contrast , sphingomyelin is not synthesized by Leishmania but is abundant in mammals . In the promastigote stage in vitro , Leishmania use SL metabolism as a major pathway to produce ethanolamine ( EtN ) , a metabolite essential for survival and differentiation from non-virulent procyclics to highly virulent metacyclics . To further probe SL metabolism , we identified a gene encoding a putative neutral sphingomyelinase ( SMase ) and/or IPC hydrolase ( IPCase ) , designated ISCL ( Inositol phosphoSphingolipid phospholipase C-Like ) . Despite the lack of sphingomyelin synthesis , L . major promastigotes exhibited a potent SMase activity which was abolished upon deletion of ISCL , and increased following over-expression by episomal complementation . ISCL-dependent activity with sphingomyelin was about 20 fold greater than that seen with IPC . Null mutants of ISCL ( iscl− ) showed modest accumulation of IPC , but grew and differentiated normally in vitro . Interestingly , iscl− mutants did not induce lesion pathology in the susceptible BALB/c mice , yet persisted indefinitely at low levels at the site of infection . Notably , the acute virulence of iscl− was completely restored by the expression of ISCL or heterologous mammalian or fungal SMases , but not by fungal proteins exhibiting only IPCase activity . Together , these findings strongly suggest that degradation of host-derived sphingomyelin plays a pivotal role in the proliferation of Leishmania in mammalian hosts and the manifestation of acute disease pathology .
Leishmania parasites infect 10–12 million people worldwide , causing a spectrum of diseases known as leishmaniasis [1] . Transmitted by sandflies , these protozoan pathogens have tremendous negative impacts on public health worldwide , especially in developing countries [1] . During their life cycle , Leishmania parasites alternate between flagellate promastigotes which live in the midgut of sandfly and non-flagellate amastigotes which reside in the phagosome of mammalian macrophages . Control of Leishmaniasis has been hampered by the lack of a safe vaccine , limitation of frontline drugs , and the emergence of drug resistant strains [2] . To develop new and more cost effective drugs , it is necessary to understand the molecular mechanism of Leishmania pathogenesis . A number of surface molecules including lipophosphoglycan ( LPG ) , glycosylinositolphospholipids ( GIPLs ) , and the metalloprotease gp63 provide resistance to digestive enzymes , reactive oxygen species , and complement-mediated lysis [3] , [4] . Other virulence factors including the LACK antigen ( Leishmania homologue of receptors for activated C kinase ) [5] and cysteine proteases play important roles in modulating host immune response [6] . In addition to these well-studied virulence factors , recent work has provided insight into the function of sphingolipids ( SLs ) in Leishmania . SLs are ubiquitous membrane components in eukaryotes with well-documented functions in general membrane physiology , raft formation , cell-to-cell recognition , and signaling [7]–[10] . Unlike mammalian cells which synthesize sphingomyelin and glycosylsphingolipids to high abundance , the majority of SLs in Leishmania are unmodified inositol phosphorylceramide ( IPC ) , a class of lipids mainly found in fungi and plants [11] , [12] . The functions of SLs metabolism in L . major were recently probed using two deletion mutants: a spt2− strain that is SL-free due to the deletion of an essential subunit gene ( SPT2 ) of serine palmitoyltransferase , the first enzyme in the de novo synthesis of SLs ( Fig . S1 ) [12] , [13]; and a spl− strain that lacks the degradative enzyme sphingosine-1-phosphate lyase ( SPL ) ( Fig . S1 ) [14] . Both mutants were fully viable and replicated normally during log phase growth; however , they died quickly in stationary phase and failed to differentiate from non-infective procyclics to infective metacyclics ( metacyclogenesis ) [14] . Remarkably , supplementation of ethanolamine ( EtN ) or phosphoethanolamine ( a product of SPL , Fig . S1 ) completely reversed the viability and differentiation defects of both mutants [14] . EtN is likely used to synthesize plasmenylethanolamine ( PLE , the dominant phosphatidylethanolamine species in L . major ) , a class of plasmalogen lipid that is highly abundant in metacyclics [12] , [14] . These results indicate that a main function of SL synthesis and degradation is to generate EtN , a metabolite essential for survival and metacyclogenesis in Leishmania promastigotes ( Fig . S1 ) . The conversion from SLs to phosphoethanolamine could occur via two pathways: an IPC-independent route directly from sphingoid bases or ceramide , or an alternative route which requires the synthesis and degradation of IPC ( Fig . S1 ) . Contrary to the problems encountered during metacyclogenesis , spt2− and spl− amastigotes ( the non-motile forms that reside in macrophages ) were morphologically normal and fully infective [14]; in addition , expression of SPT2 ( mRNAs and protein ) was greatly downregulated in wild type ( WT ) amastigotes [12]; finally , amastigotes of WT and spt2− contained abundant amounts of IPC and PLE ( similar to WT promastigotes ) , despite the loss of de novo SL synthesis and EtN production [15] . Since IPC is not found in mammals , these results imply: 1 ) amastigotes acquire SL metabolites from the mammalian host , either directly or through the hydrolysis of host SLs to generate metabolite; and 2 ) salvage of host lipids may play important roles for the survival and proliferation of Leishmania amastigotes . To explore the functions of SL metabolism that are independent of EtN production , it is necessary to study the enzymes that are directly involved in the uptake and degradation of host SLs in Leishmania . In mammalian cells , the sphingomyelinase ( SMase ) -mediated sphingomyelin hydrolysis is the major pathway to produce stress-induced ceramide ( Fig . S2 ) [16] . Ceramide is a well-studied bioactive molecule implicated in many signaling processes including apoptosis , inflammation , growth and differentiation [17] . Among the five known classes of SMases , neutral SMases have emerged as the main candidates for the production of stress-induced ceramide [16] . Although yeasts do not synthesize sphingomyelin , they produce abundant amount of glycosylated inositol phosphoceramides ( glycosylated IPC ) [18] , [19] . The functional homologue of mammalian neutral SMase in yeasts is inositol phosphosphingolipid-phospholipase C or IPCase ( Fig . S2 ) [20] . In Saccharomyces cerevisiae , the activity and localization of IPCase ( ScISC1p; encoded by ScISC1 ) were regulated in a growth-dependent manner: predominantly in the endoplasmic reticulum ( ER ) during early log stage but associated with mitochondria in late logarithmic growth , which might lead to the activation of this enzyme by the anionic phospholipids in the mitochondrial membrane [21] , [22] . Deletion of ScISC1 resulted in a slow growth phenotype , suggesting it was required for the utilization of non-fermentable carbon sources and/or the respiration function of mitochondria [22] . The null mutant was also more sensitive to heat and high salt concentration , which seemed to be linked to the deregulation of several stress-response genes [23] , [24] . In the opportunistic fungal pathogen Cryptococcus neoformans , CnISC1 ( which encodes the IPCase in C . neoformans or CnISC1p ) was critical for the survival of this pathogen in macrophages and its dissemination to the brain [25] . Neither IPCase nor neutral SMase has been studied in protozoa . In this report , we identified a single ISCL gene in L . major homologous to the neutral SMase genes in mammals and the ISC1 genes in fungi ( Fig . S3 ) . To study its role in SL metabolism and pathogenesis , null mutants of ISCL were generated through targeted gene deletion and results suggest the ability to degrade sphingomyelin is essential for acute virulence in Leishmania .
Through a BLASTp search , ISCL ( system ID: LmjF08 . 0200 , 1962 bp ) was identified from the L . major genome ( www . genedb . org ) as the sole homologue of human neutral SMase 1 ( Genbank accession #NP_003071 ) and S . cerevisiae ISC1p ( Genbank accession #P40015 ) ( Fig . S3 ) . The predicted protein ( ISCL , 653 aa ) contains several conserved amino acids including Glu51 , Asp116 , Asp383 , and His384 ( Fig . S3 ) . From studies of Bacillus cereus SMase [26] , ScISC1p [27] , and human neutral SMase 1 [28] , these residues may be critical for Mg2+ binding , substrate recognition , and catalytic activity . In addition , ISCL possesses a P-loop motif ( His115 to Lys122 ) which is found in phosphatases and nucleotide-binding proteins and may be essential for catalytic efficiency [27] ) , and two predicted transmembrane helices near the C-terminus ( Ala447 to Arg466 and Trp612 to Val 634 ) which may tether the protein to anionic phospholipid-rich membranes [29] ( Fig . S3 ) . To examine the role of ISCL in SL degradation and virulence , null mutants were generated in L . major through two rounds of targeted gene replacement as previously described , since Leishmania are predominantly diploid [30] , [31] . Southern-blot analysis confirmed the loss of both ISCL alleles in two candidate iscl− ( ΔISCL::PAC/ΔISCL::BSD ) clonal lines ( Figs . 1A and S4 ) . As a control , these mutants were complemented with an episome ( pXG-ISCL ) which drove the overexpression of ISCL ( Fig . S4 ) . The two clonal lines showed very similar phenotypes , and thus data for only clone #1-1 and its reconstituted control ( iscl−/+ISCL ) are presented here . In culture , iscl− promastigotes grew normally with a doubling time of ∼7 . 0 hours during logarithmic growth phase , indicating ISCL is not required for survival or replication in the insect stage ( Fig . 1B ) . The iscl− mutants also attained similar densities as WT parasites in stationary phase ( 2 . 5–3 . 3×107 cells/ml ) ( Fig . 1B ) . Interestingly , although iscl− promastigotes exhibited normal morphology in log phase and the first 1–2 days in stationary phase ( Fig . 1C–D ) , they became progressively less elongated in late stationary phase ( Fig . 1C–D ) . Microscopic evaluations revealed that after 3 days in stationary phase , 30–40% of iscl− cells became round ( round cells were defined as those with the length of the long axis less than twice the length of the short axis ) whereas only 10–20% of WT cells were round ( Fig . 1C–D ) . This difference became more pronounced as cultures continued to age , with 70–80% of round cells in iscl− versus 24–38% in WT after 5 days in stationary phase ( Fig . 1C–D ) . This round phenotype is characteristic of unhealthy promastigotes , consistent with the increased percentage of dead cells as 35–49% of iscl− became permeable to propidium iodide versus 10–20% of WT after 3–4 days in stationary phase ( Fig . 1E ) . These viability and shape defects did not arise from programmed cell death as iscl− parasites showed no characteristics of apoptosis ( such as the exposure of phosphatidylserine or PtS , data not shown ) . Importantly , stationary phase defects were solely due to the loss of ISCL , as the iscl−/+ISCL and ISCL+/− ( heterozygote ) parasites showed normal viability and morphology ( Fig . 1 C–E and data not shown ) . Finally , these viability and shape defects could not be reversed by EtN ( Fig . 1D–E ) , which is different from the stationary phase defects exhibited by spt2− and spl− mutants [14] . For Leishmania promastigotes , the cessation of growth in stationary phase coincides with the onset of metacyclogenesis , i . e . differentiation from non-infective procyclic forms to infective metacyclic forms [32] . Metacyclics can be distinguished from procyclics based on morphology , reactivity to lectins and monoclonal antibodies , and density gradient sedimentation [33] , [34] . Since mutant parasites showed altered morphology and increased death in stationary phase , it was important to examine whether the loss of ISCL affected metacyclogenesis . To do so , we isolated metacyclics from stationary phase iscl− parasites after a Ficoll density gradient centrifugation [34] . These iscl− metacyclics had very similar morphology as those purified from WT and iscl−/+ISCL parasites ( Fig . 2A ) . The percentage of iscl− metacyclics increased progressively and peaked at 10–13% after 3–4 days in stationary phase , as seen with WT and iscl−/+ISCL parasites ( Fig . 2B ) . Similar results were observed when metacyclics were isolated using the peanut agglutination method which is based on cell surface carbohydrate and antigenic changes between metacyclics and procyclics [33] ( data not shown ) . Together , these data suggest ISCL is not required for metacyclogenesis , although is critically involved in the maintenance of cell shape , and , to a lesser degree , cell viability in late stationary phase . Since ISCL is a homolog of mammalian neutral SMase and fungal ISC1p , we tested whether it is required for the hydrolysis of sphingomyelin , IPC , or both . Briefly , Leishmania lysates were incubated with Triton X100/lipid mixed micelles prepared as described by Okamoto et al . [29] with minor modifications . When a NBD-labeled C6 sphingomyelin was used as substrate with whole cell extracts from WT log phase promastigotes , ceramide ( one of the degradative products of SMase ) was detected by thin layer chromatography ( TLC ) ( Fig . 3A–B ) . This high level of SMase activity was comparable to what was observed from mammalian and yeast whole cell lysate [35] , [36] . In contrast , lysates from iscl− mutants did not induce ceramide production and were similar to the negative control made of boiled lysate from WT parasites ( Fig . 3A–B ) , indicating ISCL is required for the hydrolysis of sphingomyelin in vitro . As expected , the complemented strain iscl−/+ISCL , predicted to have increased expression ISCL due to overexpression from a multicopy episomal vector , exhibited higher SMase activity ( 5–7 times more than WT , Fig . 3A–B; Table 1 ) . Furthermore , we tested whether this ISCL-mediated SMase activity was sensitive to a specific inhibitor of mammalian neutral SMase 2 , GW4869 . As shown in Fig . 3A–B , at a final concentration of 1 µM , GW4869 completely shut down the degradation of sphingomyelin in iscl−/+ISCL , indicating that this ISCL-dependent Leishmania SMase activity is as sensitive as the murine neutral SMase 2 ( IC50 around 1 µM [37] ) . Next , we tested whether ISCL was required for the hydrolysis of sphingomyelin by intact promastigotes . To do so , parasites were metabolically labeled with NBD C6 sphingomyelin for 48 hours and cellular lipids were extracted and analyzed by TLC . As a control , we added NBD C6 sphingomyelin to growth medium without parasites for 48 hours and results showed very little spontaneous degradation ( Fig . 3C ) . Both WT and iscl−/+ISCL parasites were capable of sphingomyelin uptake and degradation , whereas iscl− mutants only showed uptake without hydrolysis ( Fig . 3C–D ) . Therefore , ISCL is essential for the SMase activity in vivo . Interestingly , iscl−/+ISCL parasites only caused ∼40% more degradation ( based on quantative analysis of the ceramide/ceramide + sphingomyelin ratio ) than WT parasites ( Fig . 3C–D ) . This differs from the SMase activity data acquired from cell lysate where iscl−/+ISCL showed 5–7 times more activity than WT ( Fig . 3A–B ) . We next examined whether Leishmania ISCL possessed IPCase activity . Assays were carried out similarly to those examining sphingomyelin hydrolysis , but using Triton X100/lipid mixed micelles containing PtS and NBD-labeled C12 IPC , followed by lipid extraction and TLC . As shown in Fig . 3E and 3F , WT parasites exhibited a detectable level of IPCase activity whereas iscl− mutants failed to degrade IPC . Episomal expression of ISCL in iscl−/+ISCL led to a marked increase of IPCase activity , suggesting ISCL is involved in the IPC degradation ( Fig . 3E–F ) . Notably , the specific activity of IPCase is 10–20 times lower than that of SMase ( Table 1 ) , suggesting sphingomyelin is the preferred substrate of ISCL . We then examined the cellular level of IPC , ceramide , and PLE ( which is synthesized from SL-derived EtN ) in WT , iscl− , and iscl−/+ISCL promastigotes by electrospray ionization mass spectrometry ( ESI/MS ) in the negative ion mode ( Table 2 ) . Abundances of IPC ( composed of d16:1/18:0-PI-Cer , d18:1/18:0-PI-Cer , and d16:1/18:0-PI-PhytoCer [12] ) and PLE ( composed of p18:0/18:2-PtE and p18:0/18:1-PtE [12] ) were estimated through comparison with appropriate internal standards ( d18:1/8:0-ceramide phosphate for IPC , d18:1/8:0-ceramide for ceramide , and p18:0/20:4-PtE for PLE ) . As summarized in Table 2 , iscl− mutants contained 53–59% more IPC and 30–52% less ceramide than WT and iscl−/+ISC parasites , consistent with a role for ISCL in IPC degradation . The cellular level of PLE in iscl− mutants was very similar to WT and iscl−/+ISCL parasites ( Table 2 ) . As described before [12] , L . major promastigotes did not contain sphingomyelin . Next , we assessed the virulence of iscl− mutants in BALB/c mice , which are highly susceptible to L . major . In footpad and ear infections , WT , ISCL+/− ( heterozygote ) , and iscl−/+ISCL parasites caused rapid progression of lesion which correlated with the increasing number of parasites in the infected tissue ( Fig . 4A–D and data not shown ) . Remarkably , neither stationary phase promastigotes nor purified metacyclics of iscl− mutants induced detectable lesions in the footpads or ears of BALB/c mice ( Fig . 4A–C ) . This virulence defect was not reversed by EtN ( Fig . 4A ) , which makes iscl− clearly different from the spt2− and spl− mutants [14] . The defect in cell viability was not sufficient to cause a complete loss of virulence in iscl− because we used 3-day old stationary phase promastigotes of which 85–95% of cells were healthy as judged by PI-exclusion ( Figs . 1E , 4A–B ) . In addition , metacyclic forms of iscl− that were morphologically normal and impermeable to propidium iodide also failed to cause pathology ( Fig . 3C–D and data not shown ) . Despite the lack of pathology , limiting dilution assays showed iscl− mutants were able to persist at the site of infection at very low levels ( 50–120 parasites/footpad ) for at least 7 months post infection ( Fig . 4D ) . Therefore , ISCL was essential for acute virulence and pathology but not long-term persistence . To corroborate the results obtained in mouse infections , we examined the virulence of iscl− mutants in an in vitro macrophage infection assay using peritoneal macrophages isolated from BALB/c mice [38] . When the infection was performed at 37 °C , iscl− mutants were able to bind and enter macrophages efficiently ( as shown by the 2-hour time points in Fig . 4E–F ) but these parasites failed to replicate thereafter and were quickly eliminated ( Fig . 4E–F ) . In contrast , when the infection was done at 33 °C , iscl− mutants were able to survive in macrophages , albeit at a lower level than WT and iscl−/+ISCL parasites ( 2–3 times lower in both infection rate and the number of parasites/100 macrophages , Fig . 3G–H ) . The ability to survive better at lower temperatures could be related to the ability to survive for long periods in peripheral mouse tissues like the footpad or ear . Our results also imply ISCL is involved in heat tolerance as the iscl−/+ISCL parasites , which overproduced ISCL from a multicopy episome ( pXG-ISCL ) , survived better than WT at 37 °C in macrophages ( Fig . 4E–F ) . The ability to survive better at a lower temperature could be related to the ability to survive for long periods in peripheral mouse tissues like the footpad or ear . To probe the importance of SMase vs . IPCase in Leishmania , we expressed SL hydrolases of known specificity in the iscl− mutant . These included human neutral SMase 1 ( NP_003071 ) , murine neutral SMase2 ( NM_021491 . 3 ) , and the S . cerevisiae ISC1p , as this latter enzyme exhibits both SMase and IPCase activity [39] . Human and murine ORFs were cloned into the pIR1SAT vector and integrated into the rRNA locus ( to generate iscl− SSU::hNSM1 or iscl− SSU::mNSM2 , respectively ) which results in high levels of expression [40] . Similarly , ScISC1 was inserted into the multicopy episomal vector pXG and transfected into the iscl− mutant yielding iscl−/+ScISC1 , which also yields high level of expression . We examined the ability of these transgenic parasites to hydrolyze sphingomyelin in vivo following provision of NBD C6 sphingomyelin as described above . Both human neutral SMase 1 and murine neutral SMase 2 possessed the ability to break down sphingomyelin when expressed in iscl− promastigotes ( Fig . 5A and 5B ) ; in contrast , control parasites transfected with the pIR vector alone ( iscl− SSU::pIR ) did not exhibit SMase activity despite maintaining sphingomyelin uptake ( Fig . 5A and 5B ) . Similarly , expression of ScISC1 restored the ability to degrade sphingomyelin in iscl− ( Fig . 5C–D ) . Consistent with these in vivo labeling results , we were able to detect strong SMase activity in the in vitro assay with whole cell lysates from iscl− SSU::hNSM1 , iscl− SSU::mNSM2 , and iscl−/+ScISC1 , but not from iscl− SSU::pIR ( data not shown ) . Similar in vitro experiments were performed to assess whether these SMases could degrade IPC ( using NBD C12 IPC as described above ) when expressed heterologously in iscl− mutants . As shown in Fig . 5E–F , ScISC1 and murine neutral SMase 2 induced modest IPC degradation , whereas the activity from human neutral SMase 1 was close to background . Similar to L . major ISCL , the IPCase activity exhibited by these enzymes were much lower compared to SMase activity ( Table 2 ) . Next we examined the effects of mammalian neutral SMases and ScISC1 on cell morphology and virulence in iscl− . As shown in Fig . 6A and 6B , both human neutral SMase 1 and murine neutral SMase 2 reversed the cell shape defects in iscl− during stationary phase and restored the ability to elicit lesion pathology in BALB/c mice ( lesion sizes correlated with parasite numbers in the footpads , Table S1 ) , whereas control parasites with the empty vector ( iscl− SSU::pIR ) behaved similarly to the parental iscl− mutants . Further analyses confirmed that iscl− SSU::hNSM1 and iscl− SSU::mNSM2 also had improved viability in late stationary phase ( by propidium exclusion flow cytometry ) and increased virulence in macrophage infections ( data not shown ) . Therefore , defects in iscl− can be complemented by mammalian neutral SMases . Similar to mammalian enzymes , ScISC1 completely restored morphology , viability , and virulence in iscl− parasites ( Fig . 6C and 6D ) , indicating it could functionally substitute ISCL . Again , lesion pathology induced by iscl−/+ScISC1 was consistent with parasite numbers over time ( Table S2 ) . Together , our complementation study strongly suggests that SMase activity is required for acute virulence in L . major . Recently , it was shown by the group of Maurizio Del Poeta ( Medical University of South Carolina , Charleston ) that unlike the ScISC1p , the ISC1 of Cryptococcus neoformans lacked significant activity with sphingomyelin while retaining activity with IPC ( M . Del Poeta , personal communication ) . Heterologous expression of an enzyme lacking SMase activity could thus serve as a probe to test the relative importance of SMase vs . IPCase activity . The C . neo ISC1 ORF was inserted into pXG and introduced into the iscl− mutant , yielding iscl−/+CnISC1 . Consistent with Del Poeta's findings , lysate from this line showed a high level of IPCase activity but little if any SMase activity ( Fig . 5C–F; Table 1 ) . In contrast to the ability of the SL hydrolases with significant SMase activity , expression of CnISC1 in iscl− did not restore their ability to induce lesion pathology in susceptible BALB/c mice infection ( Fig . 6C–D ) . Instead , these iscl−/+CnISC1 parasites persisted at low levels in BALB/c mice , similar to those iscl− mutants transfected with pIR1SAT vector only ( Fig . 6C–D ) ( Table S2 ) . Interestingly , the iscl−/+CnISC1 parasites exhibited normal morphology ( Fig . 6C ) and viability ( data not shown ) in stationary phase . Therefore , the cell shape defect appears to be separate from the virulence defect in iscl− . In total , our complementation experiments with SL hydrolases suggest the degradation of host sphingomyelin is essential for acute pathology in L . major , whereas the activity of IPCase is not required for virulence ( Table 1 and Fig . 6 ) . Surface glycoconjugates including LPG , GP63 , and GIPLs are important virulence factors in Leishmania . Because the degradation of IPC generates phosphoinositol ( Fig . S2 ) , which could be used to synthesize GPI-anchored molecules , we tested whether the deletion of ISCL affects the production of LPG or GP63 . Whole cell extracts from log phase promastigotes were subjected to western-blot analysis using monoclonal antibody WIC79 . 3 [41] , [42] and a rabbit anti-GP63 antiserum to detect LPG and GP63 , respectively . As illustrated in Fig . 7A , cellular levels of LPG and GP63 were not significantly altered in iscl− mutants or the iscl−/+ISCL parasites; in addition , both LPG and GP63 showed normal surface ( cell membrane ) localization in log and stationary phases ( Fig . 7B and 7C; data not shown ) . Together , these results indicate the loss of ISCL has no adverse effects on the expression or localization of GPI-anchored molecules . Therefore , the loss of acute virulence in iscl− is not due to defects in surface virulent factors . To determine the cellular localization of ISCL , a GFP-ISCL fusion protein was introduced into the iscl− mutant . Fluorescence microscopy revealed the distribution of GFP-ISCL to be similar to the staining pattern of the mitochondrial marker MitoTracker , in both log and stationary phase ( Fig . 8A–E and data not shown ) , suggesting this protein is mostly localized in the mitochondria . The GFP-ISCL was functional , as iscl−/+GFP-ISCL parasites were able to degrade sphingomyelin ( Fig . 8F ) and showed normal morphology and virulence ( data not shown ) . GFP-ISCL did not overlap with the plasma membrane , as revealed using an anti-LPG monoclonal antibody WIC 79 . 3 or the endoplasmic reticulum ( ER ) revealed by fluorescence microscopy with an anti-T . brucei Bip antiserum ( data not shown ) . Next , we examined whether ISCL could be secreted . Promastigotes of iscl−/+ISCL were grown from log to stationary phase and culture supernatant was examined for neutral SMase activity . As shown in Fig . S5 , contrary to the robust SMase activity from iscl−/+ISCL cells , no activity was detected in the supernatant . Adding sphingomyelin to the culture medium did not induce any ISCL secretion ( Fig . S5 ) . Similar results were observed with concentrated supernatant ( using filtration-based concentrators ) ( data not shown ) . Therefore , consistent with the localization study and the predicted ISCL sequence which lacks an obvious signal peptide , promastigotes do not secrete ISCL . The lack of acute virulence in iscl− mutants suggests SMase activity is essential for amastigote survival and replication in the mammalian host . However , methods for the generation of axenic L . major parasites amastigotes are not available , and lesion-derived or macrophage-derived amastigotes typically are contaminated with host material likely including neutral SMase . To circumvent this problem , we used L . amazonensis , a new world Leishmania species able to form axenic amastigotes that resemble closely to lesion-derived amastigotes in morphology , virulence , expression of stage-specific genes , and interaction with mammalian cells [43] , [44] . As shown in Fig . 9 , lysates from both promastigotes and amastigotes of L . amazonensis were able to hydrolyze sphingomyelin . Similar to L . major , the neutral SMase activity in L . amazonensis promastigotes and amastigotes was sensitive to GW4869 ( data not shown ) . After normalizing to protein levels , a two-fold higher level of activity was seen in amastigotes than promastigotes ( Fig . 9 ) ( p<0 . 05 ) . Thus L . amazonensis amastigotes express SMase which was shown genetically in L . major to be required for parasite survival and growth in the mammalian host .
In L . major , ISCL is the sole homologue of the neutral SMase genes in mammals and the ISC1 genes ( which encode Inositol phosphoSphingolipid phospholipase C or IPCase ) in fungi ( Figs . S2 and S3 ) . The predicted ISCL contains several well-conserved regions that are essential for the catalytic activity of SMase and ISC1p , e . g . a P-loop motif which may be involved in Mg2+ binding , and two transmembrane helices near the C-terminus , which may anchor the protein to mitochondrial membrane for activation [20] , [27] . Despite the lack of sphingomyelin synthesis , L . major parasites can actively take up and hydrolyze sphingomyelin and the neutral SMase activity clearly requires ISCL ( Fig . 3A–D ) . In addition to sphingomyelin , ISCL also contributes to the turnover of endogenous IPC as the iscl−/+ISCL parasites exhibited elevated IPCase activity whereas the activity in iscl− mutants was close to background ( Fig . 3E–F ) . Consistent with this result , iscl− mutants showed modest accumulation of IPC and less ceramide compared to WT and iscl−/+ISCL parasites ( Table 2 ) . Similar to mammalian neutral SMases , L . major ISCL exhibited much stronger activity with sphingomyelin than IPC ( 10–20 times higher , Table 1 ) , suggesting sphingomyelin is the preferred substrate . For promastigotes , the synthesis and degradation of sphingoid base is a major pathway to generate EtN [14] ( Fig . S1 ) . Although Leishmania do possess other pathways to generate EtN ( such as salvage from the medium ) , they are not sufficient to support growth and metacyclogenesis in the absence of SL metabolism as manifested by the spt2− and spl− mutants [14] . As illustrated in Fig . S1 , the production of phosphoethanolamine from SL metabolites could occur via two routes: an IPC independent route from sphingoid bases and ceramide; and an alternative route which requires the synthesis and degradation of IPC ( Fig . S1 ) . The latter is considered a reasonable pathway because SL metabolites such as sphingoid bases and ceramides are toxic and labile with low steady state concentrations , whereas IPC is highly abundance and could serve as a reservoir for EtN production [14] . However , our results clearly indicate although ISCL is responsible for the degradation of IPC , it is not essential for EtN production . First , iscl− mutants are fundamentally different from spt2− and spl− mutants: iscl− parasites grow like WT in vitro and form apparently normal metacyclics yet fail to induce pathology in mice , whereas spt2− and spl− mutants grow well but upon entry into stationary phase die rapidly and fail to differentiate; however , they retain the ability to yield acute disease pathology , as some surviving parasites are able to differentiate into amastigotes , which are able to acquire ethanolamine by savage [14]; second , defects in iscl− mutants cannot be reversed by exogenous EtN at all ( Figs . 1–2 , 4 ) ; third , the abundance of PLE was similar between iscl− mutants and WT parasites ( Table 2 ) . Therefore , there is no shortage of EtN in iscl− mutants , suggesting the IPC-independent pathway is sufficient for EtN/PLE biosynthesis in the absence of ISCL ( Fig . S1 ) . The most striking defect of iscl− mutants is their complete lack of acute virulence in susceptible BALB/c mice , as stationary phase promastigotes or purified metacyclics yielded no pathology even after 7–8 months ( Fig . 4A–D ) . In vitro macrophage infection indicated that iscl− were able to enter host cells but did not survive well , especially at 37 °C ( Fig . 4E–H ) . This virulence defect was completely reversed by the neutral SMases from human , mouse , and yeast ( Table 1 ) ( Fig . 6 ) . We confirmed the finding from Del Poeta's group that the C . neo ISC1 lacked significant SMase activity in our studies , when it was expressed heterologously in Leishmania ( Fig . 5 ) . Despite its strong IPCase activity , heterologous expression of CnISC1 failed to reverse the virulence defect ( Figs . 5–6 ) . Together , these complementation results strongly suggest the degradation of host-derived sphingomyelin is necessary and sufficient for parasite survival and replication in mammals , whereas the degradation of endogenous IPC by itself is not essential for Leishmania virulence . In agreement with this conclusion , axenic amastigotes of L . amazonensis showed strong SMase activity , about 2-fold higher than promastigotes ( Fig . 9 ) . Interestingly , most GFP-tagged ISCL is localized in the mitochondria during the promastigote stage ( Fig . 8 ) when cells can take in sphingomyelin for hydrolysis ( Fig . 3E–F ) . In Leishmania , lipid vesicles and lipid-protein complexes can be incorporated into the parasite plasma membrane through fusion or be taken up through an endocytic pathway including the flagellar pocket and endosomes [45] , [46] . Although mitochondria are not directly connected to the vesicular pathways , phospholipids such as PtS are synthesized in the ER and transported to the mitochondria transported in mammalian cells and yeast [47] , [48] . In S . cerevisiae , an ER-mitochondria tethering complex has been identified composed of proteins resident of both ER and mitochondria [49] . Consistent with these findings , our data imply lipids from the plasma membrane and/or endocytic compartments may contribute to the homeostasis of mitochondrial membrane ( where GFP-ISCL resides and the hydrolysis of SLs occurs ) . Localization of ISCL in amastigotes is currently under investigation . In S . cerevisiae , ScISC1p generates phytoceramide in the mitochondrial membrane and such activity may be important for the respiratory function of mitochondria and the metabolism of non-fermentable carbon sources [36] . As promastigotes , Leishmania acquire energy through the metabolism of sugars , amino acids and fatty acids [50] . It is possible that the mitochondrion-localized ISCL has similar functions as the Sc enzyme in regulating respiration and plays an important role in parasite growth in low sugar conditions . Consistent with this hypothesis , iscl− mutants showed poor viability and were more round in shape ( signs of cells under stress ) in late stationary phase when glucose became depleted ( Fig . 1 ) . Interestingly , although CnISC1p failed to complement the virulence defect in iscl− , it did restore the mutants' morphology and viability in late stationary phase , implying that while SMase activity is required for the survival and proliferation of amastigotes , IPC hydrolysis , however , may be involved in the maintenance of mitochondrial function in promastigotes . In addition to its potential role in mitochondria , we could envision several other mechanisms by which SMase activity may contribute to virulence; notably these mechanisms are not mutually exclusively . First , amastigotes of most Leishmania species reside within mature phagolysosomes which are enriched in amino acids and lipids but poor in carbohydrates [51] . To survive , these amastigotes need to salvage lipids ( including SLs ) from the host [15] and SMase ( along with other lipases ) may be required for the acquisition of nutrients . In addition , the degradation of host SLs by amastigotes could disrupt SL-dependent signaling pathways in macrophages . It has been reported that Leishmania donovani infection led to elevated ceramide levels in macrophages , which were responsible for the downregulation of classical PKC activity and the induction of PKCzeta ( an atypical Ca-independent stress kinase ) , as well as the ceramide-activated protein phosphatases . These changes were associated with the inhibition of NF-kappaB transactivation and the suppression of nitric oxide generation [52]–[54] . In the future , molecular interactions between iscl− mutants and host phagocytes will be evaluated to determine whether sphingomyelin degradation is required for the inhibition of proinflammatory cytokine release . In addition , the ability of iscl− mutants to persist without pathology provides an excellent platform to study the long-term , asymptomatic infection where the interaction between Leishmania and host is poorly understood . Our study revealed an essential yet previously unrecognized role of SL degradation in L . major virulence . Despite the lack of sphingomyelin biosynthesis , Leishmania parasites possess an ISCL-dependent neutral SMase activity . Deletion of ISCL completely abolished acute disease pathology in mice . ISCL is also required for the turnover of IPC , but is not required for the production of EtN . Future studies will determine the molecular mechanism by which host-SL degradation contributes to acute virulence .
BALB/c ( female , 7–8 weeks old ) mice were purchased from Charles River Laboratories International . All procedures involving mice were approved by the Animal Care and Use Committee at Texas Tech University ( PHS Approved Animal Welfare Assurance No . A3629-01 ) . N-[6-[ ( 7-nitro-2-1 , 3-benzoxadiazol-4-yl ) amino]hexanoyl]-sphingosine-1-phosphocholine ( NBD C6-sphingomyelin ) was purchased from Invitrogen Corporation . N-[12-[ ( 7-nitro-2-1 , 3-benzoxadiazol-4-yl ) amino]dodecanoyl]-sphingosine-1-phosphoinositol ( NBD C12-IPC ) was custom-synthesized by Avanti Polar lipids . All other chemicals were purchased from VWR International unless specified otherwise . The open reading frame ( ORF ) of ISCL ( LmjF08 . 0200 ) was amplified by PCR from L . major genomic DNA using primer pairs 5′ISCL ORF BamHI ( attactGGATCCACCATGTCGCACGCATCGACCTT , P58 ) and 3′ISCL ORF BamHI ( attactGGATCCCTACAACTTCTTCAGCT , P59 ) . The resulting DNA fragment was digested with BamHI and cloned in the pXG vector [55] as pXG-ISCL ( B83 ) or the pIR1SAT vector [56] as pIR1SAT-ISCL ( B106 ) . After confirming its sequence , ISCL ORF was cloned into the pXG-GFP+2′ vector to generate pXG-GFP-ISCL ( B103 ) , which was used in localization studies . To generate the knock-out constructs for ISCL , the predicted 5′- and 3′-untranslated regions ( ∼1 Kb each ) were PCR amplified and cloned in tandem in the pUC18 vector; genes conferring resistance to puromycin ( PAC ) and blasticidin ( BSD ) were inserted between the 5′- and 3′-untranslated regions to generate pUC-KO-ISCL:PAC ( B85 ) and pUC-KO-ISCL:BSD ( B84 ) . ORFs of human neutral SMase 1 ( accession #NP_003071 ) and mouse neutral SMase 2 ( accession #NM_021491 . 3 ) , derived from constructs generated in Dr . Yusuf Hannun's lab ( Medical University of South Carolina ) , were cloned in the pIR1SAT vector as pIR1SAT-hNSM1 ( B142 ) and pIR1SAT-mNSM2 ( B141 ) , respectively . ISC1 genes from S . cerevisiae ( ScISC1 , accession #P40015 ) and C . neoformans ( CnISC1 , accession #DQ487762 ) were PCR amplified and cloned in the pIR1SAT vector as pIR1SAT-ScISC1 ( B108 ) and pIR1SAT-CnISC1 ( B107 ) , respectively . L . major LV39 clone 5 ( Rho/SU/59/P ) promastigotes were grown in M199 medium with 10% fetal bovine serum and other supplements as described [57] . L . amazonensis ( MHOM/BR/77/LTB0016 ) promastigotes and axenic amastigotes were cultured as previously described [43] , [58] . Growth rate was determined by monitoring the density of culture over time using a hemacytometer . Cell viability was measured by flow cytometry using an Accuri C6 Flow Cytometer after staining with 5 µg/ml of propidium iodide . Metacyclics were isolated from stationary phase culture using the density gradient method [34] and/or the peanut agglutination method [33] . The ISCL alleles were sequentially replaced by puromycin ( PAC ) and blasticidin ( BSD ) resistance genes to generate the iscl− mutant ( ΔISCL::BSD/ΔISCL::PAC ) . Transfection and selection of promastigotes were performed as previous described [57] . To confirm the deletion of ISCL , genomic DNA was digested with NotI plus BamHI , resolved on a 0 . 8% agarose gel , transferred to a nitrocellulose membrane , and hybridized with a [32P]-labeled DNA probe . To re-constitute ISCL expression , pXG-ISCL was introduced into iscl− and referred to as iscl−/+ISCL ( ΔISCL::BSD/ΔISCL::PAC+ pXG-ISCL ) . To test whether iscl− can be complemented by mammalian neutral SMases , linearized DNA ( using SwaI ) from pIR1SAT-hNSM1 or pIR1SAT-mNSM2 were integrated into the small ribosomal subunit site of iscl− mutants to generate iscl− SSU::hNSM1 or iscl− SSU::mNSM2 . To test whether iscl− can be complemented by fungal ISC1 genes , mutants were transfected with pIR1SAT-ScISC1 or pIR1SAT-CnISC1 to generate iscl−/+ScISC1 or iscl−/+CnISC1 . To study the localization of GFP-tagged ISCL protein , WT parasites were transfected with pXG-GFP-ISCL and WT [pXG-GFP-ISCL] cells were selected based on resistance to G418 . To analyze the morphology of iscl− mutants , log and stationary phase promastigotes were fixed in 3 . 7% formaldehyde and percentages of round cells ( defined as those with the length of the long axis less than twice the length of the short axis ) were determined using a hemacytometer . About 200 randomly selected cells were counted in each experiment . For fluorescence microscopy , parasites were stained with 350 nM of Mitotracker Red 580 ( Invitrogen ) for 30min in darkness . Cells were then attached to poly-lysine coated cover slips , washed twice with phosphate buffered saline ( PBS ) , once with 50% ethanol , and stained with 2 . 5 µg/ml of Hoechst 33342 for 10min . Images were acquired using an Olympus BX50 Upright Fluorescence Microscope equipped with a digital camera . Localizations of LPG ( primary antibody: WIC79 . 3 [42]; secondary antibody: goat anti-mouse IgG-Texas Red ) and GP63 ( primary antibody: monoclonal anti-L . major GP63 antibody from Cedarlane Inc . ; secondary antibody: goat anti-mouse IgG-FITC ) were determined by indirect immuno-fluorescence microscopy as we previously described [12] . Peritoneal macrophages from BALB/c mice were isolated and infected with purified stationary phase promastigotes ( opsonized with C57BL6 mouse serum ) at a ratio of ten parasites per one macrophage as previously described [38] . Percentages of infected macrophages and the number of parasites per 100 macrophages were determined microscopically after staining with 2 . 5 µg/ml of Hoechst 33342 . Virulence of promastigotes was evaluated in BALB/c mice using footpad infection [59] and ear lobe infection [60] , [61] . For footpad assay , late stationary phase promastigotes ( 3 days after the onset of stationary phase ) or purified metacyclics ( prepared from 3–4 days old stationary phase culture ) were resuspended in DMEM and injected into the footpads of 8-week old female BALB/c mice ( 6–10 mice per group ) at 1×106 cells/mouse ( stationary phase promastigotes ) or 2×105 cells/mouse ( metacyclics ) . For ear lobe infection , stationary phase parasites were inoculated intradermally into the ear lobes of 8-week old female BALB/c mice ( 5 mice per group ) at 1×105 cells/mouse . Lesion sizes were measured weekly using a vernier caliper and parasite numbers in the infected tissue were determined by limiting dilution assay [59] . Mice infected with WT or iscl−/+ISCL parasites were sacrificed when their lesions became overly large ( over 2 . 5 mm for footpad infection and over 5 . 0 mm for ear infection ) . Log phase promastigotes were suspended in a lysis buffer ( 25mM Tris pH7 . 5 , 0 . 1% Triton X100 , 1× protease inhibitor ) at 2 . 0×108 cells/ml and incubated for 5 min on ice . Protein concentration was determined by the micro-BCA assay . Triton X100/lipid mixed micelles were prepared as previously described [29] with minor modifications . For neutral SMase assay , 40 µg of Leishmania protein ( ∼20 µl of lysate ) was incubated in 100 µl of buffer containing 50mM Tris pH7 . 5 , 5mM MgCl2 , 5mM dithiothreitol , 0 . 1% Triton X100 , 11 nmol of PtS ( Avanti ) , 2 . 8 nmol of unlabeled sphingomyelin ( Avanti ) , and 0 . 8 nmol of NBD C6-sphingomyelin . After incubation at room temperature for 60 min , 1 ml of chloroform , 0 . 5 ml of methanol , and 0 . 2 ml of water were added to each reaction and lipid was extracted , dried , and resuspended in 20 µl of chloroform: methanol ( 1∶2 ) . Thin layer chromatography ( TLC ) was performed as we previously described [12] and plates were scanned with a Storm 860 phosphoimager . Results were normalized to nmol/µg/hour after subtracting the value of negative control . 0 . 1 unit of Bacillus cereus SMase ( Sigma ) was used as a positive control and boiled WT lysate ( 40 µg ) was used as a negative control . For IPCase assay , similar experiments were performed except: 1 ) lysate was incubated in the absence of sphingomyelin and presence of 0 . 8 nmol of NBD C12-IPC; 2 ) TLC plates were developed in a different solvent ( chloroform∶methanol∶water = 65∶24∶5 ) ; and 3 ) 0 . 1 unit of Bacillus cereus phosphatidylinositol phospholipase C ( PI-PLC , Sigma ) was used as a positive control . Promastigotes were inoculated in M199 medium without fetal bovine serum at 7 . 0×105 cells/ml and labeled with 5 µM of NBD C6-sphingomyelin . After 48 h , cells were washed with PBS and total lipids were extracted and analyzed by TLC as described above for neutral SMase assay . Lipid extraction and analysis by ESI/MS ( negative ion mode ) was performed as previously described [15] with minor modifications . A N-octanoyl-D-erythro-sphingosine-1-phosphate ( d18:1/8:0 ceramide phosphate , FW = 505 . 5 , 1 . 0×108 molecules/cell ) was used as a standard for IPC; a 1-O-1′- ( Z ) -octadecenyl-2-arachidoyl-sn-glycero-3-phosphoethanolamine ( p18:0/20:4-PE , FW = 751 . 6 , 2 . 0×108 molecules/cell ) was used as a standard for PLE; and a N-octanoyl-D-erythro-sphingosine ( d18:1/8:0 ceramide , FW = 425 . 7 , 1 . 0×108 molecules/cell ) was used as a standard for ceramide . All three internal standards were added prior to lipid extraction . Promastigotes were collected and resuspended in PBS at 1×108cells/ml . Cell extracts were prepared and western-blotting were performed as previously described [12] . Primary antibodies include the rabbit anti-GP63 polyclonal antiserum ( a generous gift from Dr . KP Chang at Rosalind Franklin University of Medicine and Science ) ( 1∶10000 ) , monoclonal anti-L . major LPG antibody WIC79 . 3 [42] ( 1∶5000 ) , and monoclonal anti-α-tubulin antibody ( Sigma ) ( 1∶8000 ) ; secondary antibodies include the goat anti-rabbit or anti-mouse IgG Ab–HRP conjugated ( 1∶2000 ) . | Leishmania are obligate intracellular parasites responsible for a spectrum of diseases in humans ranging from self-healing skin lesions to deadly visceral infections . To survive , they must downregulate the microbicidal activity and scavenge nutrients from the host . Although Leishmania parasites do not synthesize sphingomyelin , which is an abundant lipid in mammals , they do possess the ability to hydrolyze sphingomyelin . This neutral sphingomyelinase ( SMase ) activity is dependent on an ISCL gene , which is also required for the degradation of inositol phosphorylceramide , the dominant sphingolipid in Leishmania . Deletion of ISCL in L . major led to a complete loss of SMase activity but no obvious defects in growth or differentiation in vitro . Remarkably , null mutants of ISCL failed to induce any detectable pathology in mammals yet were able to persist at low levels indefinitely . Such defect was completely reversed when a functional neutral SMase was introduced into the mutant . In total , our results suggest that degradation of host-derived sphingomyelin by Leishmania is essential for acute virulence . Further studies will elucidate the pivotal role of sphingolipid homeostasis in the molecular interaction between Leishmania parasites and their mammalian hosts . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/protozoal",
"infections",
"genetics",
"and",
"genomics/gene",
"function",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/parasitology"
] | 2009 | Degradation of Host Sphingomyelin Is Essential for Leishmania Virulence |
Between October 2001 and April 2002 , five cases of acute flaccid paralysis ( AFP ) associated with type 2 vaccine-derived polioviruses ( VDPVs ) were reported in the southern province of the Republic of Madagascar . To determine viral factors that favor the emergence of these pathogenic VDPVs , we analyzed in detail their genomic and phenotypic characteristics and compared them with co-circulating enteroviruses . These VDPVs appeared to belong to two independent recombinant lineages with sequences from the type 2 strain of the oral poliovaccine ( OPV ) in the 5′-half of the genome and sequences derived from unidentified species C enteroviruses ( HEV-C ) in the 3′-half . VDPV strains showed characteristics similar to those of wild neurovirulent viruses including neurovirulence in poliovirus-receptor transgenic mice . We looked for other VDPVs and for circulating enteroviruses in 316 stools collected from healthy children living in the small area where most of the AFP cases occurred . We found vaccine PVs , two VDPVs similar to those found in AFP cases , some echoviruses , and above all , many serotypes of coxsackie A viruses belonging to HEV-C , with substantial genetic diversity . Several coxsackie viruses A17 and A13 carried nucleotide sequences closely related to the 2C and the 3Dpol coding regions of the VDPVs , respectively . There was also evidence of multiple genetic recombination events among the HEV-C resulting in numerous recombinant genotypes . This indicates that co-circulation of HEV-C and OPV strains is associated with evolution by recombination , resulting in unexpectedly extensive viral diversity in small human populations in some tropical regions . This probably contributed to the emergence of recombinant VDPVs . These findings give further insight into viral ecosystems and the evolutionary processes that shape viral biodiversity .
Polioviruses ( PVs ) , members of the Enterovirus genus in the Picornaviridae family , are major human pathogens causing the acute paralytic disease poliomyelitis . The human enteroviruses ( HEV ) are classified into five species , HEV-A to -D , and the PV species ( PV-1 to −3 ) . The species HEV-C includes several serotypes of coxsackie A virus , and segregates in the same phylogenetic cluster ( cluster C ) as the PV species [1] . Enteroviruses , including PVs , are small non-enveloped viruses with a positive-strand RNA genome about 7 . 5 kb long . The single large coding region of the genome is flanked by 5′- and 3′-UTR . The coding region is translated as a single polyprotein that is processed by viral proteases to yield the mature viral proteins including the capsid proteins VP1 to VP4 and non-structural proteins including proteases and the RNA-dependent RNA polymerase 3Dpol . The World Health Organization's program for global eradication of poliomyelitis is based on immunization with the oral PV vaccine ( OPV ) . The attenuated OPV strains of the three PV serotypes ( Sabin 1 , 2 , and 3 ) replicate in the gut of OPV recipients where they efficiently mimic natural infection and thereby induce type-specific humoral and mucosal immunity . This strategy has reduced the incidence of polio worldwide by over 99% since the start of the global eradication program in 1988 , and has restricted wild PV circulation to countries in western and central Africa and southern Asia [2] . However , replication of OPV in humans is frequently accompanied by genetic changes in the vaccine virus . The changes can include reversion of key attenuating mutations and acquisition of other mutations throughout the genome . PV evolves very quickly , partly due to the high error frequency in RNA synthesis: roughly 10−4 per base per replication cycle [3] . In addition , recombination contributes to the variability of PV strains [4 , 5] . Intertypic recombination is a frequent phenomenon in OPV vaccinees , and strains with a recombinant genome have been isolated from both healthy vaccinees and from patients with vaccine-associated poliomyelitis [6–9] . The reversion of the OPV strains to neurovirulence is the underlying mechanism for the rare cases of vaccine-associated paralytic poliomyelitis ( VAPP ) among OPV recipients and their close contacts , and the occurrence of polio outbreaks associated with circulating vaccine-derived PV ( VDPV ) [10 , 11] . The first outbreak of poliomyelitis ( 21 cases ) associated with VDPVs , was reported in 2000–2001 , in the Dominican Republic and Haiti [12] . Subsequently , outbreaks due to VDPVs occurred in the Philippines , China , Indonesia , Cambodia , Madagascar , and more recently in Myanmar and Nigeria [13–17] . Prolonged circulation of type 2 VDPVs , responsible for 30 cases of AFP from 1983 to 1993 , has been retrospectively demonstrated in Egypt [18] . The duration of VDPV circulation before the outbreaks was estimated through nucleotide divergence to be generally from about 1 to 2 . 5 years , however , it could reach 10 years in Egypt [16 , 18 , 19] . Except in China , all VDPVs implicated in outbreaks were recombinants originating from OPV strains; it was suggested that large parts of the genomic regions encoding the non-structural proteins in these strains were derived from unknown non-polio enterovirus related to HEV-C [11–16 , 18 , 20] . In most cases , low vaccine coverage is thought to have allowed the circulation of OPV strains in non-vaccinated children and the subsequent genetic and phenotypic drift of these strains to pathogenic circulating VDPVs [16] . The emergence of epidemic VDPVs threatens the success of the program for global eradication of poliomyelitis . Improved surveillance and vaccine strategies limiting VDPV spread are urgently required . Other than low vaccine coverage , little is known about the viral factors and conditions that favor the emergence and circulation of VDPVs [16 , 19 , 21] . In Madagascar , type 2 recombinant VDPVs were identified as the causative agent of five cases of poliomyelitis that occurred from October 2001 to April 2002 [14] . We analyzed the implicated VDPV isolates and compared their sequences with those of co-circulating enteroviruses isolated from healthy children living in the small area where most of the poliomyelitis cases were reported . We demonstrate substantial co-circulation and evolution of HEV-C and OPV strains by recombination resulting in an unexpected genetic diversity . This gives further insight into the characteristics of viral ecosystems , evolution processes and factors that could favor the emergence of pathogenic recombinant VDPVs .
Five type 2 PV strains were isolated from stool samples of each of the children with AFP collected in the Toliara ( MAD 29 strain ) and Tolagnaro ( MAD 04 , 05 , 06 , and 07 strains ) districts ( Figure 1 ) . Overlapping reverse transcriptase ( RT ) -PCR products covering the entire genome of each isolate were sequenced ( accession numbers for nt sequence data are given below ) . The nt sequences of MAD 29 , isolated in 2001 , differed from those of the other strains isolated in 2002 . In contrast , the genomes of the strains isolated during 2002 ( MAD 04 to MAD 07 ) were closely related: MAD 04 , 05 , and 06 exhibited only 4 or 5 nt differences and MAD 07 , the most divergent , differed from the three others at 18 to 22 nt positions . Therefore , we focused mainly on MAD 04 , considered to be the 2002 prototype strain and on MAD 29 . The nt sequences of MAD 04 , MAD 29 , and the Sabin 2 strain were aligned and compared ( Figure 2 ) . Two different genomic regions could be clearly distinguished: the 5′-halves of the genomes were all similar and appeared to be derived from Sabin 2 with differences at few positions; in contrast , the 3′-halves of MAD 04 and MAD 29 diverged substantially from that of the Sabin 2 genome . The genomic region encoding the capsid protein VP1 ( 903 nt ) is widely used in studies of the molecular epidemiology of PV strains and in this region , MAD 04 and MAD 29 differed from Sabin 2 by 23 and 9 substitutions , including 18 and 6 synonymous ones , respectively . Only three of these substitutions were shared by MAD 04 and MAD 29 . The general molecular clock of PV has been estimated to be about 1% nt substitutions per year from studies on circulating strains and on viruses excreted from chronically infected immunodeficients [5 , 22–24] . A more precise estimation relies on the percentage of substitutions per synonymous site . In particular , from the analysis of similar recombinant type 2 VDPVs circulating in Egypt , the evolution rate of the capsid VP1 region appeared to be 2 . 5 ( ± 0 . 7 ) . 10−2 substitutions per synonymous site and per year [18] . This suggests that VDPVs MAD 04 and MAD 29 had been circulating before isolation for about 22 to 40 mo and 7 to 13 mo , respectively . The non-vaccine part of MAD 04 and MAD 29 genomes differed substantially from each other and from the Sabin 2 genome ( from 12 . 9% to 16 . 5% nt; Figure 2 ) . The percentages of nt divergence were similar to those that differentiate the 3′-halves of different PV or HEV-C genomes . For example , the 3′-half of the genome ( 3 , 730 nt ) of Sabin 2 differs by 18 . 0% from that of the prototype strain G12 of coxsackievirus A17 ( CA17 ) . The sites of recombination in the genomes of the Madagascan VDPVs , i . e . , the junction between the vaccine part and non-vaccine part of their genome , were in the region encoding the C-terminal third of protease 2A ( a 149-amino acid protein ) . According to the sequence alignments presented in Figure 2 , the likely sites of recombination in the genome of MAD 04 and other VDPV strains isolated in 2002 ( MAD 05 to MAD 07 ) were all close to nt 3 , 801 ( codon 139 of 2A ) . The likely site of recombination in MAD 29 was different , and close to nt 3 , 708 ( codon 108 of 2A ) . The patterns of nt substitutions in the Sabin 2 regions of the VDPV genomes , the sequences of the non-vaccine genomic regions , and the positions of the recombination sites between these two regions indicated that the PV strains isolated in Madagascar in 2001 ( MAD 29 ) and those isolated in 2002 ( MAD 04 to 07 ) represented two different recombinant VDPV lineages that had emerged and evolved independently . The substitutions in the vaccine part of the VDPV genomes are shown in Table 1 . Some of them may have affected the phenotype of the vaccine strain Sabin 2 . To detect any effect of these substitutions on antigenic structure , MAD 04 and MAD 29 strains were compared to the Sabin 2 strain using a Sabin 2 strain-specific monoclonal antibody ( MAb ) collection ( Table 2 ) [25 , 26] . Several MAbs directed against the neutralization antigenic sites 1a and 3a of Sabin 2 had only weak neutralizing effects on both strains MAD 04 and MAD 29 . Interestingly all MAbs directed against the antigenic site 2a neutralized MAD 04 and Sabin 2 but did not neutralize MAD 29 . In contrast , one MAb ( MAb 1108 ) directed against the neutralization antigenic site 3b showed a weak reactivity against MAD 04 but neutralized both MAD 29 and Sabin 2 . Neither MAD 04 nor MAD 29 was neutralized by MAb 1233 , highly specific for Sabin 2 , and used for intratypic analysis of PV [27] . Thus , the antigenic structures of both VDPVs differed from that of Sabin 2 . Furthermore , the two strains exhibited distinct neutralizing epitope maps , in agreement with them representing two different VDPV lineages . The location of amino acid mutations likely to be responsible for the MAb reactivity of both VDPVs is shown in Table 1 . The plaque sizes of the VDPV strains isolated in Madagascar were measured ( Table 3 ) . The VDPVs produced larger plaques than the Sabin 2 strain ( results significant for four of five VDPVs ) . We also tested the temperature sensitivity of the VDPV by measuring the titers of the viral stocks at optimal ( 36 . 0 °C ) and supra-optimal ( 40 . 2 °C ) temperature ( RCT test: Table 3 ) . The non-temperature-sensitive and highly neurovirulent type 2 vaccine-derived environmental strain S2/4568 was used as control [28] . Unlike Sabin 2 , the difference in titer of all VDPVs and S2/4568 at these two temperatures was small , indicating that they were not temperature-sensitive . We also evaluated the pathogenicity of the Madagascan VDPVs by IC and IP inoculation of groups of homozygous PVR-Tg21 mice [29] with a single virus dose per animal ( Figure 3 ) . In contrast to Sabin 2 that did not induce disease , all the Madagascan VDPVs induced paralysis in all IC inoculated animals . They were also all neurovirulent following IP inoculation but the severity of the symptoms differed between strains: strain MAD 04 was the most neurovirulent strain and MAD 29 was the least neurovirulent strain . This was confirmed when the paralytic dose affecting 50% of the inoculated mice ( PD50 ) was determined by IC inoculation ( Table 3 ) . The Madagascan VDPVs did not have either the temperature sensitive or attenuated phenotypes that characterize the OPV strains , and therefore appeared to be similar to wild PVs . Following the VDPV outbreak , two rounds of local vaccination campaigns with trivalent OPV were implemented at 1-mo interval . Two weeks after the second round , 316 stool specimens were collected from healthy children living in the rural villages of the Tolagnaro district to check the effect of these campaigns on the circulation of VDPVs . We also exploited these samples to look for other enteroviruses and to identify putative parents of the unidentified enterovirus sequences present in the VDPV genomes . Healthy children were recruited in the three villages where the AFP cases were reported and in three nearby villages , all located in a swampy coastal area , 2 to 8 kilometers away from each other ( Figure 1 and Table 4 ) . Inoculation of cultured human RD and HEp-2c cells and mouse L20B cells expressing the human PV receptor led to the isolation from these samples of 23 PVs and 71 other viruses inducing cytopathogenic effects only on human cells [30–32] ( Table 4 ) . Sixty-eight non-polio viruses were identified as enteroviruses by amplification by RT-PCR of the 5′ UTR regions using nt primers that target highly conserved enterovirus sequences . The 5′ UTR ( nt 215–582 , according to Sabin 2 nt sequences numbering ) and the regions encoding the capsid protein VP1 ( nt 3 , 067–3 , 376 ) and the nonstructural proteins 2C ( nt 4 , 123–4 , 383 ) and 3D ( nt 6 , 145–6 , 732 ) of most of the PV and other enterovirus isolates were sequenced . From the isolation and sequencing data , no evidence of co-infection by different enterovirus serotypes or genotypes was found . However , re-inoculation of cells with positive samples in the presence of neutralizing antibodies directed to the identified virus serotype was not performed to check the possible presence of a second one ( see also Discussion section ) . Most PV isolates were closely related to one of the original OPV strains with less than 0 . 5% nt divergence . However , one of the non-recombinant type 2 OPV isolates was more divergent , and the whole VP1 genomic region was sequenced: it showed 0 . 7% nt divergence from the Sabin 2 sequence ( 0 . 5% , in the three other genomic regions ) , indicating that this strain had been multiplying or circulating for about 8 months . Two PV isolates were OPV type 3 / type 1 intertypic recombinants , as commonly found in vaccinees [6] . Two type 2 isolates were closely related to the recombinant VDPVs of the MAD 04 lineage with less than 1 . 1% nt divergence in all sequenced genomic regions . Sequencing data of their whole VP1 genomic regions indicated that these new VDPVs ( VDPVs 65972 and 68266 ) were slightly more divergent from the Sabin 2 strain ( 2 . 8% and 3 . 1% nt divergence , respectively ) than their AFP counterparts ( 2 . 5% ) . Other than these two recombinant VDPVs , the sequences of the PVs isolated from healthy children provided no evidence of genetic recombination with non-OPV strains . The nt sequences encoding the VP1 capsid protein of enterovirus isolates contain serotype-specific information [33–35] . We used partial sequencing of the capsid VP1 region to identify 64 enteroviruses [33] . Several echoviruses of the HEV-B species were found including two echoviruses serotype 14 ( E14 ) , ten E19 , and one E25 . Surprisingly , coxsackie A viruses ( 51 isolates ) of the HEV-C species were the most frequent , and they included 12 CA11 , 16 CA13 , eight CA17 , one CA20 , and 14 CA24 . These findings indicated the persistence in the Tolagnaro district of VDPV isolates of the MAD 04 lineage despite two rounds of vaccination; they also show that many children were excreting non-poliovirus enteroviruses ( 21% ) in particular HEV-C ( 16% ) . The various enterovirus sequences were aligned and compared in phylogenetic trees . The phylogenetic relationships between the Madagascan HEV-C field isolates , their prototype strains , OPV strains , and the VDPVs isolated in the island and in other countries are shown in Figure 4 . The four last wild PV strains isolated in Madagascar , two type 1 strains ( PV1 . Mad96a and PV1 . Mad96b ) isolated in 1996 and two type 3 strains ( PV3 . Mad95 and PV3 . Mad97 ) isolated in 1995 and 1997 , respectively , were included . For phylogenetic trees , similar assignments for reconstruction of major clusters and , in most cases , of sub-clusters were obtained using both the maximum likelihood and the genetic distance matrix / neighbor-joining methods . As expected , the sequences of the two VDPVs isolated from healthy children ( VDPVs 65972 and 68266 ) clustered with those of the VDPVs MAD 04 and MAD 07 in the four phylogenetic trees ( Figure 4 ) confirming that they belong to the same recombinant lineage . According to VP1 sequences , HEV-C isolates displayed different patterns of genetic diversity depending on their serotype . CA13 and CA11 isolates appeared to be relatively heterogeneous: 83 to 100% VP1 nt identity within their respective clusters , and different sub-clusters could be observed . VP1 sequences of most CA17 and CA24 isolates were more homogeneous with 91%–98% nt identity within their respective clusters . However , isolates CA17 . 67610 , CA24 . 65902 , and CA24 . 68098 showed only 83%–85% , 72%–75% , and 75%–88% nt identity with their respective counterparts . Therefore , the VP1 nt sequences of these coxsackie A virus isolates showed a relatively high genetic diversity within each of their respective serotypes . In contrast to VP1 sequences , the nt sequences of the N-terminal part of protein 2C did not necessarily segregate according to the serotype of the isolate . The 2C sequences of the VDPVs of the MAD 04 lineage segregated neither with those of the Sabin 2 strain nor with those of other OPV strain serotypes but with those of the major group of CA17 field isolates ( 92%–94% nt identities; high reliability values ) . This result strongly suggests that a MAD 04 VDPV ancestor has acquired its non-OPV 2C sequences from a co-circulating CA17 isolate by genetic recombination or that both viruses acquired these sequences from a common ancestor virus . These VDPVs also showed relatively high nt identities ( 91%–93% ) with the wild PV1 . Mad96a and appeared related to a certain extent to the two other wild PV1 . Mad96b and PV3 . Mad97 ( 86%–90% nt identities ) and to the other VDPV MAD 29 ( 86%–87% nt identities ) . No other major change appeared in the groupings of the 2C phylogenetic tree compared to the groupings of the VP1 tree , except for the isolate CA24 . 67897 whose 2C sequences were distantly related to those of the other field CA24 isolates ( labeled by dotted lines in Figure 4 ) indicating that this isolate had acquired its 2C sequences by intratypic recombination . There were major differences between the groupings of isolates in the phylogenetic tree of the 3D nt sequences and those in the VP1 and 2C trees ( Figure 4 ) . The 3D sequences of many isolates , in particular those of the CA11 , CA13 , and CA17 field isolates , appeared to be split into distantly related clusters . VDPVs of the MAD 04 lineage showed 3D sequences closely related to those of four CA13 isolates ( 92%–93% nt identities; high reliability values ) . They were related to PV1 . Mad96a ( 90% nt identity ) and to a certain extent to two CA13 , to PV1 . Mad96b and to VDPV MAD 29 ( 87%–88% nt identities ) . The other CA13 isolates and all CA17 isolates were more distant ( 81%–85% nt identities ) . Recent intertypic recombination events could be inferred . In particular , the cluster containing CA13 . 68132 , CA17 . 68138 , CA17 . 68146 , and CA17 . 67591 ( 99% nt identities; high reliability value ) , and that with CA13v . 67900 , CA11v . 68123 , and CA11v . 68129 ( 94% nt identities ) indicate genetic exchanges between CA13 and CA17 isolates and between CA11 and CA13 isolates . According to the phylogenetic tree built with the 5′ UTR nt sequences , the isolates belonging to each of the major serotypes ( CA11 , CA13 , CA17 , and CA24 ) were in different clusters . This indicates that the 5′ UTR of these serotypes has frequently been subject to recombination . In order to estimate the number of different recombinant lineages among the Madagascan HEV-C isolates , we systematically considered incongruences between the four different phylogenetic trees ( Figure 4 ) that were supported by reliable values and/or observed using both the maximum likelihood and the genetic distance matrix/neighbor-joining methods . 19 HEV-C recombinant lineages differing by one of the sequenced fragments could be distinguished . In most cases , a good correlation was found between recombinant lineages and groups or subgroups of nt sequences observed in a single tree ( as mentioned above for the VP1 tree ) . Different serotypes and in some cases different recombinant lineages belonging to the same serotype were found in the same village . Moreover , a given recombinant lineage could be isolated in two different villages . We compared the polypeptides encoded: the peptide relationships did not necessarily parallel the nt relationships . The 2C-polypeptides ( 87 residues ) of the Madagascan VDPVs of the MAD 04 lineage differed from those of the closely related CA17 field isolates at one to two amino acid positions . They were strictly identical to those of PV1 . Mad96a and PV1 . Mad96b , to those of some other CA17 and CA11 isolates and even to those of some VDPVs isolated in other countries but different from the 2C peptide sequences of other field isolates ( 16 to 17 amino acids differences for the CA13 field isolates ) . These results are in good agreement with those of Brown et al . [36] indicating that the 2C peptide sequences of prototypes CA17 , CA11 , and CA20 are particularly similar to those of PVs . As expected , the peptide sequences of the 3D regions ( 196 residues ) are highly conserved for all serotypes and even the 3D amino acid sequences of the prototype strains CA13 and CA24 , as well as those of some HEV-C field isolates or those of the wild PV1 . MAd96a are strictly identical . The 3D polypeptides of the MAD 04 lineage differed from those of the four most closely-related CA13 field isolates at two to five amino acid positions , those of their most divergent HEV-C field isolates ( nt level ) showing no more than six different amino acids . These results confirmed that viral 2C and particularly 3D HEV-C polypeptides are highly conserved . They are probably poorly permissive to most amino acid modifications acquired by mutations or recombination . The corollary of this is that conserved peptidic sequences could favor genetic exchanges . Although the genetic exchanges between these enteroviruses appeared to have a limited effect on the variability of the peptide sequences , our findings indicated a substantial genetic diversity of HEV-C isolates in the Tolagnaro district due to nt divergence as well as to intertypic and interspecific recombination involving PV . As expected , phylogenetic analysis of the nt sequences of the HEV-B isolated in Madagascar yielded evidence of interspecific recombination events neither between the VDPVs and the three serotypes of HEV-B isolates ( Figure S1 ) nor between HEV-B and HEV-C isolates ( not shown ) .
Here , we describe two different lineages of type 2 PV/HEV-C recombinant VDPVs that appeared and circulated independently in Madagascar , and that induced paralytic poliomyelitis . The search for enterovirus circulating in the small area where most of the poliomyelitis cases occurred indicated an unexpectedly high diversity of coxsackie A viruses belonging to HEV-C that co-evolved by intertypic and interspecific recombination involving PVs . Circulation of endemic type 2 VDPVs and wild strains associated with low vaccine coverage in Egypt from 1983 to 1993 has been described [18] . The isolates appeared to be derived from a single OPV infection and all belong to the same lineage . The specificities of the outbreak we describe are that it occurred in a country where wild PV strains were eliminated by 1997 and that it was due to two different type 2 VDPV lineages . Studies that established the rates of infection and indirect immunity against PV in contacts of vaccinees indicate that the Sabin 2 strain spreads to unvaccinated children more easily than type 1 and 3 vaccine viruses [37–39] . Sabin 2 is highly immunogenic in vaccinees and induces higher seroconversion rates than do the other two OPV serotypes [38 , 40 , 41] . Nevertheless , the Sabin 2 strain also has the bad reputation of inducing the highest rate of VAPP following contact with vaccinees [9 , 42] . These characteristics , may explain why Sabin 2-derived viruses can in some circumstances , circulate in human populations and subsequently acquire pathogenic characteristics [43] . The discovery of two different lineages of type 2 VDPVs in Madagascar confirms this notion . Furthermore , new lineages of type 2 VDPVs were isolated from AFP cases in the southern part of Madagascar in 2005 ( Rakoto Andrianarivelo et al . unpublished data and [44] ) . Very recently , numerous poliomyelitis cases due to different type 2 VDPV lineages were reported in Nigeria , a country still endemic for wild PVs [17 , 45] . We report two VDPV isolates genetically similar to the MAD 04 lineage and isolated 2 months after the last AFP case and following two rounds of local vaccination campaigns; this indicates that this type 2 VDPV lineage was widespread and well established in the population and has not been cleared by these campaigns . However , two weeks after the second local round of vaccination only 7% of children were found to excrete Sabin-like PVs . Previous immunisation responses may have limited subsequent multiplication and excretion of vaccine strains , and numbers of excretors [39] . However , we cannot exclude the possibility that interference between OPV strains and the numerous endogenous enteroviruses circulating in the province , or some other host and environmental factors contributed to reducing vaccine strain multiplication and to limiting OPV immunogenicity [40 , 41] . Unfortunately , no data concerning OPV responses following the local vaccination campaigns were available . However , no further AFP cases due to this VDPV lineage have been reported in the area . This lineage is thought to have disappeared , possibly after the two national rounds of vaccination ( “national immunization days” ) that followed in September and October 2002 . The characterization of the enteroviruses co-circulating with the MAD 04 lineage in the Tolagnaro district indicated that both the frequency and diversity of HEV-C were substantial . Five of the six HEV-C serotypes known to grow in cultured cells were found ( three serotypes can only be isolated in new-born mice ) [36] . Moreover , the genetic diversity within each serotypes was high due to nt sequences divergence and number of subgroups distinguished and to frequent recombination events involving all four sequenced regions ( 19 different recombinant lineages differing by one sequenced fragment ) . Similar evolution processes , involving frequent intertypic recombination , have already been described mostly for HEV-B [46–52] and PVs [5 , 8 , 53 , 54] . In this study we show that recombination also contributes considerably to the genotypic diversity of HEV-C . Different HEV-C serotypes circulate in Cambodia and a previous study has pointed the relatively high frequency and the wide distribution of HEV-C in different regions of Madagascar [32 , 53] . However , this is the first time that such a high frequency and diversity of HEV-C isolates , and , in general , of isolates of the same RNA virus species , have been described in such a small area ( about 25 × 10 Km ) and in such a small population ( 316 children ) . Our observations thus shed a new light on the characteristics of viral ecosystems and their evolution . Most VDPV strains described as OPV/HEV-C recombinants were isolated in countries in which wild PVs have been eradicated , so the unidentified human enterovirus has been presumed to be HEV-C [ 11 , 12 , 14–16 , 18 , 20] . Up to now only a Sabin3/HEV-C recombinant from Cambodia was shown to be directly related to indigenous CA17 strains in the 2BC genomic region and to a CA13 isolate in the 3D region [53] . Here we show that recombinant VDPVs are similarly closely related to CA17 isolates in the 2C region and to CA13 isolates in the 3D region . These findings indicate that CA17 and CA13 isolates are particularly suitable partners for sharing nt sequences with PVs by means of recombination . Moreover , this study indicates for the first time that HEV-C isolates sharing sequences closely related to a VDPV lineage were co-circulating with this lineage in the very place where the outbreak occurred . These observations lend considerable support to the idea that there is frequent genetic recombination between OPV strains and at least some HEV-C serotypes , and that this plays a role in the emergence and/or evolution of VDPVs . Despite the recombinant OPV/HEV-C features of most VDPVs , field isolates with the capsid of HEV-C and non-structural parts from OPV were not found in this study . To our knowledge such recombinants have not been described elsewhere . The viral replication machinery or selection factors that are known to act in vivo to shape the features of intertypic OPV recombinant genomes [6] may exclude such HEV-C/OPV recombinants . This hypothesis was recently supported experimentally with recombinant viruses generated from PVs and CA20 or CA21 prototype strains [55] . Genetic recombination requires the co-infection of the host and cells by at least two parental viruses . In fact , search for viral mixtures was not the primary goal of the study and the viral isolation procedures were poorly adapted to detect them . However , recent inoculation of HEp-2c cells and RD cells with almost all PV positive samples , in the presence of a mixture of neutralizing antipoliovirus antibodies , indicated that about 25% of these samples contain at least one other enterovirus serotype that was masked on L20B cells during PV isolation ( not shown ) . It is interesting to note that the nt sequences of the CA17 and the CA13 isolates that are closely related to those of the MAD 04 lineage are also related to a certain degree to the sequences of the VDPV MAD 29 and , despite at least 5 y of nt drift , remain related to sequences of wild PVs isolated in 1996 and 1997 . Relationship between these isolates probably results from co-circulation of these viruses and evolution by recombination since many years throughout the Tolagnaro Province . This suggests that PVs and HEV-C have been occupying the same or at least overlapping viral ecological niches , both species contributing to evolution and the generation of diversity by intratypic , intertypic and interspecific recombination . These various considerations argue for a long-term evolution process involving wild or vaccine PVs and some HEV-C viruses ( at least CA17 and CA13 ) and lend support to the proposal that PVs and HEV-C should be considered to be members of a single species [5 , 36 , 55 , 56] . The functional role of interspecific genetic exchange in the evolution of PV/HEV-C recombinants is still unclear . Type 1 non-recombinant and vaccine/vaccine recombinant VDPVs have circulated for about one year in China and in Romania , respectively , showing that recombination with HEV-C is not essential for OPV strains to become circulating VDPVs [13 , 57] . Nevertheless , recombination frequency suggests that genetic exchanges may allow PV and HEV-C to evolve rapidly and to acquire some functions like those that are necessary for efficient circulation in the population . Although less likely we cannot exclude that interspecific recombination may be a neutral phenomenon and simply testify that OPV and HEV-C strains are well established in the population thereby increasing considerably the frequency of encounters and recombination . Indeed , the frequency of HEV-C circulation is high in both Madagascar and Cambodia , countries in hot humid tropical zones [32 , 53] . Possibly , the climate along with sanitation and hygiene are important risk factors for VDPVs and enterovirus spread . It is also plausible that particular physiological , immunological and genetic factors in the local human population help these viruses circulate and co-evolve . To our knowledge , the observed HEV-C frequency and biodiversity and the simultaneous presence of VDPVs in such small areas and such small human populations have not previously been described . This biodiversity combined with the poor polio vaccine coverage , may make the local ecosystem a “cauldron” particularly favorable for the emergence of new recombinant VDPVs and possibly new pathogenic coxsackie A virus strains . This argues strongly for an increased surveillance in such areas and for the continuation of studies to elucidate the viral , human and environmental factors that shape viral genetic diversity and contribute to the emergence of VDPVs .
Human HEp-2c , RD cells , and murine L20B cells ( murine L cells expressing the PV human receptor [31] ) were grown as monolayers in Dulbecco's modified Eagle medium supplemented with 5% fetal calf serum . The poliovaccine viruses , Sabin 1 , 2 and 3 were obtained from the WHO [Behringwerke ( S0+1 ) ] “master seeds” . The second passage at 34 °C in HEp-2c cells of the original seed was used to prepare viral stocks . VDPVs MAD 04 to MAD 07 and MAD 29 were isolated on human RD and murine L20B cells from specimens ( stools ) from poliomyelitis cases according to WHO recommendations for poliomyelitis surveillance . PV strain S2/4568 has been described previously: it is a highly neurovirulent and non-temperature sensitive vaccine-derived strain , of serotype 2 [28] . To determine the frequency and circulation of VDPVs field investigations were conducted on June 21 and 22 in rural villages of the district of Tolagnaro . 316 stool specimens were collected among healthy children . A baseline questionnaire , which included date of birth , sex , site of enrolment , and previous routine immunization based on health cards , was completed for each child . This investigation was conducted as part of the national AFP surveillance recommended by WHO for poliomyelitis surveillance purposes . It was organized with the agreement and help of the Madagascan Ministry of Health and Family planning and biological materials were collected after obtaining informed consent from the parents . Extracts of stool specimens were treated with chloroform and used to inoculate RD and HEp-2c cell lines , for enterovirus isolation , and L20B cells . All L20B PV isolates were characterized using microneutralization serotyping tests [30] . Isolates showing cytopathogenic effects only on HEp-2c or RD cell lines were considered to be non-polioviruses and analyzed further by molecular typing ( see below ) . Poliovirus isolates were analyzed by multiple restriction fragment length polymorphism assays involving amplification of two regions of the genome ( the VP3/VP1 capsid region and the 3Dpol-3′-UTR ) and the restriction enzymes DpnII , DdeI , HinfI and RsaI as previously described [58] . Strains identified by this method as mutant and recombinant PV vaccine strains were further analyzed by partial sequencing . Viral RNA was reverse transcribed as described previously either directly from viral stocks , following heat denaturation of the virions [8] , or following viral RNA extraction [49] . DNA fragments were amplified by PCR as described by Chevaliez et al . [49] using previously described primers [8 , 33 , 49] . Depending on the presence of a single or multiple bands in the gel , pooled PCR products ( 100 μl ) were either directly purified using the QIAquick PCR Purification kit ( Qiagen ) or excised from agarose gel following electrophoresis and purified with the QIAquick Gel Extraction kit ( Qiagen ) . The 5′-end of the viral genome was amplified with the 5′/3′ RACE kit ( Roche ) , as described in the manufacturer's protocol . Briefly , viral RNA was used for first-strand cDNA synthesis using the primer UC52 [8]; the cDNA was purified and a dA-tailing reaction was carried out . We then amplified the dA-tailed cDNA by PCR using the primer UC52 and oligo-dT . The amplified DNA fragments were directly sequenced using the Big-Dye Terminator Cycle Sequencing Ready Reaction Kit on the ABI Prism DNA 377 Sequencer ( Perkin-Elmer Applied Biosystems ) according to the manufacturer's protocol and using primers described in Guillot et al . and Caro et al . [8 , 33] . Alternatively , conditions for RT-PCR amplification and cycle sequencing were as described previously [18] , using the primers listed in Yang et al . and Kew et al . [12 , 18] . Sequencing was performed in both directions , and every nt position was sequenced at least once on each strand . A fragment of 299 to 322 bp corresponding to the 3′ third of VP1 capsid was compared with the corresponding region of available prototype sequences , using the CLUSTAL W alignment program [59] . The GenBank database was also screened for similar sequences using the FASTA program [60] . Scores were established for each strain according to nt identity and amino acid similarity with the closest prototype strains . The serotype of the field isolates was assumed to be that of the closest prototype strain according to the results of pairwise comparisons of nt sequences , as previously described [33] . In most cases , nt identities with the homologous prototype strains were higher than 75% [33] . However , for many CA24 isolates , nt identities were between 71% and 75% . In this case the putative serotype was supported by the serotype associated with the most similar enterovirus nt sequences present in data banks , usually giving higher nt identities ( >75% ) . Coxsackie A viruses serotypes 15 and 18 are now considered as antigenic variants of coxsackie A virus serotype 11 and 13 and are named in this work CA11v and CA13v , respectively [56] . Phylogenetic relationships between strains were established by comparing the sequences determined and aligning them with those of other known human enteroviruses , using the alignment program ClustaL W or Clustal X [59 , 61] . The degree of nt sequence identity and of protein similarity between strains was determined using the default scoring matrices . Complete genome sequences were compared following alignments with the plot-similarity program of GCG version 10 . 1 software ( Genetics Computer Group , Madison , Wisconsin ) , using a 50-nt sliding window and the default scoring matrix [62] . Phylogenetic relationships between sequences were inferred by the maximum likelihood method with PUZZLE 4 . 0 , which uses QUARTET PUZZLING as the tree search algorithm [63] . The Hasegawa , Kishino , and Yano ( HKY ) model of substitution for nt with a Ts/Tv of 8 . 0 was used [64] . Trees were constructed using neighbor-joining of PHYLIP ( Phylogeny Inference Package ) version 3 . 6 [65] and branch length given by Puzzle . The reliability of tree topology was estimated using 25 , 000 puzzle steps . Alternatively , phylogenetic relationships were inferred by DNADist/Neighbor of PHYLIP [65] and a genetic distance matrix was calculated using the F84 model of nt substitution with a transition/transversion ratio ( Ts/Tv ) of 8 . 0 . The robustness of phylogenies was estimated by bootstrap analyses with 1000 pseudoreplicate data sets generated with the SEQBOOT program . Trees were drawn with the TREEVIEW [66] or NJ Plot programs [67] . The temperature sensitivity of viruses was evaluated by studying the reproductive capacity of each virus strain at various temperatures ( standard RCT test ) . RCT is defined as the difference between the log10 virus titer of a viral stock measured at 36 . 0°C and that at 40 . 2°C . Titers were determined on RD cells by an endpoint micromethod after 5 days of incubation at the appropriate temperatures , and are expressed in TCID50 per ml [68] . Viruses with RCT values above 2 were considered to be temperature sensitive . See also Supporting Information for details ( Protocol S1 ) . Plaque diameter was determined by using virus-infected HEp-2c cells maintained under a 0 . 9% agarose overlay and stained after 3 days of incubation at 34 °C , in a 4% CO2 incubator . For each virus , the diameter of all isolated plaques ( about 30 plaques ) was measured and mean plaque diameter and standard deviation were calculated ( Protocol S1 ) . The antigenic properties of viruses were studied by a microneutralization assay as previously described [25 , 26] using PV Sabin-specific MAbs corresponding to antigenic sites 1 to 3 . One hundred TCID50 of the challenge virus were used in the test . Viruses were tested for neurovirulence in homozygous PVR-Tg21 mice which are susceptible to PV infection ( generous gift from A . Nomoto ) [29] . Groups of ten PVR-Tg21 mice ( 5 males and 5 females ) were inoculated intracerebrally ( ic; 40 μl ) and intraperitoneally ( ip; 1 ml ) with a single dose of virus ( 7 and 8 Log10 TCID50/mouse , respectively ) . Challenged mice were monitored daily for 14 days and clinical symptoms ( paresis , paralysis , or death ) recorded for each mouse . For evaluating the viral dose that induced paralysis or death in 50% of mice ( PD50 ) groups of eight ( 4 males and 4 females ) 5- to 6-week-old mice were used . Animals were inoculated ( IC ) with 40 μl of tenfold dilutions in Dulbecco's modified Eagle medium of virus stocks containing 0 . 1% fetal calf serum . Mice were inoculated to cover the viral titer range , causing disease in 100 to 0% of mice . In some cases , although the viral suspension with the highest or lowest titer available was used , the 100% or 0% paralytogenic dose could not be attained . To confirm the inoculated dose , viral suspensions were back-titrated after inoculation . Inoculated mice were monitored for 21 days and the PD50 was calculated by the method of Reed and Muench [68] . All experiments were conducted in full compliance with French regulations regarding laboratory animal welfare . Protocols were approved by the Veterinary Staff of the Central Animal Facility of Institut Pasteur .
The nt sequence data reported in this article are available from the EMBL GenBank under accession numbers AM084223-AM084225 and AM884184-AM884185 ( VDPV complete genomes ) . Partial genomic sequences are also available: AM774327-AM774334 ( other VDPVs ) , AM774339-AM774354 ( wild PVs ) , AM778603–778661 , AM779098–779160 , AM774410 , AM779258-AM779316 , and AM779413-AM779471 ( HEV-B and HEV-C isolates ) . The accession numbers for other sequences used in phylogenetic trees are D00820 ( EV70 ) , AY302440 ( E14 ) , AY302544 ( E19 ) , AY302549 ( E25 ) , AF499635 ( CA1 ) , AF499636 ( CA11 ) , AF499638 ( CA11v ) , AF499637 ( CA13 ) , AF499640 ( CA13v ) , AF499639 ( CA17 ) , AF499641 ( CA19 ) , AF499642 ( CA20 ) , AF546702 ( CA21 ) , AF499643 ( CA22 ) , D90457 ( CA24 ) , AF448782 ( PV2 . Egy88 ) , AF448783 ( PV2 . Egy93 ) , AF405669 ( PV1 . Hai00 ) , AF405690 ( PV1 . Dor00 ) , AB180070 ( PV1 . Phi03 ) , AB205395 ( PV3 . Cam02 ) . | Following extensive vaccination campaigns using the attenuated oral polio vaccine , wild polioviruses remain endemic in only a few countries . Nevertheless , several poliomyelitis outbreaks associated with vaccine-derived polioviruses ( VDPVs ) were reported in different parts of the world in recent years , particularly in Madagascar in 2002 . We analyzed the molecular characteristics of Madagascar VDPVs and compared them with those of co-circulating enteroviruses . These VDPVs appear to be recombinant viruses between vaccine polioviruses and human enteroviruses of species C ( HEV-C ) and to present phenotypic characteristics similar to those of wild polioviruses including pathogenicity . Similar VDPVs and other enteroviruses , including several HEV-C of different types , were found in the stools of healthy children living in neighboring villages to where most of the poliomyelitis cases occurred . Some HEV-Cs showed sequences closely related to those of VDPVs , indicating genetic recombination between these viruses and vaccine polioviruses . There was also evidence of multiple genetic recombination events among other HEV-C isolates resulting in numerous different genotypes . These findings indicate that co-circulation of HEV-C and vaccine polioviruses and their evolution by recombination results in unexpectedly extensive viral diversity , at least in some small human populations , probably contributing to the emergence of recombinant VDPVs . Results of this study give further insight into the world of viruses and their biodiversity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] | [
"primates",
"viruses",
"public",
"health",
"and",
"epidemiology",
"infectious",
"diseases",
"ecology",
"homo",
"(human)",
"virology",
"vertebrates",
"animals",
"genetics",
"and",
"genomics"
] | 2007 | Co-Circulation and Evolution of Polioviruses and Species C Enteroviruses in a District of Madagascar |
Mycetoma is a unique neglected tropical disease which is endemic in what is known as the “mycetoma belt” . The disease has many devastating impacts on patients and communities in endemic area and is characterised by massive deformity , destruction and disability . Mycetoma is commonly seen in the foot and hand and less frequent in other parts of the body . Mycetoma of the head and neck is a rarity and is associated with high morbidity and even mortality if not treated early . In this communication we report on 49 patients with head and neck mycetoma followed up at the Mycetoma Research Centre in Khartoum . Most of the reported patients had actinomycetoma and the majority were young adult males from mycetoma endemic areas in the Sudan . Most of them were students , farmers and workers . Prior to presentation the majority had long disease duration and the cause was multifactorial . Advanced disease with massive lesion , deformity and disability was the common presentation . There was no obvious history of local trauma , familial tendency or other predisposing factor identified in this group of patients . MRI and CT scan were the most accurate diagnostic tools to determine the disease extent . The treatment outcome was rather poor and characterised by low cure rate , poor outcome and high follows-up dropout . Such a gloomy outcome calls for structured and objective health education programs .
Mycetoma is one of the neglected tropical diseases , characterised by massive deformity , disability and can be fatal if not managed properly and timely [1–3] . It is a chronic , specific , granulomatous , progressive subcutaneous inflammatory disease that spreads to involve the skin , deep structures and bones [4 , 5] . The disease is caused by true fungi or by certain bacteria and hence it is usually classified into eumycetoma and actinomycetoma , respectively [6 , 7] . Madurella mycetomatis is the commonest eumycetoma causative agent while Streptomyces somaliensis and Nocardiae are the common causative organisms for actinomycetoma [8–10] . Mycetoma has a definite geographic distribution and it is endemic in what is known as the “Mycetoma Belt” that includes Sudan , Senegal , Somalia , South India , South America and Mexico; however , it is reported in many other countries [11–17] . The infection usually progresses slowly over many years and it is commonly painless and that may contribute to the late presentation of many patients [2 , 18] . The painless subcutaneous mass , multiple sinuses and discharge with grains is distinctive of this infection [1] Young adult males in the age range 20–40 years are more frequently affected [2 , 4] . Farmers , workers and students are affected most but no occupation is exempted [2 , 5] . The diagnosis of mycetoma is tedious and several tools are required to reach a proper diagnosis . These tools include imaging techniques such as radiography , ultrasonography , CT , MRI [19–21] , molecular techniques such as PCR [22] , serodiagnosis as ELISA , CIE [23 , 24] as well the classical grain culture and histopathological diagnosis [23] . Although the disease can be diagnosed clinically this is not accurate and can be misleading . Early lesions are amenable to medical and surgical treatment with good prognosis [24 , 25] . Generally , actinomycetoma responds to medical treatment in the form of combined antibiotics while eumycetoma requires both antifungal and surgical excision [26 , 27] . Late and advanced disease is difficult to treat , has poor prognosis and is associated with high recurrence and amputation rates [28] . Currently there is no preventive or control measurements as the route of infection , susceptibility and resistance to the infection are still an enigma and hence health education is essential to avoid the disease and its high morbidity and complications . Mycetoma of the head and neck region is a rarity , patients commonly present with massive lesions and is associated with poor prognosis and can be fatal . In this communication , the Mycetoma Research Centre of the University of Khartoum experience of managing 49 patients with mycetoma of the head and neck region is presented .
The study ethical clearance was obtained from Soba University Hospital Ethical Committee , it waived the need for consent . Statistical analysis was conducted using SPSS computer programme . Data was summarized as percentages for categorical variables and mean ± standard error of the mean ( SEM ) and median for continuous variables .
The 49 studied patients with confirmed head & neck mycetoma constituted 0 . 76% of the total MRC patient population seen during the study duration . In the present study , 33 patients ( 67 . 3% ) had actinomycetoma and 16 ( 32 . 7% ) had eumycetoma . There were 39 males ( 79 . 6% ) and 10 females ( 20 . 4% ) . Their ages ranged between 9 and 67 years with a mean age of 27 . 9 ± 14 . 7 years . 31 ( 63 . 3% ) of the patients were under 40 years-old at presentation and 23 ( 46 . 9% ) were in the age group 1–20 years . Only five patients ( 10 . 2% ) were more than 50 years of age at presentation . In this study , there were 14 students ( 28 . 6% ) , 11 workers ( 22 . 4% ) and 10 farmers ( 20 . 4% ) . Due to prolonged illness and disability , seven patients ( 14 . 3% ) were unemployed . There were five housewives ( 10 . 2% ) in this population . The majority of the patients , 27 ( 55 . 1% ) were from central Sudan; AL Jazeera State 10 ( 20 . 4% ) and Sinnar State 7 ( 14 . 3% ) . There were eight patients ( 16 . 3% ) from Kordofan States , seven patients ( 14 . 3% ) from Khartoum State , five patients ( 10 . 2% ) from Kassala State , four patients ( 8 . 1% ) form Darfour States and three patients ( 6 . 1% ) from the White Nile State . The disease duration at presentation ranged between one and 40 years with a mean duration of 11 . 23 ± 19 . 7 years . The majority of the patients 33 ( 67 . 3% ) had mycetoma for less than 10 year and 16 ( 32 . 7% ) of them had the disease for less than one year . Only three patients ( 6 . 1% ) had the disease for more than 30 years . Thirty patients ( 61 . 2% ) had history of discharge contained grains and the colour of the grains was yellow ( 38 . 8% ) , black ( 24 . 5% ) or white ( 8 . 2% ) . Pain at the mycetoma site , was not a frequent symptom among the study population; documented in only 11 patients ( 22 . 4% ) . Only fourteen patients ( 28 . 6% ) , recalled history of local trauma at the mycetoma site and three patients could not recall such a history . Concomitant other illness was documented in only two patients ( 4 . 1% ) . Four patients ( 8 . 2% ) had family history of mycetoma . The majority of patients , 36 ( 73 . 5% ) had recurrent disease and underwent previous surgical excisions; 23 patients ( 46 . 9% ) had one surgical excision , five patients ( 10 . 2% ) underwent two surgical excisions , six patients ( 12 . 2% ) had three previous surgical excisions while two patients ( 4 . 1% ) had more than three surgical excisions . The type of anaesthesia used ranged between general ( 41 . 8% ) and local ( 58 . 2% ) . Different parts of the head and neck were involved which included the frontal ( n = 12 ) , occipital ( n = 5 ) , parietal ( n = 1 ) and temporal region ( n = 1 ) . Multiple skull bones involvement was documented in 12 patients , ( Table 1 ) . Five patients had combined frontal and parietal and/ or temporal bone involvement . Two patients had combined occipito-temporo-parietal bones affection . One patient had massive sphenoid , ethmoid , maxillary , nasal bones , anterior cranial fossa , temporal , frontal and occipital bones and supra-orbital areas . One patient had infra-temporal fossa mycetoma extending to the nasopharynx involvement . Two patients had base of the skull and occipital mycetoma with cervical region extension . The orbit was involved in two patients . The upper eye lid , buccal cavity and cheek were affected in one each . Ten patients ( 20% ) had cervical mycetoma . Four patients ( 4% ) had intracranial lesions ( Figs . 1 , 2 , 3 ) . The mycetoma lesions were classified according to their sizes into small ( less than 5 cm ) , moderate lesion ( 5–10 cm ) and massive lesion ( >10cm ) . The study showed that , 20 patients ( 40 . 8% ) had massive lesions at presentation while 12 patients ( 24 . 5% ) had small lesions . At presentation , 30 patients ( 61 . 2% ) had lesions with sinuses; they were active in 15 patients ( 30 . 6% ) , healed in six patients ( 12 . 2% ) and nine patients ( 18 . 3% ) had both active and healed sinuses . Grains discharged through the sinuses were not detected on clinical examination in 37 patients ( 75 . 5% ) while in 12 patients ( 24 . 5% ) grains were detected . Local hyper-hydrosis at and around the mycetoma lesion was detected in one patient ( 2% ) . Regional lymph nodes enlargement was detected in six patients ( 12 . 2% ) . Dilated tortuous veins proximal to the mycetoma lesions were not detected in the present . One patient presented with massive intracranial eumycetoma with minimal skin and subcutaneous affection . At presentation 30 patients had skull and cervical X-Ray examination in at least two views and that showed normal findings in eight patients ( 16 . 3% ) , soft tissue mass in 10 ( 20 . 4% ) , periosteal reaction in one patient ( 2% ) , bone destruction in five patients ( 10 . 2% ) and in six patients ( 12 . 2% ) a combination of these findings were detected ( Fig . 4 ) . Ultrasound examination of the mycetoma lesion was performed in 11 patients ( 22 . 4% ) . This showed evidence of eumycetoma in five patients ( 10 . 2% ) , actinomycetoma in four patients ( 8 . 2% ) while in two patients ( 4 . 1% ) no diagnosis was established . Most of the patients had MRI examination and it showed the skin , subcutaneous , skull and intracranial disease spread with the typical dot-in-circle sign in most of them , ( Figs . 5 , 6 ) . FNA for cytology was performed in 21 patients to confirm the diagnosis and it showed evidence of Actinomadura madurae in eight patients ( 16 . 3% ) , M . mycetomatis in six patients ( 12 . 2% ) , Streptomyces somaliensis in four patients ( 8 . 1% ) and in three patients ( 6 . 1% ) no grains were not detected . In this series , 24 patients ( 49% ) had histopathological examinations of surgical biopsies . The diagnosis of M . mycetomatis was established in nine patients ( 18 . 4% ) , Streptomyces somaliensis in 11 patients ( 22 . 4% ) and Actinomadura madurae in three patients ( 6 . 1% ) . In one patient ( 2% ) no diagnosis was established due to grains absence and the diagnosis was established by FNA . For actinomycetoma a combination of antimicrobial agents was given and that included streptomycin sulphate and dapsone , or streptomycin and trimethoprim-sulfamethoxazole . More recently , trimethoprim-sulfamethoxazole 8/40 mg/kg/day in cycles for 5 weeks and amikacin 15 mg/kg/day in a divided dose every 12 hours for 3 weeks were administered . The two week interval of amikacin in the five-week cycle is used for renal and audiometric monitoring . For eumycetoma several antifungal agents combined with various surgical excisions were performed . The common antifungal agents used were ketoconazole and Itraconazole . All patients were offered follow up appointments but due to various reasons 14 patients ( 28 . 5% ) were subsequently lost for follow up , five patients ( 10 . 2% ) were completely cured , and 30 patients ( 61 . 2% ) had partial cure .
The incidence of mycetoma of the head and neck region is infrequent . Review of the medical literature revealed only few reports on mycetoma in this site [10 , 29–31] , and although Sudan is considered the mycetoma homeland , only few reports on head and neck mycetoma were reported . Lynch in 1964 , reported on 1860 mycetoma patients and of these only 18 patients ( 0 . 96% ) had head and neck mycetoma [15] . Mahgoub in 1977 reported an incidence of 3% of head and neck mycetoma [32] . In 1986 , Gumaa and her associates reported on 15 out 400 patients with mycetoma ( 3 . 75% ) involving the head and neck region . This communication is in line with the fact that , mycetoma at this region is a rarity . In agreement with the previously reported series , actinomycetoma was the prevalent type of mycetoma in our series and the explanation for this prevalence remains unclear [10 , 29] . It is possible that the actinomycetes are resilient and able to survive in the extra-paedal areas more than eumyceteces . Males were predominantly affected in our series and this is in accordance with previous reports from the Sudan [10 , 11 , 29] . Again the explanation for this is unclear; however there is suggestion that sex hormones play a role in this predominance [33] . The majority of the reported patients were young adults with a mean age of 27 . 9 ± 14 . 7 years and this is a typical age in mycetoma patients [4 , 10 , 34] . Students were affected most , and this may be explained by the fact that , young age groups of patients contract the disease more . The study showed that 44 . 8% of the affected patients were farmers and workers . This is an important finding as the nature of their work puts them in direct contact with the soil on a daily basis and it has been postulated that the soil harbours the causative organisms and these patients are constantly exposed to minor injuries which facilitate the traumatic subcutaneous inoculation of the organisms . The mean disease duration at presentation among the affected study population is quite long . This may be explained by the painless nature of the disease , the lack of health education , low socio-economic status of the affected patients and lack of medical and health facilities in the endemic regions . The clinical presentation of patients in this series was typical and in agreement with other reports [1 , 2 , 35] . It started gradually at the subcutaneous tissue and progressed to affect the deep structures . It was painless in the majority of patients and that may be an important contributory factor for the late presentation in most patients . The study showed that 73 . 5% of the patients had multiple surgical excisions and recurrence and most of them had surgery performed under local anaesthesia . It is well known that incomplete surgical excision performed under local anaesthesia is the major factor leading in recurrence . At presentation almost half of the patients had massive lesions which is caused by their late presentation and the fact that , most of them had actinomycetoma which is known to be aggressive and can invade the deep structures and bone at an early disease stage [10] . Different skull parts were affected in our series , however , the frontal and occipital parts were affected most . The explanation for this is unclear however these parts are more prone to direct trauma and hence local inoculation of the causative organisms . Rare sites were encountered and this included the eye . One patient presented with massive intracranial eumycetoma with minimal skin and subcutaneous involvement , again the explanation is unclear but deep inoculation of the infection may provide some explanation . In the past , the disease extends in the head and neck area was assessed clinically and radiologically by skull and cervical X-rays or by cerebral angiography which is invasive and with many complications . Currently , the use of the MRI and CT scans provided an accurate assessment with minimal complications . Mycetoma has characteristic MRI features which are diagnostic . The MRI can delineate the involvement of the skin , subcutaneous , muscles and bones accurately and can grade the disease and help in planning patients’ management [21] . The present series showed poor treatment outcome , only five patients were cured and this is in line with previous reports [28–30 , 36] . This low cure rate necessitates the need for more efficient and safe novel drugs for the treatment of mycetoma . The dropout rate ( 28 . 5% ) in our series is high . The reasons for the high dropout rate are multifactorial and to mention but a few , the patients’ dissatisfaction due to the high cost and the prolonged treatment duration which is commonly more than one year duration , the drug side effects and complications , the patients low socio-economic status , the lack of health education and difficulty to reach the MRC , particularly during rainy seasons . All these can contribute to the poor treatment outcome . In conclusion , mycetoma of the head and neck region is a serious medical and health problem , is associated with serious complications , low cure rate and high follow-up dropout rate . The route of infection , susceptibility and resistance in mycetoma remains poorly understood and this is compounded by the lack of preventive and control measures . Hence health education may be the only tool to reduce the disease morbidity and mortality . | Although head and neck mycetoma is a rare disease entity yet it is a dreadful disease for the patient , the family and the treating physician . It is potentially fatal and the most challenging to treat . The current study highlighted that , most patients were young adult males , from rural areas of the Sudan and of low socioeconomic status . The lack of medical and health facilities , financial constrains and compounded by poor health education in endemic areas meant that most of the studied patients presented late with advanced disease . The diagnosis of mycetoma in the studied population was confirmed by several imaging techniques; MRI , CT scan and radiography and tissue diagnosis by histopathology and cytology techniques . The treatment outcome was rather unsatisfactory . The cure rate was low , the dropout rate was high and the disease was associated with high morbidity . Structured and objective health education programmes in the endemic areas is important to encourage patients to seek medical advice early in the course of the disease particularly that there is no clear preventive or control measurement in mycetoma . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Head and Neck Mycetoma: The Mycetoma Research Centre Experience |
Accidents caused by Loxosceles spider may cause severe systemic reactions , including acute kidney injury ( AKI ) . There are few experimental studies assessing Loxosceles venom effects on kidney function in vivo . In order to test Loxosceles gaucho venom ( LV ) nephrotoxicity and to assess some of the possible mechanisms of renal injury , rats were studied up to 60 minutes after LV 0 . 24 mg/kg or saline IV injection ( control ) . LV caused a sharp and significant drop in glomerular filtration rate , renal blood flow and urinary output and increased renal vascular resistance , without changing blood pressure . Venom infusion increased significantly serum creatine kinase and aspartate aminotransferase . In the LV group renal histology analysis found acute epithelial tubular cells degenerative changes , presence of cell debris and detached epithelial cells in tubular lumen without glomerular or vascular changes . Immunohistochemistry disclosed renal deposition of myoglobin and hemoglobin . LV did not cause injury to a suspension of fresh proximal tubules isolated from rats . Loxosceles gaucho venom injection caused early AKI , which occurred without blood pressure variation . Changes in glomerular function occurred likely due to renal vasoconstriction and rhabdomyolysis . Direct nephrotoxicity could not be demonstrated in vitro . The development of a consistent model of Loxosceles venom-induced AKI and a better understanding of the mechanisms involved in the renal injury may allow more efficient ways to prevent or attenuate the systemic injury after Loxosceles bite .
Loxosceles spiders can be found worldwide in temperate and tropical regions but their distribution is heavily concentrated in the Western Hemisphere [1] , [2] . In fact , in South America , loxoscelism is considered the most important form of araneism due to its high incidence and morbidity [1]–[3] . In Brazil , Loxosceles spiders were responsible for approximately 7 , 000 cases of spider envenomation reported to the Brazilian Ministry of Health in 2006 [Unpublished data . SINAN-Animais Peçonhentos/SVS/MS . http://dtr2004 . saude . gov . br/sinanweb/tabnet/dh ? sinan/animaisp/bases/animaisbr . def] . Loxosceles venom is a complex mixture of several proteins including alkaline phosphatase , hyaluronidase , 5-ribonucleotidase phosphohydrolase , sphingomyelinase D , several proteases , esterase and ATPase . Sphingomyelinase D is considered the most toxic fraction of the venom , playing a key role in its local and systemic action [1]–[4] . It causes neutrophil migration , complement system activation , cytokine and chemokine release and platelet aggregation [5] . Loxosceles spiders are not aggressive and the bites usually occur when they are pressed against the body , mainly while the victim is sleeping or dressing . The accident may cause mild cutaneous inflammatory reaction or a local injury characterized by pain , edema and livedo , developing later to dermonecrosis with gravitational spreading [1]–[3] . In up to 13% of the cases [1] , loxoscelism can cause a systemic injury , known as viscerocutaneous loxoscelism ( VCL ) . This form occurs predominantly in children [6] , and patients may develop acute kidney injury ( AKI ) , which is considered the main cause of death after loxoscelism envenomation [5] , [7] . VCL is characterized by fever , malaise , weakness , nausea and vomiting , hemolysis , hematuria , jaundice , thrombocytopenia and disseminated intravascular coagulation . This severe multisystemic clinical picture can occur as early as 24 hours after the bite [1]–[3] , [7] . AKI has been described in VCL as single case reports [7]–[11] or as relatively small series of cases [12]–[14] . Data on AKI after VCL are not consistent , even in the same country . Several factors might account for this , including the spider species and the patient age . In Brazil , 49% of AKI , 45 . 7% of oliguria and 8 . 6% of anuria were found among 35 VCL cases [6] . On the other hand , among 359 cases treated in Butantan Institute , Brazil , 4% developed VCL and none presented AKI [15] . In Chile , plasma creatinine was assessed in 26 of 34 VCL cases and was elevated in all , with values ranging from 4 . 4 to 6 . 0 mg/dL [16] . In the USA , AKI was found in 1 of 6 children hospitalized due to VCL [17] . These differences in AKI frequency can be due to the distribution of different Loxosceles species through the North and South Americas [5] . In São Paulo the commonest specie is the Loxosceles gaucho but in other regions of Brazil the Loxosceles intermedia is the most prevalent . The mechanisms for Loxosceles venom-induced AKI are still elusive and renal injury has been attributed to hemolysis , rhabdomyolysis , shock and direct venom nephrotoxicity [1] , [2] , [5] , [11] , [14] , [17]–[19] . Few experimental studies have focused on the action of the Loxosceles venom in the kidney , and none have performed a detailed study on renal function and hemodynamics . The aim of the present study was to assess the nephrotoxicity of Loxosceles gaucho venom in rats and study some of the mechanisms possibly involved in the genesis of the renal injury .
Experiments were done according to the Brazilian law of protection of animals . The study was approved by the Animal Experimentation Ethics Committee ( CEEA ) , FAMERP . Specimens of L . gaucho were collected in São Paulo State . The spiders were kept in quarantine for one week without food before venom collection , and venoms were obtained as previously described [20] . A pool of venom collected from approximately 1 , 000 L . gaucho spiders was used . The protein content of venom pool was determined using bicinchoninic acid [21] . The left kidney was collected at the end of GFR measurements in 6 rats injected with LV and 6 rats injected with saline . Renal tissue was fixed in 4% buffered formalin and embedded in paraffin . Horizontal sections 3 to 4 µm thick were stained with periodic acid-Schiff's reagent ( PAS ) , hematoxylin-eosin , Masson trichrome and periodic acid methenamine silver . The tissue was evaluated by light microscopy by two observers masked to the treatment . Tubular , vascular and glomerular changes were evaluated according a 0 to 3 semiquantitative score . In the vessels and glomeruli , signs of endothelial injury ( fibrin thrombi in glomerular capillary lumina , disruption or reduplication of the glomerular capillary basement membrane , swollen endothelial cells and fibrinoid necrosis ) were extensively searched . Sections from representative formalin-fixed paraffin embedded samples were stained with monoclonal antibody against hemoglobin and myoglobin , with amplification by streptavidin-peroxidase method . Briefly , after deparaffinization in xylene and rehydration in graded ethanol , antigen epitope retrieval was performed using 10 mM citric acid solution , pH 6 . 0 in a pressure cooker . Endogenous peroxidase activity was blocked with 6% hydrogen peroxide for 20 min . Primary mouse Polyclonal Rabbit Anti-human Hemoglobin antibody ( code # A0118 , DakoCytomation , USA ) , diluted 1∶1000 , and Sheep Anti-human Myoglobin ( code # PH 213 , The Binding Site , USA ) , diluted 1∶50 , were incubated for 30 min at 37°C followed by overnight incubation at 4°C , and then by addition of biotinylated anti-mouse secondary antibody and streptavidin-horseradish peroxidase ( LSAB+ , code # k0690 , Dako , Carpinteria , CA , USA ) . Color of reaction product was developed by 3 , 3′-diaminobenzidine and H2O2 and counterstaining was performed with Harris hematoxylin . The primary antibody was omitted for negative controls and endothelial cells of tonsil were used as positive control . The immuno-expression of hemoglobin and myoglobin was evaluated in a semi-quantitative approach . Samples with no evidence of staining or those with evidence of only focally positive cells ( <1% ) were recorded as negative . Inulin was determined by chemical anthrone method . CK , AST , ALT and LDH were assessed by colorimetry ( Hitachi auto-analyzer model 917 , Japan ) . Hct was assessed by a microhematocrit method . Results are presented as mean ± standard error of mean ( SEM ) . Comparisons were done by two-tail unpaired Student's t-test or one-way ANOVA , as appropriate . P values<0 . 05 were considered significant .
Loxosceles gaucho venom caused an early and significant decrease in GFR and urinary volume . In the same way , venom caused a sharp and significant decrease in RBF and a significant RVR increase . These changes occurred without significant variation in BP . In the control group saline infusion did not change GFR , RBF and RVR , urinary volume or blood pressure ( see table1 ) . Loxosceles gaucho venom did not cause cellular injury to PT in both concentrations utilized . After 60 min LDH release was 21 . 2±0 . 4% in control , 22 . 0±1 . 0% in LV 3 . 17 µg/mL and 22 . 4±0 . 5% in LV 6 . 34 µg/mL . Loxosceles gaucho venom induced a statistically significant increase in serum CK and AST as compared to control group . LDH and ALT values also increased in the venom group , but the difference was not statistically significant when compared to saline-infused rats . Hematocrit values were similar in venom and control groups ( see table 1 ) . In the LV group , rats showed flattened epithelium ( score 1 . 3±0 . 2 ) , tubule dilatation ( score 1 . 8±0 . 3 ) , presence of cell debris in tubular lumen ( score 1 . 25±0 . 25 ) presence of detached epithelial cells in tubular lumen ( score 1 . 2±0 . 2 ) and acute epithelial degenerative changes ( score 1 . 6±0 . 3 ) . In the control group , animals showed normal renal histology . Endothelial injury was not found in any animal . All glomerular capillaries showed patent lumina and preserved endothelial cells . The control group showed negative myoglobin immunostaining in all rats . In the LV group , three animals presented focal myoglobin deposition ( 1+ ) , one animal disclosed focal and interstitial myoglobin staining ( 1+ ) , one animal showed interstitial myoglobin deposition ( 2+ ) and one animal did not present myoglobin at renal tissue . In the control group two animals presented focal hemoglobin staining ( 1+ ) and four animals were negative for hemoglobin . In the LV group , four animals showed focal hemoglobin staining ( 1+ ) , one animal showed ( 3+ ) of hemoglobin deposition , and one animal did not present hemoglobin at renal tissue . These data are represented at Figure 1 .
An important and novel result from this study is the finding that Loxosceles gaucho venom may produce renal damage and rhabdomyolysis independently from the dermonecrotic injury or to blood pressure changes . The factors most likely contributing for the observed renal injury were renal vasoconstriction and myoglobinuria . In a consistent way with the available clinical results [14] , [16] , [24] , [25] and to data originated from experimental studies in mice [18] , [19] , [26] , renal histology disclosed acute tubular necrosis . There was an early and intense RBF decrease and RVR increase in venom-infused animals . Loxosceles venom may cause intravascular clotting and consequently tissue ischemia [27] , [28] , but this phenomenon was not observed in the present study . Other mechanisms possibly related to LV-induced decrease in RBF are the venom toxicity to endothelial cells [29] , its property to degrade extracellular matrix molecules acting against basement membrane structures [30] and its vasoconstrictive activity [5] . Contrasting with the findings of an experimental model that utilized Loxosceles intermedia venom [18] , no signs of endothelial injury were found in the present study , in which Loxosceles gaucho venom was used . These differences might explain the variation in lethality of the venom of diverse Loxosceles species . In fact , Loxosceles intermedia venom is more lethal than Loxosceles gaucho venom [31] . Rhabdomyolysis has been sporadically related after human loxoscelism [7] , [14] , [17] . Although only low myotoxicity activity was reported in experimental studies with LV [31] , [32] , the venom clearly caused rhabdomyolysis in the current experiment , as evidenced by significant CK increase and deposition of myoglobin in renal tissue . It is noteworthy that rhabdomyolysis was caused by a systemic venom effect , totally independent from local injury , which actually did not exist in the present model . Rhabdomyolysis is a well known cause of AKI , causing direct injury to tubule cells and inducing or enhancing vasoconstriction [33] . Loxosceles envenomation has been associated to hemolysis in humans [5] , [7] , [34] . Although there was no hematocrit decrease in the venom-infused rats , renal tissue stained positively for hemoglobin after venom infusion and LDH was three times higher in the venom as compared to the control group . Even considering that this difference was not statistically different , it is possible that some degree of hemolysis had occurred , contributing for the renal injury genesis . The early development of renal dysfunction , the low molecular weight and the cationic charge of the venom components , facilitating its renal excretion , suggest that LV might have a potential direct toxic effect on tubule cells . In fact , Luciano et al . [18] showed L . intermedia venom binding at glomerular and tubule cells basement membranes of mice with venom-induced AKI . Considering that this venom is capable to damage the basement membrane , a direct nephrotoxicity effect may have contributed to AKI development in these mice . Chaim et al . [19] demonstrated the deposit and binding of the dermonecrotic fraction of L . intermedia venom to renal intrinsic structures of mice with LV-induced renal injury . When the venom was added to a MDCK cell culture , it deposited in the cells surface and caused cells structural changes , impaired spreading and cells adhesion and altered cell viability . The current results are discordant with this previous paper , since Loxosceles venom did not cause direct toxicity in proximal tubules suspension . However , there are some possible explanations for this difference . While Chaim et al . [19] , used cultured MDCK cells , we used fresh prepared proximal tubules suspension from rats . The spider species utilized were different , L . gaucho in the present experiment versus L . intermedia in the other cited studies [18] , [19] . Finally , it is possible that the venom needs plasma components , such as complement , not present in our experimental preparation , in order to cause proximal tubule cell toxicity . There is just another study assessing the effects of Loxosceles venom in vivo , which was performed in mice [18] . The authors didńt measure glomerular filtration rate , systemic and renal hemodynamic were not assessed , neither muscle enzymes measured . They found blood urea elevation four hours after intraperitoneal injection of crude L . intermedia venom . There was no hemolysis and renal histology analysis disclosed glomerular collapse and loss of vascular integrity and tubule cell injury . In a subsequent study , the same group demonstrated that the intraperitoneal injection of the dermonecrotic fraction of the venom in mice caused nephrotoxicity similar to that seen with the crude venom [19] . In summary , intravenous injection of Loxosceles gaucho venom induced a striking acute kidney injury , without dermonecrotic lesion or variations in systemic blood pressure . The observed renal changes were associated to impaired renal blood flow and systemic rhabdomyolysis . Although a direct venom effect on isolated proximal tubules was not demonstrated , venom direct nephrotoxicity cannot be totally ruled out . | Loxosceles ( recluse or brown spider ) is the most important spider genus causing human envenomation . In Brazil Loxosceles spiders were responsible for approximately 7 , 000 cases of spider envenomation per year . The brown spider accidents may cause late cutaneous necrosis at the bite site , intravascular hemolysis , rhabdomyolysis , coagulation system changes and acute kidney injury ( AKI ) . Even patients with mild cutaneous lesion may develop severe hemolysis and AKI , which is the main cause of death after these accidents . The mechanisms causing kidney injury are poorly understood . In this manuscript we described a consistent rodent model of Loxosceles gaucho venom-induced AKI and studied some of the possible mechanisms of the renal lesion . The results of this research showed that kidney injury may occur independently of the cutaneous lesion and without changes in the systemic blood pressure . Kidney dysfunction occurred likely due to intra-renal vasoconstriction and rhabdomyolysis , although a direct toxic effect of the venom on the proximal tubules cannot be ruled out . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"nephrology",
"neglected",
"tropical",
"diseases",
"acute",
"renal",
"failure",
"critical",
"care",
"and",
"emergency",
"medicine",
"toxicology"
] | 2011 | Loxosceles gaucho Venom-Induced Acute Kidney Injury – In Vivo and In Vitro Studies |
Like other tropical African countries , Gabon is afflicted by many parasitic diseases , including filariases such as loiasis and mansonellosis . This study aimed to assess the prevalence of these two filarial diseases in febrile and afebrile children using quantitative real-time PCR and standard PCR assays coupled with sequencing . DNA from blood specimens of 1 , 418 Gabonese children ( 1 , 258 febrile and 160 afebrile ) were analyzed . Overall , filarial DNA was detected in 95 ( 6 . 7% ) children , including 67 positive for M . perstans ( 4 . 7% ) , which was the most common . M . perstans was detected in 61/1 , 258 febrile children ( 4 . 8% ) and 6/160 afebrile children ( 3 . 8% , P = 0 . 6 ) . Its prevalence increased statistically with age: 3 . 5% , 7 . 7% and 10 . 6% in children aged ≤5 , 6–10 and 11–15 years , respectively . M . perstans prevalence was significantly higher in Koulamoutou and Lastourville ( 12% and 10 . 5% , respectively ) than in Franceville and Fougamou ( 2 . 6% and 2 . 4% , respectively ) . Loa loa was detected in seven febrile children including one co-infection with M . perstans . Finally , 21 filarial DNA positive were negative for M . perstans and Loa loa , but ITS sequencing could be performed for 12 and allowed the identification of a potential new species of Mansonella provisionally called “DEUX” . Mansonella sp . “DEUX” was detected only in febrile children . Further study should be performed to characterize Mansonella sp . “DEUX” and evaluate the clinical significance of mansonellosis in humans .
Tropical African countries are afflicted by many febrile diseases including HIV , tuberculosis , malaria , and bacteremia; it has been suggested that their emergence can be influenced by infectious factors such as filarial parasites , including Mansonella perstans [1 , 2] . In 2012 , Dolo et al . , during assessment of the effects of filariasis on anemia and pro-inflammatory responses associated with clinical malaria in Mali , reported that the geometric mean of hemoglobin levels was significantly lower in 31 malaria patients without filariasis than in 31 with filariasis [3] . They added that these filariases , including M . perstans , significantly decrease plasma levels of Interleukin-1ra , Inducible Protein-10 and Interleukin-8 at the time of presentation with clinical malaria [3] . Brown et al . reported in 2004 that M . perstans did not have an adverse effect on HIV infection , as a higher load of CD4 was detected in HIV patients co-infected with M . perstans compared to those without this parasite [4] . In 2006 , Brown et al . also added that the progression to active tuberculosis among HIV-1 infected Ugandans was not associated with M . perstans [5] . Until now , no distinct and specific clinical features have been unequivocal associated with M . perstans infections , unlike some other human filariasis [1] . For this reason , the potential ability of M . perstans to induce pathology has been largely neglected . However , a significant spectrum of clinical features related to this parasite has been described [6 , 7 , 8 , 9] . Some of these manifestations have been reported from endemic areas in Uganda but without statistical links to M . perstans infection [10] . Four reasons were proposed to explain the lack of linkage: ( a ) it is possible that some of the earlier reported manifestations are rare and therefore were not present with high enough frequency to show a significant association , ( b ) the high prevalence of microfilaremia reduced the ability to detect a significant difference in frequency of manifestations between the microfilaremia positive and microfilaremia negative groups , ( c ) previously reported manifestations may be more common in expatriates exposed to infection later in life than in individuals who have grown up in the endemic environment and ( d ) manifestations may be related more to adult worm infections or to exposure to infectious larvae than to microfilaremia [10] . M . perstans , whose vectors are tiny blood-sucking flies called midges belonging to Culicoides genus , infects approximately 114 million people in Africa , mostly located in 33 Sub-Saharan African countries [1 , 2 , 11] . Moreover , M . perstans currently remains unaffected by the most common anti-filarial drugs [1 , 2] . In Gabon , a central African country , most information on M . perstans has been retrieved when studying other filariases . Its prevalence was recently reported to be 10 . 2% , including a 3 . 2% co-infection with Loa loa in 270 villages of Gabon [12] . M . perstans is widespread in Sub-Saharan Africa but no study has reported its prevalence among febrile children in Gabon . This study aimed mainly to assess the prevalence of mansonellosis and Loa loa in febrile and afebrile children from Gabon using molecular methods .
After approval from the Central Ethics Committee of Gabon ( N°00370/MSP/CABMD and N°0023/2013/SG/CNE ) , 1 , 258 febrile and 160 afebrile children were recruited from 2011 to 2014 . Samples from the Republics of Côte d’Ivoire and Senegal were obtained under Ethics numbers N°86/MSLS/CNERN-dkn and N°00 . 87 MSP/DS/CNERS , respectively . Written Informed Consent from parents or legal guardians of each child was systematically received before inclusion in the study . This study took place in four areas of Gabon located in three provinces of the country: Franceville ( Haut-Ogooue province ) , Koulamoutou and Lastourville ( Ogooue Lolo province ) , and Fougamou ( Ngounie province ) . Among children , 997 ( 873 febrile and 124 afebrile ) were included in Franceville , 171 in Lastourville ( 155 febrile and 16 afebrile ) , 83 in Fougamou ( 63 febrile and 20 afebrile ) , and 167 febrile children in Koulamoutou . All the children were recruited from pediatric outpatient wards of Reginal hospital centers ( Franceville and Koulamoutou ) , Medical Center ( Lastourville ) , and Medical Research Unit ( Fougamou ) . Finally , seven blood samples from two other areas of West Africa that had previously been found positive for M . perstans were included in the analysis in order to compare sequences and construct phylogenetic trees . Two samples were from the Republic of Côte d’Ivoire and five samples were from Senegal . For each child , 100μl of DNA were extracted from 200μl of blood samples ( collected in EDTA tubes ) using a DNA Blood Kit E . Z . N . A ( Omega Bio-Tek , Norcross , U . S . A ) according the manufacturer’s guide . The DNA was extracted in Franceville , stored at -20°C and sent in ice packs to URMITE , Marseille , France for molecular analysis . Epi Info software ( Centers for Disease Control and Prevention , Atlanta , GA , USA ) was used to perform data analysis: Mantel-Haenszel χ2 and Fisher exact tests . Statistical significance was considered for a two tailed P value lower than 0 . 05 .
Overall , 95 out of 1 , 418 samples ( 6 . 7% ) were positive based on ITS qPCR . Among them , 67 ( 4 . 7% ) were positive for M . perstans and 7 for Loa loa ( including 1 co-infection with M . perstans ) , whereas 21 were negative using these two qPCR tests . Identification of M . perstans and Loa loa was confirmed with sequencing and the BLAST search tool . All children with Loa loa were febrile; two were from Franceville , two from Koulamoutou , one from Lastourville , and two from Fougamou . M . perstans is the most prevalent filarial parasite detected in 67 children , including 61 febrile ( 4 . 8% , 61/1 , 258 ) and 6 afebrile children ( 3 . 8% , 6/160; P = 0 . 6 ) . Overall , M . perstans was detected in 3 . 5% of children ≤ 5 years of age ( 36/1 , 036 ) , 7 . 7% of six to ten year-olds ( 21/272 ) , and 10 . 6% of 11 to 15 year-olds ( 7/66 ) , ( Fig 2 ) . The prevalence was statistically significantly lower in children ≤ 5 years of age than in those between six and ten year-olds ( P = 0 . 004 ) and in those between 11 and 15 year-olds ( P = 0 . 01 ) . No statistical difference of prevalence was observed in children between ages six and ten and those between 11 and 15 year-olds ( P = 0 . 4 ) . Furthermore , no difference in M . perstans prevalence was observed between males ( 4 . 3% , 30/703 ) and females ( 4 . 9% , 33/673 P = 0 . 6 ) . The prevalence of M . perstans was lower in the dry season ( 3 . 9% , 34/864 ) than in the rainy season ( 6% , 33/554 ) , but without statistical significance ( P = 0 . 09 ) . The lowest prevalence of M . perstans was observed in Haut-Ogooue and Ngounie areas , Franceville ( 2 . 6% , 26/997 ) , and Fougamou ( 2 . 4% , 2/83 ) , respectively . The prevalence was higher in the 2 sites of Ogooue Lolo province: Koulamoutou ( 12% , 20/107 ) and Lastourville ( 10 . 5% , 18/171 ) , ( Fig 3 ) . Besides , these differences of prevalence of M . perstans were statistically significant . The ITS1 and 5S sequences that were obtained were of high quality . Sequences from all Gabonese samples were identical for both of the two sequences . However , while the ITS1 sequences of M . perstans from Republic of Côte d’Ivoire and Senegal were completely identical to each other , they differed from all sequences from Gabon by only one base pair ( position 383 from the beginning of the amplicon ) . Comparison of the 5S sequences from the different analyzed areas permitted identification of five genetic variants of M . perstans: three from Senegal , one from Côte d’Ivoire and one from Gabon ( Fig 4A ) . Among the 21 positive samples with ITS qPCR , but negative with M . perstans and Loa loa qPCR assays , sequencing of ITS1 was carried out for 12 samples; not enough DNA was available for 9 samples from Franceville . The sequences were almost identical to each other ( two samples had the same single nucleotide polymorphism ) and differed significantly from sequences of M . perstans: nine nucleotide polymorphisms and five deletions/insertions for the sequenced portion ( 94% of identity ) . The BLAST search did not yield any identification . Unfortunately , DNA sequences of Mansonella streptocerca , another possible human pathogen [15] , were unavailable in the Genbank . The only available gene sequence ( 5S ribosomal RNA ) of M . streptocerca was printed in the manuscript of Fischer et al . [15] . So , in order to identify if the Mansonella from 12 samples was M . streptocerca , we sequenced the 5S rRNA of these samples . The comparison of both sequences ( the one printed in the manuscript and the one obtained from our samples ) showed a difference ( Fig 4B ) . This Mansonella sp . ( provisionally called “DEUX” ) was detected only in febrile patients . The 12 patients were from Koulamoutou ( four patients ) , Lastourville ( four patients ) , and Fougamou ( four patients ) . All nucleotide sequences obtained during this study were stored in the Genbank under the following numbers: KR080185-KR080190 for the ITS1 sequences from the two genetic variants of Mansonella sp . “DEUX” from Gabon , from M . perstans from Gabon , Senegal , and Republic of Côte d’Ivoire and for Loa loa from Gabon , respectively; KR080177-KR080184 for the 5S sequences from the two genetic variants of Mansonella sp . “DEUX” from Gabon , for Loa loa from Gabon , and for M . perstans from Côte d’Ivoire , Gabon and three genetic variants from Senegal , respectively .
Febrile illnesses are common in Sub-Saharan Africa while filarial parasites , including M . perstans , are known to influence their emergence . However , no study evaluated the prevalence of filarial parasites , especially M . perstans , among febrile children in Gabon . The data reported here were validated following rigorous criteria . Each PCR assay included a positive control ( DNA of targeted filarial parasite ) and a negative control ( PCR mix alone ) . All positive samples were sequenced to confirm our results and the sequences obtained were carefully verified . This study confirmed that M . perstans and Loa loa are widespread in Gabon [12 , 18] . However , no significant difference was observed between the prevalence of M . perstans in febrile ( 4 . 8% ) and afebrile ( 3 . 8% ) children . Similar lack of difference has also been recently reported in Senegal where the prevalence of M . perstans was 14 . 4% ( 29/201 ) among febrile and 15% ( 14/96 ) in afebrile children [11] . The lack of difference in the prevalence of M . perstans between febrile and afebrile subjects seems to support the previously reported observation that in general M . perstans is not a major cause of febrile illness [12] . Mansonelloses are most often considered to present few symptoms or even asymptomatic . Only eosinophilia , pruritus , and ocular involvement are typically reported as caused by M . perstans . However , severe complications have been observed in M . perstans infections , such as in the case of a missionary family returning from an African area endemic to M . perstans in which multiorgan failure was reported [9] . Ndibazza et al . in 2013 reported that parasites including , M . perstans infections were associated with an increase of the rate of clinical malaria unlike to other types of infection such as Schistosoma mansoni , which had no consistent association with childhood malaria [19] . Several other clinical features have been also reported and linked to M . perstans in studies , including hormonal disturbances , subcutaneous swellings , skin rashes , acute swelling in the forearms , itching , pain or ache organs , extreme exhaustion , fever , neurological , and psychological symptoms [6 , 7 , 8 , 9 , 20] . The diversified reports about the pathogenicity of M . perstans suggest that some factors such as the degree of microfilaremia , coinfection , sex , age , and the geographical origin of the person should greatly influence its pathogenicity [9 , 21 , 22] . In addition , the pathogenicity of M . perstans could be related to the age of first exposure . That could explain the severity of this infection among expatriates whereas few symptoms have been reported in native populations where asymptomatic carriers have also been observed for other parasites , including Plasmodium falciparum [13] . Overall , very few studies have screened rural endemic populations for symptoms [1] . M . perstans remains unresponsive to most antifilarial drugs [1] . Antibiotics for Wolbachia , its endosymbiont , had been reported as a strategy to combat this parasite [23] . The prevalence of M . perstans increased significantly with age . This finding supports those previously reported in Africa [2 , 11 , 10] . In fact , the higher prevalence in older children and adults should be related to their common exposure to the Culicoides bites during their activities [24] . For example , fishermen , farmers , and cattle breeders were the most affected occupational categories in a study on filariasis in Bauchi State , Nigeria [25] . Besides , a significant frequency of M . perstans was observed according to the studied areas . Indeed , the prevalence was higher in the Ogooue Lolo province ( Koulamoutou and Lastourville ) than in Ngounie ( Fougamou ) and Haut Ogooue ( Franceville ) . At Franceville , the vegetation consists mainly of savanna , while Koulamoutou and Lastourville are covered of tropical rainforest , and Fougamou is a grassland forest area . These finding correlate with those recently published by Akue et al . in 2011 [12] . Among the three major Gabonese ecosystems , the forest had a higher prevalence of parasites than the savannah and wetlands [12] . Studies summarized by Simonsen et al . supports this observation [1] . They reported that the high prevalence of M . pertans occurred in many areas where tropical forests alternate with large swamps and open ground [1] . They added that M . perstans was common in the rain forest , less common in the forest peripheries , higher in or near densely forested areas and low in the mountain grassland zones of the British Cameroons , and related to the species and biting density of the vectors [1] . Variation among other filarial parasites such as loasis , according to contrasting bioecological zones , was observed in central Africa [26] . Some vector species prefer to breed in decomposing plant material; extensive cultivation of bananas has been identified as a risk factor for transmission [1] , this should explain why farmers are one of the most infected occupational categories[25] . Studies in other areas have shown that low-pH soil , low organic soil content , salty soil , and wet soil contributed to Culicoides fly breeding [27 , 28] , while temperature may affect vector competence [29] . A novel potential species of Mansonella was detected on the basis of molecular analyses . Interestingly , a Mansonella species that was morphologically different from M . ozzardi was recently identified in blood samples from Peru [30] , but no genetic difference was found . In this case , unfortunately , we failed to obtain blood smears , thus , no morphological analyses and comparisons of Mansonella sp . “DEUX” were possible . It is important to emphasize that Mansonella sp . “DEUX” was only detected in febrile children . We strongly suspect that Mansonella sp . “DEUX” may be a new species , because based on ITS1 comparison , M . perstans is very homogenous throughout Africa ( from Senegal to Gabon ) and Mansonella sp . “DEUX” differs significantly from M . perstans with this spacer ( 94% ) . Based on comparison by 5S rRNA sequences , Mansonella sp . “DEUX” is different from M . streptocerca [14 , 15] . Other possible variants may include Mansonella rodhainii , Mansonella gorillae , Mansonella vanhoofi , Mansonella leopoldi , and Mansonella lopeensis , reported from humans and great apes in Africa [31] . Unfortunately , genes from these species are unavailable in the Genbank . The slides containing microfilariae should enable us to describe morphological features of Mansonella sp . “DEUX” and to compare them with the aforementioned Mansonella spp . Until now , no other valid species belonging to the genus Mansonella were reported in Africa [31] . Thus , Mansonella sp . “DEUX” may represent a potential new species of Mansonella with a possible pathogenic role in humans . An additional morphologic study is necessary in order to identify whether Mansonella sp . “DEUX” represents one of the Mansonella species not yet molecularly characterized , a genetic variant of M . perstans or really a new species . Gabon remains a significant target of filariasis including Loa loa , M . perstans , and a potential new species of Mansonella , Mansonella sp . “DEUX” . M . perstans was commonly observed in both febrile and afebrile children whereas Loa loa and Mansonella sp . “DEUX” were observed only in febrile children . Even if M . perstans does not appear to be directly linked to febrile episodes among the local population , its real clinical impact has not yet been determined . Moreover , further study should be performed to characterize Mansonella sp . “DEUX” morphologically and evaluate its clinical significance in humans . | Approximately 114 million people in Africa , mostly located in 33 Sub-Saharan African countries , are infected with Mansonella perstans , a filarial nematode . The ability of M . perstans to induce severe clinical features has only recently been considered . Unfortunately , no study has evaluated its burden in febrile patients in Gabon , a tropical African country where febrile and parasitic illnesses are common . There , we developed molecular tools to detect M . perstans and other Mansonella spp . , as well as Loa loa , another filarial nematode in blood specimens of febrile and afebrile Gabonese children . Our findings suggest that there is no direct link between M . perstans and fever among the local population ( 61/1 , 258 febrile children [4 . 8%] versus 6/160 afebrile children [3 . 8%] ) , whereas Loa loa and another potential new species Mansonella sp . “DEUX” were only found in febrile patients ( seven and twelve , respectively ) . Further study should be performed to characterize Mansonella sp . DEUX and evaluate the clinical significance of mansonellosis in humans . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Mansonella, including a Potential New Species, as Common Parasites in Children in Gabon |
Drosophila telomere maintenance depends on the transposition of the specialized retrotransposons HeT-A , TART , and TAHRE . Controlling the activation and silencing of these elements is crucial for a precise telomere function without compromising genomic integrity . Here we describe two chromosomal proteins , JIL-1 and Z4 ( also known as Putzig ) , which are necessary for establishing a fine-tuned regulation of the transcription of the major component of Drosophila telomeres , the HeT-A retrotransposon , thus guaranteeing genome stability . We found that mutant alleles of JIL-1 have decreased HeT-A transcription , putting forward this kinase as the first positive regulator of telomere transcription in Drosophila described to date . We describe how the decrease in HeT-A transcription in JIL-1 alleles correlates with an increase in silencing chromatin marks such as H3K9me3 and HP1a at the HeT-A promoter . Moreover , we have detected that Z4 mutant alleles show moderate telomere instability , suggesting an important role of the JIL-1-Z4 complex in establishing and maintaining an appropriate chromatin environment at Drosophila telomeres . Interestingly , we have detected a biochemical interaction between Z4 and the HeT-A Gag protein , which could explain how the Z4-JIL-1 complex is targeted to the telomeres . Accordingly , we demonstrate that a phenotype of telomere instability similar to that observed for Z4 mutant alleles is found when the gene that encodes the HeT-A Gag protein is knocked down . We propose a model to explain the observed transcriptional and stability changes in relation to other heterochromatin components characteristic of Drosophila telomeres , such as HP1a .
Telomere elongation is needed in all eukaryotes with linear chromosomes due to the incapacity of cellular polymerases to proceed in 3′ to 5′ direction . Telomere length homeostasis is important for protecting the chromosomes from terminal erosion and the loss of important genetic information . Moreover , a defined telomere length is required for the proper assembly of the telomere-capping complex ( shelterin in telomerase telomeres or terminin in Drosophila ) [1]–[3] . When telomeres recess excessively , the disassembly of the protective cap leaves the telomere ends unprotected . Consequently , the telomeres are recognized by the DNA damage machinery , and upon repair are fused together resulting in genomic instability [4] . Eukaryote telomeres are dynamic structures that make up their telomere length from a balanced mechanism of gains and losses . The net result of this process is a telomere of the appropriate length to exert the different telomeric functions , as well as for protecting the genetic content [5] , [6] . Several proteins have been described with both positive and negative effects on telomere length regulation [1] . Some of these cellular components act regulating the different telomerase subunits either by directly activating their expression , or their biochemical function [7] . On the other hand , changes on the telomeric chromatin have also been related to changes in telomere length in several organisms pointing to an epigenetic component in telomere regulation in eukaryotes [8] . Thus , telomere length homeostasis is a complex cellular process that integrates signals from different regulatory mechanisms . Drosophila is the telomerase exception better studied so far , having acquired a retrotransposition based mechanism whose prevalence along all the genus ( 120 MY ) demonstrates its robustness [6] , [9] , [10] . The success of this mechanism is based in the targeted transposition of three different specialized non-LTR retrotransposons , HeT-A , TART and TAHRE [11]–[14] . Retrotransposons belong to Class I transposable elements ( TE ) , and their mechanism of transposition involves an RNA intermediate implying that each new successful transposition will increase the copy number of the element . This , in the case of the telomeric transposons will translate in increased telomere length directly benefiting the host and indirectly incrementing the absolute copy number of the telomeric transposons , ensuring their survival . Recent molecular studies demonstrate that telomeres in most eukaryotes are composed of two domains; the protective cap that lies at the very end and the distal ( telomeric ) domain . Flanking the telomere domain lays the proximal ( subtelomeric ) domain , which shows different chromatin characteristics [8] , [15] , [16] . The telomeric domain is composed of the telomerase repeats in telomerase organisms and of the retrotransposon array HeT-A , TART and TAHRE ( HTT ) in the case of Drosophila . Andreyeva and collaborators took advantage of the Drosophila melanogaster Telomere elongation ( Tel ) mutant strain , which has telomeres ten times longer than the wild type in order to obtain a better resolution of the chromatin in the different telomeric domains [17] . Their study demonstrates that the HTT array shows mixed characteristics of euchromatin and heterochromatin , and that two chromosomal proteins , JIL-1 and Z4 , specifically localize in this domain [15] . Z4 , also known as Putzig , is a seven-zinc finger protein known to localize at polytene chromosome interbands and necessary to maintain the band-interband structure in these special chromosomes [18] . Moreover , Z4 has been shown to be an important cofactor in at least three different pathways related with chromatin remodeling: the NURF and the TRF2/DREF remodeling complexes , where it acts as an activator [19] , [20]; and in the JAK/STAT pathway , where Z4 acts as a co-repressor [21] . With the exception of its role as a co-repressor in the JAK/STAT pathway , where Z4 binds to the Ken protein , Z4 exerts its effects mediating chromatin changes . In Drosophila , JIL-1 is the chromosomal kinase in charge of the phosphorylation of Serine 10 of histone H3 during interphase [22] . Like Z4 , JIL-1 also localizes at interbands in polytene chromosomes , and has a role in maintaining the band-interband structure of polytene chromosomes [23] . JIL-1 seems to have two interdependent functions; on one hand , JIL-1 is required for the structure of the chromosomes , and on the other , is involved in reinforcing active transcription of certain genes during interphase , as well as participating in the dosage compensation of genes in the male X chromosome [24]–[26] . Several JIL-1 interacting partners have been identified; JIL-1 interacts with Lamin Dm0 , histone H3 , and the chromosomal protein Chriz , also known as Chromator , among others [27]–[30] . A possible interaction between JIL-1 and Z4 has also been suggested [28] . Telomere function in Drosophila has been suggested to be epigenetically determined since the terminin complex that protects the telomeres assembles there in a sequence independent manner [2] . Maintenance of chromatin domains is a dynamic process in which chromatin marks and proteins interchange in a complex network of interactions . Each network , defines a set of characteristics that favor expression or repression of the genes embedded in such domains . Because telomere function in Drosophila strongly depends on transcription from the HTT array , where the genes involved in telomere elongation reside , the regulation of the chromatin structure of this domain is especially relevant to understand the regulation of telomere length in this organism . With the objective of better understanding this regulation , we have studied the role of JIL-1 and Z4 , two chromosomal proteins shown to localize at the HTT array [15] , on the regulation of the telomeric chromatin and their influence upon the expression of the main telomeric component in D . melanogaster , the HeT-A retroelement [31] . We have found that JIL-1 regulates HeT-A transcription , being thus the first described positive regulator of telomere expression in Drosophila . Moreover , we demonstrate that different Z4 mutant alleles show telomeric fusions in metaphase chromosomes from larval neuroblasts . ChIP experiments of Z4 and JIL-1 mutant alleles highlight changes of other components of the telomeric chromatin like the HP1a protein that can explain the transcriptional and stability effects observed in JIL-1 and Z4 mutant alleles . Importantly , we have also obtained proofs of a possible telomere targeting mechanism to recruit the JIL-1-Z4 complex to the telomeres .
We analyzed the levels of mRNA of the HeT-A element in different JIL-1 and Z4 mutant alleles and , in order to contextualize our results , we compared them with the levels obtained from the mutant allele of one of the genes already known to influence HeT-A expression , the Su ( var ) 2-5 gene which encodes for HP1a ( Figure 1A and 1B ) [32] , [33] . Different mutations in the Su ( var ) 2-5 gene , result in a pronounced increase in HeT-A transcription and severe problems of telomere stability [32] , [33] . Because the number of HeT-A copies may vary among stocks , we determined the number of copies of the HeT-A element for each of the analyzed stocks ( Figure S1A and S1B ) . We used this data to normalize the level of HeT-A transcription per number of copies and understand if JIL-1 and Z4 mutants show a differential HeT-A transcription activity ( Figure S2A and S2B , Figure 1A and 1B ) . Because in any given stock full length and truncated HeT-A elements coexists at the telomeres [31] , [34] , we performed the quantitative Real-Time experiments with two different sets of primers separated more than 3 kb along the full length HeT-A transcript ( see materials and methods for primer details ) . To determine the level of HeT-A transcription we used whole third instar larvae to extract total mRNA . Some of the larval tissues in this stage , like the brain and imaginal discs , are in demand for active cell division and have been reported to show active HeT-A expression [35] . We analyzed a JIL-1 hypomorph allele , JIL-1z60 that contains a molecular lesion , which results in low levels of functional protein [22] , and also a null allele JIL-1z2 [25] . In addition , we analyzed the JIL-1Su ( var ) 3-1 allele , which has been suggested to be a gain of function allele [36] . Homozygous JIL-1z60 animals have only a 17% of eclosion rate [22] , and JIL-1z2/JIL-1z2 are homozygous lethal [25] . We obtained a significant reduction of HeT-A transcription with both sets of primers ( 3′UTR and gag gene ) compared to the control strain ( w1118 ) for all the alleles with the exception of the JIL-1Su ( var ) 3-1 allele ( Figure 1A and 1B , left section of the graphics ) . In this last case , we did not observe a decrease in HeT-A transcription probably due to the ectopic phosphorylation activity of JIL-1 in this allele [30] . The JIL-1 alleles show telomere lengths comparable to wild type ( Figure S1A and S1B ) . The three JIL-1 alleles tested are in different genetic backgrounds , although for the hypomorph and the null mutations we have obtained very similar results , we crossed all the alleles with the w1118 strain in order to minimize the contribution of the genetic background in these measures . Similarly , we analyzed three different Z4 mutant alleles , Z47 . 1 , Z42 . 1 and pzg66 . The Z47 . 1 allele is a hypomorph allele that lacks the promoter region of the Z4 gene and is lethal at the pupal stage [18] . The Z42 . 1 and the pzg66 alleles are null alleles that result in embryonic and early larval lethality [18] , [21] . We obtained a substantial increase in HeT-A transcription in the case of the hypomorph allele Z47 . 1 ( Figure 1A and 1B ) . The results are similar for both sets of primers used indicating that the increase affects most HeT-A copies . The increased HeT-A transcription in the Z47 . 1 allele is consistent with a major level of HeT-A transcripts in all the allelic combinations where this allele is present ( Figure 1A and 1B ) . In addition , the number of copies of the HeT-A element in this allele was substantially increased indicating longer telomeres in this stock ( Figure S1A and S1B ) . Note that the level of HeT-A transcription in the Z47 . 1/Z47 . 1 homozygous is close to the HeT-A transcription in the Su ( var ) 2-505 mutation of HP1a . The Z42 . 1 and pzg66 alleles did not show a different level of HeT-A transcription compared to our control strain ( w1118 ) for neither the 3′UTR region nor the gag gene . As expected the number of HeT-A copies in these null Z4 alleles is not significantly different from the control strain ( w1118 ) . Only when the Z47 . 1 allele is combined with the Z42 . 1 or the pzg66 alleles the levels of HeT-A transcription increase above the ones in the w1118 strain . The levels of HeT-A expression concerning the HeT-A gag gene in the Z47 . 1/Z42 . 1 and the Z47 . 1/pzg66 genotypes are significantly different from the ones obtained by the Z47 . 1/Z47 . 1 homozygous combination . For the 3′UTR region , the difference in expression in only significant from the one obtained for the Z47 . 1/Z47 . 1 genotype for the Z47 . 1/Z42 . 1 allelic combination . We also investigated if we could observe a genetic interaction between JIL-1 and Z4 by measuring the levels of HeT-A transcription of a total of seven allelic combinations between JIL-1 and Z4 ( Figure 1A and 1B , Figure S2A and S2B ) . Although for the Z42 . 1 and pzg66 alleles we had not detected levels of HeT-A transcription significantly above the ones of w1118 , we found that the combinations JIL-1z60/Z47 . 1 and JIL-1z60/Z42 . 1 for the gag gene , and the JIL-1z60/pzg66 for both the 3′UTR region and the gag gene recover HeT-A transcription to w1118 levels ( Figure 1A and 1B ) . Accordingly with the results obtained for the single mutation , in the combination JIL-1Su ( var ) 3-1/Z47 . 1 we obtained levels of HeT-A gag transcription above the ones in w1118 . Because both JIL-1 and Z4 are proteins related with chromosome structure , we studied if the changes observed in the expression of the HeT-A retrotransposon were related to changes in the chromatin environment at the promoter of this retroelement . Therefore , we investigated by Chromatin immunoprecipitation ( ChIP ) experiments , changes in different chromatin marks and changes in the levels of the proteins of study , JIL-1 and Z4 ( Figure 2 ) , as well as of HP1a , a protein already known to localize at the HTT array and to affect HeT-A transcription ( Figure 1A and 1C ) , [15] , [16] , [33] . To analyze the relative chromatin changes in the homozygous mutant alleles JIL-1z60/JIL-1z60 and in the hypomorph Z47 . 1/Z47 . 1 allele which had been the only one with increased HeT-A transcription , we measured trimethylation of both lysine 9 and 4 of Histone H3 ( H3K9me3 and H3K4me3 ) , the most characteristic histone modifications indicative of repressed and active chromatin . Figure 2A shows the relative changes , compared to wild type , in homozygous mutant alleles of JIL-1 ( JIL-1z60/JIL-1z60 ) , and Z4 ( Z47 . 1/Z47 . 1 ) . The increase observed for H3K9me3 in JIL-1z60/JIL-1z60 mutants is in accordance with the decrease of HeT-A expression in the same allelic combination . In contrast , the increase in HeT-A transcription of the Z47 . 1/Z47 . 1 allele has two different causes , a substantial decrease in H3K9me3 and a simultaneous increase in H3K4me3 , indicative of active transcription . Next , we quantified the presence of JIL-1 at the HeT-A promoter in mutant and wild type flies . Figure 2B shows the changes in JIL-1 occupancy at the HeT-A promoter . Z47 . 1/Z47 . 1 mutant flies show an increase in JIL-1 , in accordance with a higher expression of HeT-A in this mutant allele . In contrast , Su ( var ) 2-505/CyO flies , heterozygous mutant for HP1a , show a moderate decrease of JIL-1 occupancy suggesting a subtle dependence between these two proteins . Interestingly , JIL-1z60/JIL-1z60 shows a substantial increase in the presence of HP1a at the HeT-A promoter , which could be in part responsible for the silencing of HeT-A expression in this same allele ( Figure 2C ) . The Z47 . 1/Z47 . 1 allele on the other hand , shows HP1a levels comparable to Su ( var ) 2-505/CyO flies suggesting an interdependent relationship between these two chromosomal proteins . Finally , we observed that the presence of the Z4 protein decreases in JIL-1 and Su ( var ) 2-5 mutants further interconnecting these three chromosomal proteins in their role of chromatin modulators at the HeT-A promoter ( Figure 2D ) . We did not perform ChIP analyses in the case of the Z42 . 1 and pzg66 alleles for the HeT-A promoter because of two reasons; these alleles did not show a significant difference in HeT-A transcription , very few third instar larvae of the pzg66/Z47 . 1 and Z42 . 1/Z47 . 1 genotypes are obtained from each cross , and for the combination pzg66/Z42 . 1 no animals eclose , making very difficult to perform this experiment with the adequate amount of material . Because JIL-1 and Z4 localize similarly in polytene chromosomes and in the HTT array [15] , [18] , [22] , [23] , and both of them had been found to directly interact with Chromator [27] , [28] , we wondered if the two proteins could be directly or indirectly interacting . We thus performed a co-immunoprecipitation experiment with the endogenous proteins . In Figure 3A we show how both proteins JIL-1 and Z4 are able to co-immunoprecipitate in Schneider S2 cells . Although the input lane of JIL-1 shows a very faint signal caused by the fact that a considerable amount of protein is not extracted and remains in the cell pellet , the protein is clearly detectable in the IP with the Z4 antibody , thus suggesting that a significative amount of soluble or extractable JIL-1 is part of a complex containing Z4 . In the case of Z4 , no substantial amount of the protein was detected in the pellet ( not shown ) . The results of the co-immunoprecipitation experiments suggest that Z4 and JIL-1 belong to the same protein complex when they are at different genomic locations , such as at the HTT array . JIL-1 and Z4 specifically localize at the HTT array but not at the TAS or the cap domain , therefore a specific telomere targeting mechanism should be in place . One of the proteins that specifically localizes at Drosophila telomeres , is the Gag protein of the HeT-A element [37] , [38] . We therefore tested if HeT-A Gag could be involved in the targeting mechanism of Z4 and JIL-1 to the HTT array . We set a co-immunoprecipitation experiment with a recombinant form of HeT-A Gag fused to GFP together with the endogenous Z4 protein . Figure 3B shows that the HeT-A Gag-GFP protein co-immunoprecipitated with Z4 , and that conversely Z4 co-immunoprecipitated with HeT-A Gag-GFP . Although we have not been able to detect co-immunoprecipitation of the endogenous HeT-A Gag with Z4 ( we assume that due to low levels of expression of HeT-A Gag in most Drosophila tissues and cells ) , the overall data suggest that the Z4-HeT-A Gag interaction likely occurs in vivo . We did not detect a JIL-1-HeT-A Gag interaction ( data not shown ) . Because changes in telomere length and telomere chromatin can result in telomere instability , we checked whether JIL-1 and Z4 mutant alleles showed any sign of genomic instability detectable by telomere fusions ( TFs ) . We checked metaphase chromosome preparations from third instar larvae neuroblasts of JIL-1 and Z4 mutants and compared them to a negative control ( w1118 ) and positive controls ( mutant alleles of genes known to participate in telomere protection in Drosophila ) like woc and caravaggio , the gene encoding the HOAP protein [39] , [40] . We could observe TFs involving the same chromosome ( intra-chromosomal ) and different chromosomes ( inter-chromosomal ) in all the Z4 mutant alleles present in this study Z47 . 1 , Z42 . 1 and pzg66 , ( Figure 4A , 2ond , 3rd and 4th column ) . Similarly , TFs were observed in neuroblasts from the positive control , woc964/wocB111 mutant allele ( Figure 4A 5th column ) . No TFs were observed in neuroblast preparations of the negative control stock ( w1118 ) ( Figure 4A 1st column ) . We further investigated whether the observed TFs in the Z4 mutant alleles could be resolved during the next anaphase with no other consequences for the cell , or in contrast could cause asymmetric heredity of the genomic content and initiate genomic instability . We analyzed anaphase neuroblasts of Z47 . 1/Z47 . 1 third instar larvae and compared them again with a positive ( woc964/wocB111 ) and a negative control ( w1118 ) . Figure 4B ( 2nd column ) shows different anaphases of the Z47 . 1/Z47 . 1 neuroblasts where chromatin bridges ( 1st and 3rd panel ) , and aberrant DNA content ( 2nd and 3rd panels ) can be observed . Similarly , different chromatin bridges were observed for neuroblast preparations of woc964/wocB111 larval brains ( 3rd column Figure 4B ) . No abnormal anaphases were observed for the w1118 neuroblast preparations ( 1st column Figure 4B ) . In order to rule out a possible unrelated effect of the genetic background in the Z4 mutant alleles , we knocked down Z4 by RNA interference in S2 cells . Intra and inter-chromosomal TFs were also detected after the preparation of metaphase chromosomes of the interfered cells for the Z4 gene ( Figure 4C , 2nd column and Figure 4D ) . Again TFs were detected in the positive control ( S2 cells interfered for the caravaggio gene , encoding the HOAP protein ) ( Figure 4C 4th column and Figure 4D ) and no TFs were observed when the S2 cells were interfered for an unrelated RNA ( Figure 4C 1st column , and Figure 4D , see materials and methods for details ) . As we had observed an interaction between Z4 and HeT-A Gag , we decided to test if the lack of the latter could also result in telomere instability . Due to the impossibility to obtain mutant alleles for HeT-A in D . melanogaster ( many copies of the HeT-A element exist in any given stock , [31] ) , we decided to interfere for the HeT-A retrotransposon mRNA in S2 cells by RNAi . Figure 4C , 3rd column and Figure 4D show how a decrease on HeT-A mRNA and , as a consequence , on the levels of HeT-A Gag protein results in different TFs in metaphase chromosomes , involving chromatids from the same chromosome and from different chromosomes . Obtaining similar TF phenotypes when interfering for HeT-A Gag and Z4 reinforces the relationship of these two proteins at the HTT array . In order to investigate other possible causes for the telomeric instability observed in the Z4 mutant alleles , apart from the changes in the telomeric chromatin in these mutants , we tested two alternative hypothesis; 1 ) disturbance of the loading of the telomere-capping complex and 2 ) the possible involvement of the non-homologous end joining DNA repair complex in fusing the telomeres after being recognized as a double strand break by the non-homologous end-joining pathway [41] . Thus , we investigated if the loading of one of the major capping components , the HOAP protein [40] , was perturbed in Z4 mutants by crossing them with flies with an endogenous HOAP-GFP protein . In Figure 4D ( 2nd column ) , HOAP signals can be distinguished in TFs from metaphase chromosomes of the Z47 . 1/HOAP-GFP allele suggesting that at least part of the capping complex is still able to recognize and be loaded at the telomere ( see arrowheads for HOAP signals over different TFs , Figure 4D ) . No TFs are seen in the HOAP-GFP metaphase chromosomes ( Figure 4D 1st column ) . In order to rule out a possible contribution of the non-homologous end joining repair complex , we analyzed the contribution of the Ligase IV enzyme in the observed TFs in the Z4 mutant alleles [41] , by combining the Z4 mutation ( Z47 . 1 ) with a mutation for the gene encoding the Ligase IV enzyme ( ligIV−/− ) . Therefore , in case that the TFs observed in the Z4 mutants were caused by this mechanism , we should have seen a decrease in the number of TFs when the two mutations are combined . The ligase IV allele that we assayed , ( ligIV−/− ) does not show a TF phenotype compared with our control strain , ( w1118 ) ( Figure 4F ) . As shown in Figure 4E , the TF number detected in Z47 . 1/ligIV double mutant was not statistically different from the Z47 . 1/TM3Sb allele . The results from these experiments strongly suggest that Z4 controls telomere stability independently of the DNA repair machinery and that Z4 is not directly involved in the loading of the telomere-capping complex . We also investigated if the mutant alleles for JIL-1 might also have a problem of telomere stability . We inspected metaphase chromosomes of 3rd instar larva neuroblasts for the JIL-1z60 , JIL-1z2 and JIL-1Su ( var ) 3-1mutant alleles and did not find any significant telomere fusion ( TFs ) phenotype compared to w1118 ( data not shown ) . We also inspected the possibility of TFs in the trans-heterozygous combination JIL-1z60/JIL-1z2 and found no result significantly different from the w1118 strain ( Figure 4F ) . Next , we decided to test if a JIL-1 mutant in a Z4 mutant background could rescue the TF phenotype . Because in a JIL-1 mutant background some heterochromatin marks increase their presence in the HeT-A promoter , ( H3K9me3 and HP1a , Figure 2A and 2C and Figure 5 ) it is possible that they are enough to compensate the lower amount of Z4 in the JIL-1 mutation ( Figure 2D ) . With this purpose we tested the double mutant combinations ( Z47 . 1/JIL-1z2 , pzg66/JIL-1z60 , z42 . 1/JIL-1z2 ) and we found no significant difference between the single Z4 mutant alleles ( Figure 4F ) , indicating that the partial increase in heterochromatin marks is not sufficient to compensate the lower levels of Z4 in a JIL-1/Z4 mutant background . Therefore , the role of Z4 in the structure of the telomere chromatin is key to guarantee telomere stability in Drosophila .
Much effort has been put forward to study the negative regulation of the telomeric retrotransposons [42] ( for a review , see [6] ) as these elements have been able to maintain their personalities or individual characteristics as transposable elements while fulfilling a cellular role [9] , [43]–[45] . HeT-A is a retrotransposon with the essential function of telomere elongation , and therefore a fine-tuned regulation capable of achieving both , telomere replication and avoiding putative harmful transpositions and consequently genomic instability should be in place to ensure a normal telomere structure . During development , all the tissues that undergo active cell division such as the brain or the imaginal discs need certain levels of telomere replication . Naturally , these are the tissues where the telomeric retrotransposons are more expressed [35] . Here , we demonstrate that the JIL-1 kinase is important to achieve wild type levels of HeT-A transcription in larval tissues , being the first positive regulator of telomere transcription described in Drosophila . Although in Drosophila the role of JIL-1 in activating transcription has remained controversial [46] , [47] , at least in the HTT array it could act as a positive regulator of transcription for three different reasons: 1 ) When telomere elongation is needed , a fast activation of HeT-A transcription should be expected . Accordingly , the mammalian JIL-1 orthologous MSK1/2 have been shown to rapidly induce gene expression on the face of stress or steroid response [48] . 2 ) HeT-A is embedded into the HTT array , a domain that needs to be protected from the influence of the repressive heterochromatin of the neighboring TAS domain [16] . JIL-1 has been suggested to protect the open chromatin state from the spreading of neighboring repressive chromatin at certain genomic positions [49] , [50] . 3 ) The decrease in expression that we have observed in the JIL-1 mutants is moderate ( Figure 1C ) . Recent data at genomic level revealed that JIL-1 function agrees with a reinforcement of the transcriptional capability of a particular genomic domain rather than net activation [51] . In summary , the telomeric role of JIL-1 at the HTT array is in agreement with all of the above . Phalke and co-workers [52] suggest that JIL-1 has a role in retrotransposon silencing in general and has no effect on telomere transcription . A possible explanation for this discordance with our results and hypothesis is that the mutant allele of JIL-1 assayed by Phalke and co-workers , the JIL-1Su ( var ) 3-1 allele , corresponds to a C-terminal deletion of the JIL-1 protein that causes the protein to miss-localize and phosphorylate ectopic sites [30] , [36] . The ectopic phosphorylation caused by the JIL-1Su ( var ) 3-1 allele would activate the expression above wild type levels in those genes that normally are not targeted by JIL-1 , as it happens to be the case for the Invader4 retrotransposon . In our study , we have assayed the JIL-1Su ( var ) 3-1 allele obtaining similar result than for the wild type stock , likely for similar reasons ( Figure 1A and 1B ) . Supporting this , in addition of the JIL-1Su ( var ) 3-1 , we present here data from two more JIL-1 alleles ( Figure 1 ) , JIL-1z60 and JIL-1z2 , that correspond to loss of function alleles and , in both cases , result in a substantial decrease in HeT-A transcription ( Figure 1A and 1B ) . Moreover , the changes in telomere transcription that we report here have been assayed directly on the major component of the HTT array , and not through a reporter [52] . Our data demonstrates that JIL-1 is necessary to maintain active transcription of the telomeric retrotransposon HeT-A or , what is the same , transcription from the telomeres in Drosophila . Although we have demonstrated that JIL-1 is necessary to maintain transcription from the HTT array , we have not detected a decrease in telomere length in the JIL-1 mutant alleles . A reasonable explanation for this observation is that the JIL-1 mutant alleles here analyzed ( JIL-1z60 and JIL-1z2 ) have been maintained as heterozygous . It is therefore possible that one copy of JIL-1 is enough to promote enough HeT-A transcription to elongate significantly the telomeres when needed . Although in the case of the hypomorph mutation Z47 . 1 we have observed an increase in HeT-A transcription and HeT-A copy number significantly above the control strain ( w1118 ) , the null alleles Z42 . 1 and pzg66 do not show an up-regulation of HeT-A transcription or an increase in its copy number ( Figure 1A and 1B , Figures S1 and S2 ) . Although we have crossed all the stocks to the w1118 strain to minimize the effects of the genetic background , it could still have a certain influence when comparing the pzg66 allele with the Z47 . 1 . Nevertheless the Z47 . 1 and Z42 . 1 alleles come from the same genetic background [18] . A possible explanation could rely on the fact that the Z47 . 1 mutation is a hypomorph mutation where a small amount of Z4 protein is still present . By ChIP analyses we have detected an increase of JIL-1 protein at the HeT-A promoter above control levels , which could explain in part the major transcription of HeT-A in this mutant background ( Figure 2B ) . Because Z4 and JIL-1 interact ( Figure 3A ) , it is possible that although low , the amount of Z4 present in the Z47 . 1 allele is enough to recruit JIL-1 to the HeT-A promoter . In the pzg66 and the Z42 . 1 null alleles , JIL-1 cannot be recruited towards the HeT-A promoter and there is no increase in transcription . Nevertheless , with our current data we cannot conclude that Z4 directly controls the level of HeT-A transcription . We have detected a phenotype of telomere instability in all three Z4 mutant alleles Z47 . 1 , Z42 . 1 and pzg66 ( Figure 4 ) , suggesting a role of this chromosomal protein in guaranteeing telomere stability in Drosophila . Although a number of genes involved in the capping function in Drosophila still remain unidentified [3] , we do not have evidences that Z4 directly participates in the protection of the telomeres . Mutant alleles of genes directly involved in the capping function , such as woc or caravaggio ( HOAP ) , show multiple and more numerous TFs in larval neuroblasts ( Figure 4A , 4C and [39] , [40] ) than the ones that we have observed in the Z4 mutant alleles . Moreover , we have been able to detect staining for one of the major capping components , the HOAP protein , in the TFs of Z4 mutant neuroblast cells ( Figure 4D ) , indicating that the telomere-capping complex is still loaded to a certain degree . Instead of directly participating in the capping , our hypothesis is that the major chromatin changes caused by the lack of Z4 at the HTT array result in a secondary loss of necessary chromatin and capping components like HP1a ( Figure 2C ) . Results from the ChIP experiments ( Figure 2 ) suggest a relationship between JIL-1 , Z4 and HP1a in fine-tuning the chromatin structure at the HTT array . HP1a has a dual role at the telomeres explained by its participation in both the capping function and the repression of gene expression that also exerts in other genomic domains [33] , [53] . In the HP1a Su ( var ) 2-505 allele , which it is known to have a major transcription of HeT-A and problems of telomere stability , we have observed a pronounced decrease in Z4 and JIL-1 ( Figure 2B and 2D ) . In the Z47 . 1 allele the decrease in Z4 protein is accompanied by a similar decrease in H3K9me3 and HP1a at the HeT-A promoter ( Figure 2A , 2C and 2D ) . Finally in the JIL-1z60 allele the increase in silencing epigenetic marks like H3K9me3 and HP1a is also accompanied by a decrease in Z4 ( Figure 2 ) . In particular , the pronounced dependence of the presence of HP1a and Z4 , points toward the loss of HP1a and H3K9me3 to a possible cause for telomere instability in the Z4 mutant alleles here studied . Interestingly , in the Su ( var ) 2-504/Su ( var ) 2-505 heteroallelic combination ( considered a null mutation ) [33] ) , 15% of telomeres involved in telomere associations are still able to recruit the HOAP protein [40] . Therefore our data on HOAP localization in the Z4 mutant alleles is still consistent with the TFs being caused by the decreased availability of HP1a in these cells ( Figure 2C ) . The above results demonstrate that Z4 in a coordinated manner together with JIL-1 and HP1a is an important component of the telomere chromatin in Drosophila , which upon its reduction causes significant changes in the chromatin of the HTT array , which are the cause of the observed telomere instability in all the Z4 mutant alleles here studied ( discussed below , Figure 5 ) . We have been able to detect a biochemical interaction between JIL-1 and Z4 , and our data suggests that these two proteins can be components of the same protein complex ( Figure 3A ) . This interaction had been previously suggested because both proteins have been found co-localizing in different genomic locations , but no direct proof existed to date [15] , [18] , [22] , [23] , [27] , [28] . In each genomic location where the Z4-JIL-1 complex is needed , a special mechanism of recruitment should exist . Importantly , we have shown how Z4 specifically interacts with HeT-A Gag ( Figure 3B ) . HeT-A Gag is the only protein encoded by the HeT-A element and has been shown to specifically localize at the telomeres [37] , [38] . HeT-A Gag has been shown to be in charge of the targeting of the transposition intermediates for the HeT-A element and also for its telomeric partner the TART retrotransposon [37] . Interestingly , when we studied the consequences for telomere stability after knocking down the HeT-A gag gene by RNAi , we also observed similar TFs than when knocking down the Z4 gene , further relating the action of both genes in telomere stability . Z4 is known to participate in different protein complexes with roles in different genomic locations [19] , [21] , [27] . Because it has been demonstrated that Z4 is able to associate with a variety of proteins in these complexes , we think that the description of a mechanism for its specific targeting to telomeres through one of the telomeric retrotransposon proteins is especially relevant . Integrating information from previous literature and the results exposed by this study , we propose a possible model to describe the state of the chromatin at the HTT array in each of these three mutant scenarios; JIL-1 , Z4 and Su ( var ) 2-5 , as well as in wild type ( Figure 5 ) . We should take into account that 1 ) HP1a has been shown to spread along the HeT-A sequence [16] . 2 ) The structure and the phenotypes of the different Z4 mutant alleles suggest a possible role of this protein in setting and maintaining the boundaries between heterochromatin and euchromatin in polytene chromosomes [18] , [28] . 3 ) JIL-1 has been extensively shown to be important to counteract heterochromatinization and , when missing , causes a spreading of heterochromatin markers such as H3K9me2 , HP1a and Su ( var ) 3-7 [49] , [50] , [54] , [55] . 4 ) JIL-1 has been found to co-localize with Z4 at the band-inter-band transition in polytene chromosomes and also to co-purify with Z4 in different protein complexes [18] , [27] , [28] . In addition to this , we have been able to detect a biochemical interaction between JIL-1 and Z4 ( Figure 3A ) , as well as , a certain dependence on the presence of JIL-1 for the proper localization of Z4 ( Figure 2D ) , suggesting a possible role of JIL-1 upstream of Z4 . Finally , 5 ) The ChIP analyses in this study suggest a certain dependence of Z4 on HP1a or onto similar chromatin requirements for the loading of both proteins at the HTT array , more specifically at the HeT-A promoter ( Figure 2A , 2C and 2D ) . Summarizing all of the above , we propose that the chromatin at the HeT-A promoter could have the following structure: In a wild type situation ( Figure 5A ) , the HeT-A promoter contains intermediate levels of HP1a , JIL-1 and Z4 . HP1a would be spread along the HTT array , JIL-1 would be concentrated at the promoter region of HeT-A guaranteeing certain level of expression and Z4 would be important to set the boundary between these two opposite modulators . In a JIL-1 mutant , ( Figure 5B ) , the lack of JIL-1 would disturb the Z4 boundary causing a slight decrease in the Z4 presence . This result is in agreement with a Z4-JIL-1 partial interaction ( Figure 3A and [28] ) . The decrease in JIL-1 presence and the disturbance of the boundary causes a spreading of HP1a into the HeT-A promoter , increasing its presence and repressing transcription from the HTT array ( Figure 1 and Figure 2 ) . In a Z4 mutant ( Figure 5C ) , the disappearance of the boundary together with the significant decrease in H3K9me3 causes a decrease in HP1a binding and a substantial modification of the chromatin at the HTT array ( Figure 2 ) . The lack of sufficient HP1a at the HTT array causes a destabilization of the chromatin at the cap domain triggering telomere instability as a result ( Figure 4 ) . This scenario applies to the three Z4 mutant alleles present in this study , the hypomorph Z47 . 1 , and the nulls pzg66 and Z42 . 1 . On one hand the loss of some Z4 in Z47 . 1/Z47 . 1 genotype produces overexpression of HeT-A because in addition to a relaxation of the chromatin , part of JIL-1 is still recruited to the HeT-A promoter ( Figure 2A and 2B ) and activates transcription in a more effective way than in a wild type situation . Finally , in a Su ( var ) 2-5 mutant background ( Figure 5D ) , the lack of HP1a along the HeT-A sequence allows a relaxation of the boundary causing a spread of JIL-1 and Z4 from the HeT-A promoter towards the rest of the array and creating as a consequence , permissive chromatin environment releasing HeT-A silencing ( Figure 1 , Figure 2 , and [33] ) . Our model does not completely explain the complex relationships that regulate telomere chromatin , likely because other important components are yet to be described or associated with the ones presented here . For example , other chromatin regulatory components that have been associated with Drosophila telomeres are such as: the deacetylase Rpd3 , with a regulatory role on chromatin structure [56] , and the histone methyltransferase SetDB1 and the DNA methylase Dnmt2 [52] , [57] which by acting in the same epigenetic pathway repress transcription of HeT-A as well as of retroelements in general [52] . Future in depth studies on additional chromatin components will allow us to complete and detail even more the description of the chromatin at the HTT array , and allow a better understanding of the mechanism of retrotransposon telomere maintenance and the epigenetic regulation of eukaryote telomeres in general . In the meantime , here we describe a plausible scenario in the view of our transcription and ChIP data . The results shown here demonstrate the role of JIL-1 as the first described positive regulator of telomere ( i . e . HeT-A ) expression in Drosophila . Because HeT-A is in charge of telomere maintenance in Drosophila , these results are key to understand how telomere elongation is achieved in retrotransposon telomeres . We also demonstrate that Z4 is necessary to guarantee telomere stability . The data presented here strongly suggest that JIL-1 and Z4 exert these functions by maintaining an appropriate telomere chromatin structure by a coordinated action together with other known telomere components such as HP1a . Moreover , we show that JIL-1 and Z4 interact biochemically . Last , and importantly for understanding how the specific role of the Z4-JIL-1 complex at the telomeres is defined and differentiated from its role in other genomic regions , we show that Z4 might interact with the HeT-A Gag protein , providing evidence for a targeting mechanism that specifically retrieves this complex to the telomeres .
Fly stocks were maintained and crosses performed at 25°C on standard Drosophila corn meal medium . w1118 strain was used as control . JIL-1z60/TM6 , JIL-1z2/TM6 and JIL-1Su ( var ) 3-1/TM3SbTb stocks were provided by Kristin M . Johansen . Z47 . 1/TM3Sb and Z42 . 1/TM3Sb came from Harald Eggert and Harald Saumweber . pzg66/TM6 from [21] was a kind gift of Anja Nagel . The stocks ligIV −/− and HOAP-GFP were obtained from Yikang Rong . The woc964/TM6 and wocB111/TM6 alleles were provided by Maurizio Gatti . Su ( var ) 2-505/CyO was obtained from Bloomington Stock Center . Genomic DNA was extracted from adult flies to quantify the number of HeT-A copies in each strain . Ten third instar larvae without salivary glands were homogenized in 200 µl solution A ( 0 . 1 M Tris-HCl pH 9 . 0 , 0 . 1 M EDTA and 1% SDS ) and incubated at 70°C for 30 min . 28 µl 8 M KAc were added and the samples incubated for 30 min on ice . Cell debris were harvested at maximum speed for 15 min at 4°C . The supernatant was transferred to a new tube and the DNA precipitated by adding 0 . 5 volumes isopropanol and centrifuging at 15 . 000 rpm for 5 min . Pelleted DNA was washed with 1 volume 70% ethanol and centrifuged . Finally , the DNA pellet was air-dried , and re-suspended in 50 µl 1× TE by rotating o/n at 4°C . After genomic DNA extraction , the number of copies was determined by quantitative Real-Time PCR using 2 ng of DNA per reaction . Primers used for real time HeT-A_F ( CCCCGCCAGAAGGACGGA ) and HeT-A_R ( TGTTGCAAGTGGCGCGCA ) for the 3′UTR region , HeT-A Real Time Gag F ( ACAGATGCCAAGGCTTCAGG ) and HeT-A Real Gag Time R ( GCCAGCGCATTTCATGC ) for the Gag gene , Actin_F ( GCGCCCTTACTCTTTCACCA ) and Actin_R ( ATGTCACGGACGATTTCACG ) . Total RNA was isolated from ten whole third instar larvae and extracted using RNeasy Mini Kit ( Qiagen ) according to manufacturer's protocol . RNase Free DNase Set ( Qiagen ) was used to remove genomic DNA contaminations as follows: one on column during the extraction accordingly to manufacturer's protocol , and two in solution for 2 hours at 37°C . RNA was cleaned by precipitation and its quality was assessed using NanoDrop spectrophotometry . One microgram of RNA was reverse transcribed into cDNA using Transcriptor First Strand cDNA Synthesis Kit ( Roche ) with oligo ( dT ) primers , and the expression of the different transcripts analyzed by quantitative Real-Time PCR . For each fly strain , two independent RNA extractions were prepared and analyzed three independent times . Primers used for real time PCR: HeT-A_F ( CCCCGCCAGAAGGACGGA ) and HeT-A_R ( TGTTGCAAGTGGCGCGCA ) for the 3′UTR , HeT-A Real Time Gag F ( ACAGATGCCAAGGCTTCAGG ) and HeT-A Real Gag Time R ( GCCAGCGCATTTCATGC ) for the Gag gene . Actin_F ( GCGCCCTTACTCTTTCACCA ) and Actin_R ( ATGTCACGGACGATTTCACG ) . Brains and imaginal discs from third instar larvae were dissected in 1× PBS with protease inhibitors . After dissection , the brains were resuspended in 5 ml buffer A ( 60 mM KCl , 15 mM NaCl , 15 mM HEPES pH 7 . 6 , 0 . 5% Triton X-100 , 0 . 5 mM DTT , complete EDTA-free protease inhibitor cocktail from Roche ) with 1 . 5% formaldehyde , homogenized in a Wheaton Dounce and incubated for 15 min at room temperature . Crosslinking was stopped by adding glycine to a final concentration of 0 . 125 M and incubating 5 min at 4°C on a rotating wheel . Brains were washed 3 times with Buffer A and resuspended in lysis buffer ( 140 mM NaCl , 15 mM HEPES pH 7 . 6 , 1 mM EDTA , 0 . 5 mM EGTA , 1% Triton X-100 , 0 . 5 mM DTT , 0 . 1% sodium deoxycholate , complete EDTA-free protease inhibitor cocktail from Roche ) . Cross-linked nuclei were fragmented using bioruptor sonicator ( high amplitude , 15 sec ON and 45 sec OFF ) . For the following steps a standard protocol ( Upstate ) was used . Thirty brain/discs complexes were used per IP . Chromatin was immunoprecipitated with the following antibodies: anti-H3K9me3 ( ab8898 , Abcam ) , αnti-H3K4me3 ( ab8580 , Abcam ) , αnti-HP1 ( The anti-HP1 ( C1A9 ) antibody developed by Lori L . Wallrath was obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by The University of Iowa , department of Biology , Iowa City , IA 52242 ) , αnti-JIL-1 ( gift from Kristen M . Johansen ) , and αnti-Z4 ( gift from Harald Saumweber ) . Three independent ChIP samples were analyzed and the amount of immunoprecipitated DNA was calculated by quantitative real-time PCR using iQ SYBR Green Supermix ( BioRad ) . Primers used for real time PCR: HeT-A5′UTR_F ( TCGCGCTTCCTCCGGCAT ) and HeT-A 5′UTR_R ( GCGGTTATTACATTACGGCGG ) , RpL32-F ( CAAGAAGTTCCTGGTGCACAA ) and RpL32-R ( AAACGCGGTTCTGCATGAG ) . Quantitative real-time PCR was performed to determine HeT-A copy number and HeT-A expression , and in ChIP experiments . The iQ5 Multicolor Real-Time PCR Detection System was used and the iQ SYBR Green Supermix ( BioRad ) was used to prepare the reactions . Relative levels of HeT-A expression were determined using the threshold cycle and normalized to actin levels ( or RpL32 for ChIPs ) . Three independent experiments of two samples each strain were performed . Third instar larvae brains were dissected in 0 . 7% NaCl solution , incubated in 10 µM colchicine ( Roche ) for 2 hours and submitted to a hypotonic shock ( 0 . 5% sodium citrate ) for 10 min . Brains were fixed in 60% acetic acid and squashed . For anaphase preparation , brains were dissected as before , the hypotonic shock was omitted , and the brains were successively immersed in 45% and 60% acetic acid . DNA was stained with DAPI in mowiol medium . Mitotic chromosome preparations were analyzed on a Zeiss Imager Z2 fluorescence microscope using the AxioVision software . Third instar larvae brains were dissected in 1× PBS with protease inhibitors and incubated in 0 . 5 mg/ml colcemid ( Roche ) for 2 hours . A hypotonic shock was applied by incubating brains in 0 . 5% sodium citrate for 10 min . Proteins were fixated by incubating with Brower's Fixation Buffer ( 0 . 15 M PIPES , 3 mM MgSO4 , 1 . 5 mM EGTA , 1 . 5% NP-40 , and 2% formaldehyde , pH 6 . 9 ) for 3 min . Brains were washed in 1× PBS-Triton ( 0 . 1% ) for 3 min and allowed to soak in 50% glycerol for 5 min . Brains were squashed in a drop of glycerol , immersed in liquid nitrogen , and mounted in DAPI-mowiol medium . Mitotic chromosome preparations were analyzed on a Zeiss Axio Imager . Z2 fluorescence microscope using the AxioVision software . Fragments of the Z4 , HeT-A gag , hoap , and Sart1 ( non-LTR retrotransposon from Bombyx mori ) coding sequences were amplified by PCR and cloned in pSTBlue-1 vector to produce dsRNA . Single stranded RNAs were synthesized by using SP6 and T7 RNA polymerases ( Promega ) , both strains were incubated for 5 min at 90°C and annealed by slowly cooling to room temperature to obtain the dsRNA . dsRNA was then precipitated and treated with DNase ( Qiagen ) and RNase A ( Roche ) for 15 min at 37°C . A phenol:chloroform extraction was performed followed by precipitation and quantification of dsRNA with NanoDrop spectrophotometer ND1000 . 50 µg of dsRNA were diluted in 1 ml of supplemented Schneider medium , added drop-wise to a total of 1 . 5×107 cells , and incubated at 25°C . The same protocol was repeated at 24 h and 48 h after seeding the cells . An aliquot of cells was collected at 24 h , 48 h , and 72 h after seeding . For description of cytology experiments , see next section S2 cells metaphase chromosome preparation . Gene fragments of about 550 bp were amplified using the primers: HeTGag-RNAi-F ( CTAGCGGCAAACAACATCG ) and HeTGag-RNAi-R ( GGGATTGCAGATTCTTGGC ) to amplify HeT-A sequence with accession number: X68130 from nucleotide ( nt ) 2701 to nt 3255 , Z4-RNAi-F ( TAATTATCCAGCAGGGACAG ) and Z4-RNAi-R ( CAATCAGATCTGGTCTTTGTCTCCGTAAAC ) to amplify the Z4 gene acc num: CG7752 from nt 3046 to nt 3383 , HOAP-RNAi-F ( GCCGAGACTAAGAAGCAGAAC ) and HOAP-RNAi-R ( CCTGATCGTCAGGCTCTTG ) amplify the caravaggio gene acc . num: CG6219 from nt 1689 to nt 2166 , and SART1-RNAi-F ( CAACGGCAGCAGAATCAATG ) and SART1-RNAi-R ( CGTAATTTCTCCGCCAGCAA ) amplify the SART1 retrotransposon acc . number: D85594 from nt 1943 to nt 2432 . All amplified regions were checked for off-site targets . 500 µl collected cells were treated with 500 µl colcemid ( 10 µg/ml; Roche ) during 2–3 h in the dark . Cells were centrifuged 3 min at 1500 rpm and the pellet was washed with PBS . Cells were centrifuged again and the pellet resuspended with 500 µl 0 , 5% sodium citrate . After 10 min r . t . incubation , cells were centrifuged , resuspended in 1 mL fixation solution ( methanol∶acetic , 3∶1 ) and incubated for 10 min . Cells were centrifuged again , re-suspended in 50 µl Fixation Solution , and cells were dropped onto a microscope slide . Slides were air dried and mounted in DAPI-containing Mowiol medium . Images were obtained using the Zeiss Axio Imager . Z2 fluorescence microscope . Drosophila S2 cells were seeded at 3×106 cells/ml and transfected with one microgram of plasmid DNA using Effectene Transfection Reagent ( Qiagen ) , accordingly to manufacturers protocol . Cells were incubated for 48 hours at 25°C and collected by centrifugation at 2000 rpm for 5 min , washed twice in 1× PBS and frozen at −80°C . HeT-A Gag-GFP plasmid was used in cell transfection [37] . Protein extracts from S2 cells were prepared in 1 ml lysis buffer ( 50 mM Tris-HCl pH 7 . 4 , 100 mM NaCl , 1% TritonX-100 , 1 mM EDTA , 1 mM EGTA , and Complete EDTA-free protease inhibitor cocktail from Roche ) , incubated on ice for 20 min , and centrifuged at 13 000 rpm for 15 min at 4°C . Fresh lysates were incubated with 50 µl PureProteome Protein A and Protein G Magnetic Beads ( Millipore ) coated with specific antibodies , for 4 hours at 4°C with rotation . The magnetic beads were previously incubated with the respective antibodies in 500 µl lysis buffer for one hour at 4°C with rotation and washed 3 times with 500 µl lysis buffer . Immunocomplexes were washed 3 times with lysis buffer and eluted from the beads with 50 µl 1× sample buffer . Samples were boiled for 10 min , loaded on a SDS-PAGE gel and analyzed by Western Blot . Anti-GFP ( Invitrogen , A11120 ) , anti-Z4 [58] , anti-JIL-1 ( mouse , gift from Kristen Johansen ) , and control mouse IgG ( Santa Cruz Biotechnology , sc-2025 ) were used for protein immunoprecipitation , and anti-HeT-A Gag , anti-Z4 and anti-JIL-1 [59] were used in Western Blot experiments . | Drosophila telomeres constitute a remarkable exception to the general telomerase mechanism of telomere maintenance in eukaryotes . The essential role of the telomeric transposons HeT-A , TART , and TAHRE ( HTT ) in this organism contrasts with the strong conservation of their retrotransposon personalities . The particularities of this system add an extra layer of complexity to the control of telomere length in Drosophila; on one hand , telomere expression should be fine-tuned in order to achieve telomere function whenever needed; on the other , terminal transposition should be tightly controlled to guarantee genomic stability . Here , we report the dual role of the JIL-1-Z4 complex in regulating the HeT-A retrotransposon transcription ( by the action of the JIL-1 kinase ) and in guaranteeing the stability of telomeres ( by the zinc finger protein Z4 ) . We show how the loss of JIL-1 and Z4 causes major changes at the chromatin of the HeT-A promoter that can explain the phenotypes that we observe in JIL-1 and Z4 mutant alleles . Moreover , we give evidence of the involvement of the HeT-A Gag protein in the recruitment of Z4 to the HTT array , and we demonstrate how the disruption of this interaction has fatal consequences for telomere stability . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"telomeres",
"cell",
"biology",
"chromosome",
"biology",
"biology",
"molecular",
"cell",
"biology"
] | 2012 | The Chromosomal Proteins JIL-1 and Z4/Putzig Regulate the Telomeric Chromatin in Drosophila melanogaster |
Inherently dynamic , chromosomes adopt many different conformations in response to DNA metabolism . Models of chromosome organization in the yeast nucleus obtained from genome-wide chromosome conformation data or biophysical simulations provide important insights into the average behavior but fail to reveal features from dynamic or transient events that are only visible in a fraction of cells at any given moment . We developed a method to determine chromosome conformation from relative positions of three fluorescently tagged DNA in living cells imaged in 3D . Cell type specific chromosome folding properties could be assigned based on positional combinations between three loci on yeast chromosome 3 . We determined that the shorter left arm of chromosome 3 is extended in MATα cells , but can be crumpled in MATa cells . Furthermore , we implemented a new mathematical model that provides for the first time an estimate of the relative physical constraint of three linked loci related to cellular identity . Variations in this estimate allowed us to predict functional consequences from chromatin structural alterations in asf1 and recombination enhancer deletion mutant cells . The computational method is applicable to identify and characterize dynamic chromosome conformations in any cell type .
The three-dimensional organization of the genome was shown to dynamically adapt to nuclear function and complexity [1–3] . Chromatin fibers can be represented by a polymer random coil adopting a considerable , largely unappreciated number of states in response to DNA metabolism [4–7] . How the intrinsic folding of a chromosome within the nucleus and the relative position of loci on specific chromosomes contribute to these processes is not known . Chromosome conformation capture techniques that rely on protein-DNA cross-linking have provided precious information on the frequency of three-dimensional long range molecular contacts between genomic DNA segments [4 , 8–10] . Microscopy approaches are , on the other hand , necessary to further our understanding of the dynamics of DNA transactions because they allow analysis of live and single cells [11 , 12] . Using two-color distance measurements between two fluorescently labelled loci in 3D in fixed or living cells has yielded data to infer chromatin compaction parameters by polymer modeling [13–15] . These parameters were also used to model 4C and HiC data ( for example see [4 , 8 , 12] ) or to simulate genome-wide chromosome organization[2] . Because the nucleosome fiber is highly flexible , inferring fiber properties from measuring distances between two labeled loci implies making a number of assumptions to describe the actual path of the fiber . In order to obtain spatial , 3D information , at least 3 points in space are needed . Three points provide geometrical information that can be used to establish physical models . In the nucleus , these points can include reference structures , such as the nuclear envelope or the nucleolus in yeast , or three distinct DNA tags . Few previous studies used three labeled loci: in fixed , mammalian cells , distances and angles within a triangle formed by three probes in the same nucleus were measured to study changes in chromatin domain compaction [16]; in live bacteria cells , three labels were used to determine the position of two chromosomal loci relative to a third , reference one , in 2D [17] . A geometrical interpretation of changes in chromosome folding was not proposed . The challenge for the analysis of three points in space is to develop mathematical algorithms allowing comparison of two sets of data , because commonly used tests for comparing distributions ( 1D , using Wilcoxon or KS tests ) are no longer applicable . New computational tools are needed to identify specific features which may not be the most prominent ones , notably in cellular systems evolving over time or in space . To address this need , we developed a new system to fluorescently label three distinct genomic loci in living cells simultaneously and implemented mathematical algorithms to analyze the relative 3D positions of the labelled DNA loci . We used S . cerevisiae chromosome 3 ( Chr3 ) as a model to study its folding . Chr3 is a short chromosome of only 320 kb with a tripartite organization: two AT rich domains flank a GC-rich centromere proximal region and forms a ring-like structure mediated by frequent contacts between heterochromatin loci near the ends [18–20] . This chromosome has received particular attention because the study of the mating type loci , and their interconversion , contributed to fundamental knowledge about cell lineage control , silencing and recombination [21] . Numerous genetic data contributed to a better understanding of the directionality of the mating type switch , yet the underlying mechanism is not known [22 , 23] . A role for chromosome architecture is usually invoked as a possible driving force for donor choice [24–27] . We have the possibility now to address this question by studying the nuclear position of the three mating type loci in the nucleus simultaneously . In this study , we adapted a third system , based on the λ operator[28] , from bacteria to yeast . In combination with the widely used Tet and Lac operators , we simultaneously tag three distinct loci in live yeast cells . We then developed a computational approach to demonstrate , that the frequency of specific positional combinations between three loci point to folding properties of a small chromosome . We find that folding of the left arm is different in MATa and MATα cells . In strains lacking either a component involved in chromatin compaction , the chaperone Asf1 , or the recombination enhancer ( RE ) , the mating type specific conformation of Chr3 is altered . Our results suggest that chromosomal organization brought about by fiber folding and heterochromatic domains contribute to control of the yeast mating type loci by regulating long-range contacts .
To simultaneously label three distinct loci , we adapted the bacterial λ repressor operator system ( λO and λCi ) for use in yeast [28 , 29] and combined it with the existing Lac and Tet systems [30] ( Fig 1A ) . Strains bearing three distinct labels were created by integration of constructs encoding fluorescent repressor fusion proteins followed by the integration of the operator sequences into the yeast genome by transformation . Yeast strains are listed in Table 1 . Expression of an operon containing the phage λcI repressor gene fused to the gene encoding YFP was placed under the control of the pURA3 promoter . The λ repressor sequence bears amino acid modifications G48S and Y210H which strengthen DNA binding and reduce tetramerization , respectively [28] . The λcI sequence was amplified by PCR using pRFG116 and kindly provided by Dr D Chattoraj . During the PCR reaction a NotI recognition site was introduced . The pURA3 promoter was amplified from the pCJ97 plasmid with an introduction of a NheI site . The two fragments were then digested by NheI and ligated to generate a pURA3- λcI fragment . This fragment was double-digested with EcoRI and NotI , and ligated to the double-digested ( EcoRI/NotI ) pCJ97 plasmid bearing a YFP sequence . Following the validation steps discussed above , we subcloned the pURA3- λcI-YFP in a plasmid bearing pHIS-CFP-lacI-pUra3-TetR-YFP ( named pGVH30 [18] ) , plasmid which allows expression of two fusion proteins from a single plasmid thus requiring a unique selection marker ( ADE2 ) , finally leading to plasmid pIL01 . The λO were extracted from the bacterial pRFB122 plasmid by an XhoI digestion and ligated to the pSR6 plasmid [31] and digested by XhoI/SalI . Integration of the operator repeats was performed using a cloning free technique . The method entails insertion of a marker gene generated by PCR using long primers , with the optimal size of the locus-specific primer tails varying from 60 to 80 nt , near the locus of interest . The marker is then replaced by the operator repeats found in pSR plasmids . The following PCR-amplified genomic fragments ( SGD coordinates ) were used for insertion within 0 . 5 to 4 kb from the respective loci: 15160 kb to 15773 kb for HML , 294898 kb to 295245 kb for HMR , 90917 kb to 92521 kb for LEU , 239254 kb to 240927 kb for ARS1413 and 16431 kb to 17993-kb for TelVI . The PCR-amplified sequences of HMR and HML were cloned into a pAFS52-lacO plasmid and in the pAFS59-tetO bearing plasmid , respectively[18] . The lambda operator repeats were integrated at 197197–197310 kb on Chr3 to label the MAT locus . The ASF1 gene ( 196334 kb to 197080 kb on Chr10 ) or the RE ( 29083 kb to 29748 kb on Chr3 ) were replaced by the hygromycin resistance gene amplified from pAG32 ( 1743bp ) . Alternatively , the RE ( 28987–29852 on Chr3 ) was replaced by loxP flanked hygromycin resistance gene amplified from pGI10 . Galactose induced expression of the recombinase from pHS47 induces deletion of the entire cassette ( 162bp remaining plasmid sequences ) . The lambda operator ( λO ) system comprises a relatively small number of repeated binding sites; 64 repeats compared to the usual 128–256 repeats of TetO and LacO arrays . The focus formed by binding of multiple repressor fusion proteins to the arrays integrated into the genome is easily detectable using conventional fluorescence microscopy ( Fig 1A ) . The constitutive expression of the λcI repressor fused to YFP and its binding to a short 1 . 5kb DNA λO fragment was not toxic to the cell , which displayed growth rates identical to unmodified yeast cultures . λcI-YFP fusion proteins diffuse freely in the cytoplasm , and in the nucleoplasm apart from the vacuole ( Fig 1A ) . The focus formed at a site near the MAT locus ( 197 kb along right arm of Chr3 ) tagged using λO was positioned in the center of the nuclear lumen with the same frequency as the same genetic locus tagged with LacO . Live microscopy was performed using an Olympus IX-81 wide-field fluorescence microscope , equipped with a CoolSNAPHQ camera ( Roper Scientific ) and a Polychrome V ( Till Photonics ) , electric piezo with accuracy of 10 nm and imaged through an Olympus oil immersion objective 100X PLANAPO NA1 . 4 . Yeast cells were spread on a concave microscopy slide filled with SD-agarose ( YNB + 2% sugar/ carbon source + 3% ( w/v ) agarose ) . Acquisition of CFP , mRFP and YFP was performed in 3D ( 21 focal planes at 0 . 2 μm distance intervals; 500 ms , 300 ms , 500 ms acquisition times respectively ) . Fluorescent intensities of the acquired spots were identical in both cell types demonstrating that the inserted operator arrays maintained the same size . The x , y and z coordinates for each focus were automatically measured using the “Spot distance”plug in on image J ( http://bigwww . epfl . ch/sage/soft/spotdistance/; D Sage , EPFL; [29] . This program uses multi-channel z-stack images to localize the center of the nucleus based on the background fluorescence of the CFP-lacI . The position of each focus is assigned to the center of gravity of the fluorescence around the brightest pixel found in this nucleus in a filtered version of the image . The signals are scored on 3D stacks using at least 200 nuclei , monitoring nuclear integrity and cell cycle stage through bud shape and nuclear diameter . Distances between pairs of loci can be determined in 3D using this Image J plugin . Statistical analysis was performed using the Wilcoxon test . Expression of the HO-endonuclease gene from the pHO ( URA selection[32] ) was induced by addition of 2% filtered galactose ( SIGMA ) to a yeast culture exponentially growing in 2% raffinose . For quantitative PCR reactions , Bio-Rad SybrGreen Supermix was used in the presence of 30ng of genomic DNA and 0 . 26μM of each primer . PCR were run on ABI 7900 . The MAT a-specific primers were 5’-GGCATTACTCCACTTCAAGT ( P1 ) and 5′-ATGTGAACCGCATGGGCAGT ( P2 ) . The MATα-specific primers were 5′-ATGTGAACCGCATGGGCAGT ( P2 ) and 5′- GCAGCACGGAATATGGGACT ( P3 ) . Primers specific for the ARG5 , 6 locus 5′- CAAGGATCCAGCAAAGTTGGGTGAAGTATGGTA and 5′- GAAGGATCCAAATTTGTCTAGTGTGGGAACG or for the actin locus were used for normalization . All qPCR assays were accompanied by reactions using dilutions of genomic DNA from wt strains’ 0h input sample to assess the linearity of the PCR signal and to create calibration curves . One considers a fiber with N nodes . Each state Ek of this fiber defined in a reference frame Rk is represented by N nodes: For all states Ek1 and Ek2 , Rk1≠Rk2 since k1≠k2 . On a chromosome represented as a polymer fiber , the center of gravity of each fluorescently labelled locus represents a node . N loci can be represented by ( xi , yi , zi ) i = 1…N . If we denote θi the angle around the node i . For each local analysis around node i , we make the following change of variables: xi-1 , yi-1 , zi-1 , xi , yi , zi , xi+1 , yi+1 , zi+1≻di1 , di2 , θi The main advantage of these new variables is their invariance during the reference frame change . To analyze the variations of positions around the node i , we introduce the normalized principal components analysis operator PCAnorm and consider a set of experimental data di1 , k , di2 , k , θiki=1 . . Nk=1 . . M acquired around all nodes i in all nuclei of state Ek . We denote by Ci and Pi the correlation matrix of the principal component analysis and the diagonal matrix of eigenvalues . For all nuclei , we can write the coordinates of red , green and blue loci in the referential linked to the microscope as xr , yr , zrRmicroscope , xg , yg , zgRmicroscope . Taking the red locus as the origin , we can write ( xr , yr , zr ) Rmicroscope≡ ( 0 , 0 , 0 ) Rmicroscopenuclei , ( xg , yg , zg ) Rmicroscope≡ ( xg−xr , yg−yr , zg−zr ) Rmicroscopenuclei , ( xb , yb , zb ) Rmicroscope≡ ( xg−xr , yg−yr , zg−zr ) Rmicroscopenuclei . Referential Rmicroscopenuclei is called pseudo-referential because its origin is inside the nucleus and its basis vectors are linked to the microscope . To overcome the orientation problem inherent to the nuclear sphericity , we can define new variables simple enough to analyze the position of the three loci as ( 1 and 1 ) ( S1 Fig ) . Let us take: d1=[ ( xg−xr ) 2+ ( yg−yr ) 2+ ( zg−zr ) 2]12 ( 4 ) d2=[ ( xb−xr ) 2+ ( yb−yr ) 2+ ( zb−zr ) 2]12 ( 5 ) and θ=arccos ( ( xg−xr , yg−yr , zg−zr ) ∘ ( xb−xr , yb−yr , zb−zr ) d1⋅d2 ) ( 6 ) where ∘ is the scalar product . With these new variables each nucleus is represented by three variables instead of nine: ( xr , yr , zr , xg , yg , zg , xb , yb , zb ) > ( d1 , d2 , θ ) We developed an original abstract model based on the assumption that each point of a fiber ( or polymer ) moves in a specific area ( Fig 3A ) . If we consider a dynamic polymer in the plane in which we can define N points ( xi , yi ) i = 1…N . For each point ( xi , yi ) , we define the survival zone as the smallest closed set Pi where ( xi , yi ) takes its values during temporal fluctuations . Any configuration Coj of our polymer is defined as: ∃ ( xkj , ykj ) k=1 . . . N∈Pk/Coj= ( x1j , y1j ) → ( x2j , y2j ) → . . . → ( xNj , yNj ) ( 15 ) 10 . 1371/journal . pcbi . 1004306 . g003 Fig 3 An abstract model to determine the relative physical constraint of three linked loci in wt and mutant strains . A ) The forward mathematical approach allows determining features of a subpopulation of relative positions between 3 loci in space . The inverse computational approach consists in modeling the relative positions or survival zones of loci whose positions are spatially linked . The underlying polymer fiber imposes constraints on the positions each locus can adapt . Iterative calculation defines the survival zones of each locus ( or node ) which can predict biological relevant features . B-C ) Survival Zones ( Z ) of HML , MAT and HMR . The initial position of the 3 loci is set based on the estimated conformation for Chr3 ( B ) . Iterations were run using Eq 25 ( see Methods ) and the statistically most significant zones are represented for wt , asf1 mutants and strains in which the recombination enhancer element was deleted ( C ) . Let us note ZV1 , ZV2 , …ZVN survival zones of ( x1 , y1 ) , ( x2 , y2 ) and ( xN , yN ) defined as ℝ2-closed set . We can define an abstract polymer from the knowledge of the N survival zones . Here , the abstract fiber will be close to the real fiber as soon as the survival zones will be small and the number of nodes is large enough . We can build several random configurations of our polymer knowing the survival zones . ( X1 , i , Y1 , i ) i=1 . . . K= rand ( ZV1 ) ( 19 ) ( X2 , i , Y2 , i ) i=1 . . . K= rand ( ZV2 ) ( 20 ) ( XN , i , YN , i ) i=1 . . . K= rand ( ZVN ) ( 21 ) where randZVi denotes the unform random function in the set ZVi We can construct an abstract configuration: Coia= ( X1 , i , Y1 , i ) → ( X2 , i , Y2 , i ) → . . . → ( XN , i , YN , i ) ( 22 ) For the given abstract configuration set , we can define as ( 1 and 2 ) new system variables: { ( Di , k1 , Di , k2 , Θi , k ) k=1 . . . M}1 . . . N Let us note Cia and Pia correlation matrix and diagonal eigenvalues matrix of the abstract system [Cia , Pia]=PCAnorm{ ( Di , k1 , Di , k2 , Θi , k ) }k=1 . . M' ( 23 ) Where M' denotes the number of abstract configurations and N the number of nodes . For a given set of points , the system of survival zones that best correlates with our experimental data has to be determined . Let us note MΔi=Ci−Cia ( 24 ) as the difference between the experimental correlation matrix and abstract correlation matrix . The optimal abstract model will be defined by: { ( x1 , y1 , ε11 , ε21 ) , ( x2 , y2 , ε12 , ε22 ) , . . . , ( xN , yN , ε1N , ε2N ) }=argminεji∈D∑i=1 . . . N∥MΔi∥frob ( 25 ) || . ||frob denotes the Frobenian norm of a square matrix . D is the base interval . Eq ( 25 ) is solved iteratively . The center of each survival zone is fixed by conservation of proportions between the three distances . On chromosome 3 , the center of HML positions is taken at ( 0 , 0 ) , the center of HMR is fixed at ( 5 , 0 ) . If dHML-MAT , dMAT-HMR and dHML-HMR are experimental mean distances , the lengths r0 and r1 are given by: 5dHML−HMR=r0dHML−MAT=r1dMAT−HMR ( 26 ) The center of the MAT zone is defined as the intersection of two circles . The circle with ( 0 , 0 ) center and r0 as radius . The second circle has ( 5 , 0 ) as a center and r1 as radius . Iterations are made using the variables ε11 , ε21 , ε12 , ε22 , ε13 and ε23 , in the range [0;5] . Our code has been parallelized on one hundred CPU cards using MPI ( Message Passing Interface ) . The step of subdivision for the iterative resolution is taken at 0 . 2 to yield 512 abstract configurations . Each experiment represents ~100 hours of calculation; computing of survival zones for all conditions tested took 1500 hours of calculation .
Combinations of the widely used Tet and Lac repressors , each fused to a fluorophore with a distinct emission spectrum , has proven to be a valuable tool for simultaneous visualization of two loci [18 , 19 , 30 , 35–37] . In order to simultaneously label three distinct loci to track their relative position , we adapted a third system based on the the bacterial λ repressor operator system ( λO and λCi ) [19] for use in yeast ( Fig 1A ) . Operator sequences were inserted by homologous recombination near the HML , MAT and HMR or near HML , LEU and MAT loci on Chr3 . Acquisition of fluorescent RFP , YFP and CFP proteins fused to the Lac , Tet and λ repressors , respectively , was performed in 3D . We introduce a normalized principal components analysis ( PCA ) operator to convert the automatically measured x , y and z coordinates for each focus into geometrical variables ( see Methods ) . The resulting coordinates of the formed triangle , here d1 ( side of the triangle delimited by HML- MAT ) , d2 ( MAT-HMR ) and d3 ( HMR-HML ) , were plotted on the same graph for all analyzed cells ( Fig 1B ) . The large variation in positional combinations of the three loci reflects the highly dynamic nature of chromosome folding in yeast [38] . The MAT locus is mobile [18 , 39 , 40] but the two silent mating type loci , despite their frequent juxtaposition , also change position relative to each other within 10–30 seconds [19] . 50% of the most frequent positions observed for the three loci were included in a volume whose 3D surface is colored in red for MATa and in green for MATα cells ( Fig 1B ) . A large fraction of the d1_d2_d3 combinations are correlated in both mating types ( intersection between the red and green iso-volumes ) . They represent the most probable conformations of Chr3 and are as such readily detected by other methods . Strikingly , subsets of relative triangular positions of the three mating type loci are specific to MATa or MATα cells . Our goal was to characterize the folding features leading to this variant part of the distribution and to correlate them with donor preference . The distribution of angles within the triangle formed by MAT , HML and HMR in MATa and MATα cells was statistically significant ( S1 and S2A Figs ) , again suggesting that certain conformations of Chr3 could be mating type specific . To extract folding features from the distribution of the three loci , we generated 2D projections of geometrical coordinates recorded for all nuclei . In the resulting density maps ( Fig 2 and S2–S7 Figs ) , data are grouped within 10% color-coded increments of occurrence . For each dataset , nine distinct maps are generated and compared to the equivalent maps of another dataset using PCA ( see Methods ) . Correlation coefficients ( c ) between duplicate experiments using the same strain were greater than 0 . 8 ( S2C Fig; four independent experiments 223<n<559 ) . As a control , density maps resulting from a simulation of data obtained using random positioning of three loci within a sphere of 2 μm diameter ( one example is shown in Fig 2A ) were significantly different from experimental data ( c<0 . 1 ) . Furthermore , the distribution of three independent loci ( MAT on Chr3 , the right telomere of Chr5 and ARS1412 on Chr14 ) did not correlate with the ones on Chr3 ( c <0 . 6 ( n = 405 ) ) . Thus , density maps obtained for the distribution of the three mating type loci on Chr3 are non-random . Strikingly , the maps obtained in a-cells were distinct from those in α-cells . This was surprising because the nuclear positions of individually labelled mating type loci were previously shown not to be statistically different in a- and α-cells[26] . Density maps differed at the 10–50% contour levels around HML and MAT ( S2C Fig ) . The angles formed at HML were significantly smaller in a fraction ( ~30% ) of α-cells compared to a-cells ( Fig 2B ) suggesting that , in α-cells , loci on the left arm of Chr3 are more confined with respect to those on the right arm . Also , in about 20% of cells , the angle at MAT was much smaller in a-cells than in α-cells ( 80°-140° ) for a similar distribution of HML-MAT distances . These data expose , for the first time , that positioning of HML relative to MAT and HMR differs between MATa and MATα cells . Geometric analysis of another combination of three loci provides additional detail ( Fig 2C and S3 Fig ) . We labeled two loci , HML and LEU2 , on the left arm and one , MAT , on the right . Density maps confirm that a portion of the left arm of Chr3 is largely compressed in MATa cells . For example , the angle at LEU2 formed with the vector pointing towards HML was , in the 30% most representative cells , significantly smaller in MATa than in MATα cells ( c = 0 . 27 ) . This suggests that HML and LEU2 roam in a similar volume with respect to MAT in MATa but not in MATα cells . Increased constraint of the LEU2 locus supports the view that the left arm , or at least a large region of it , can crumple and shorten in MATa cells , dynamically changing between an extended conformation and a transiently more compressed one . Our goal was to determine whether the linkage to a chromosome fiber contributes to the relative positions of three distinct loci . We developed an abstract polymer model to define physical constraint imposed by the fiber ( Eq ( 25 ) in Methods ) . Each nucleus can be taken as a state of the chromosome fiber . Hence , we have up to five hundred states of our system to describe our data . In an optimization approach , we assume a known configuration of Chr3 as the initial configuration ( Fig 3A ) to reduce the number of parameters to be estimated . The abstract model is a model based on the assumption that each point of a fiber ( or polymer ) moves uniformly in a specific area or survival zone ( Z ) . To solve Eq ( 25 ) we used an iterative method based on the variables ε11 , ε21 , ε12 , ε22 , ε13 and ε23 , in the range [0;5] . Our code has been parallelized on one hundred CPU cards . The step of subdivision for the iterative resolution is taken at 0 . 2 to yield 5 . 1012 abstract configurations . We can thus determine interactions between the survival zones ZMAT , ZHML and ZHMR as the extent by which two points roam in the same space while being under the physical constraint of the fiber . ZMAT and ZHMR partially overlap in a- cells ( Fig 3 ) in agreement with the fact that chromatin in yeast is highly flexible [38] , notably between these two loci 100kb apart on the same chromosome arm . In contrast , ZHML is excluded from the two other survival zones . In a-cells , ZHML is significantly greater than in α-cells consistent with our finding that the left arm is more crumpled and flexible in a subset of nuclei ( Fig 2 ) . In addition , we asked whether our abstract mathematical model could inform on physical properties of the chromatin fiber in yeast . First simulations ( S4 Fig ) suggest that the determined survival zones Z of the three mating type loci correspond to rather flexible , moderately constrained polymer fibers . For example , the survival zones of three linked loci corresponding to Z1 = [-1;1]×[-1;1] , Z2 = [-1;5]×[-1;5]and Z3 = [3 . 5;6 . 5]×[-1 . 5;1 . 5] are closer to simulation B than to A or C ( compare experimental data in Fig 3 to S4 Fig ) . The simulated survival zones Z2 and Z3 in our example are separated by <0 . 5 or less . If we assign a value of Z = 5 to the ~100kb contour length which corresponds to the separation between MAT ( Z2 ) and HMR ( Z3 ) the intervening fibers’s flexibility is <10kb . In future work , using different sets of multiple labels at varying distances along chromosomes , the abstract mathematical model presented in this study and polymer modeling will allow to better define chromatin fiber properties . We then tested whether our abstract model could be applied to predict functional consequences in mutant cells . We deleted the asf1 gene , coding for a histone chaperone . Asf1 was previously shown to regulate juxtaposition of the HM loci [19] . An increase in the random kinetics of the loci is expected due to general chromatin decompaction in the absence of Asf1[41] . All distances measured between the three loci on Chr3 in asf1 strains increased by nearly 20% ( S5 and S6 Figs; p<0 . 007 ) . Interestingly , the distribution of angles formed around each locus was the same in wt and asf1 mutant MATa but varied in MATα . This suggests that decompaction of chromatin in the absence of Asf1 leads to an overall extension of the chromatin fiber without compromising the folding properties of Chr3 in a-cells . In α-cells , however , density maps change to resemble those of wt MATa cells ( Fig 4 and S5 Fig ) . Computing the survival zones of HML relative to MAT and HMR in α-cells showed that HML is less constrained in asf1 mutants than in wild type ( Fig 3 ) . ZMAT and ZHMR partially overlap and the zone of HML expands , again , similar to the situation determined in wt a-cells . Thus , the extended , stiffer conformation of the left arm of Chr3 in α-cells seems to be dependent on chromatin structural features brought by Asf1 . The lesser impact of chromatin structure in a-cells suggests that the folding of the left arm of Chr3 in MATa is intrinsic and that in α-cells certain conformations are excluded due to chromatin structural properties . To test whether a greater contact probability between HML and the right arm of Chr3 would favor recombination , we determined donor preference in asf1 mutant cells . We found that in α-cells , usage of HML increased nearly four-fold in the absence of Asf1 . Hence , reduced physical constraint of the left arm of Chr3 in a subset of cells correlates with improved recombination competence . Finally , we asked whether the Recombination enhancer ( RE ) , a <1kb DNA element located 17 kb to the right of HMLα , shown to be required for recombinational competence of a large region ( 40 kb ) near the left end of Chr3 in MATa [22] , is involved in folding of Chr3 . Deletion of the RE region reduces the use of HML to repair MATa from >80% to <10%[22 , 23] . In MATα , the RE is in a heterochromatin configuration and non-functional , leaving the left arm incompetent for recombination[42] . We found that the distribution of the three loci was altered in both mating types in the absence of the RE , although the differences in density maps are more pronounced in α- than in a- cells ( Fig 4 , S6 and S7 Figs ) . In a-cells , only the most frequently observed wt conformations of the three loci remained . The relative positioning of HML and MAT varied and this was more noticeable in α-cells ( S7 and S8 Figs ) . To verify that this effect was due to the RE element rather than the insertion of the hygromycin resistance gene we deleted the RE region using the cre/lox method . The generated density maps of the relative positions of the three loci , although in a different manner , also show differences to the wt maps . Notwithstanding that we cannot formally rule out that replacing the RE with heterologous sequences ( the deletion of 803bp or the addition of 1078bp within the left arm , 29kb from the telomere ) might affect chromosome folding , RE specific sequences appear to mediate some of the detected cell type dependent differences . It was previously debated whether the RE may be involved in changing the localization or the higher-order organization of the entire left arm of Chr3 to make it more flexible in pairing with the recipient site in MATa cells [24 , 25] . Our data suggest that folding of the left arm is such that at a given moment it can be in the proximity of the MAT locus without being pre-folded or permanently in a recombination favorable position ( see model Fig 5 ) . Thus , in MATa cells , donor preference seems to only be imposed at commitment to recombination following cleavage of the MAT locus through recruitment of repair and recombination factors to RE elements or even synthesis of non-coding transcripts [23 , 43] . Strikingly , in α-cells , the repressed RE element appears , at least in part , to be responsible for the extended conformation of the left arm of Chr3 . Hence , the heterochromatin complex at the RE may sequester part of the chromosome , an organization that could counteract looping and may limit recombination aptitude of this chromosome arm . We present a new system for labelling and visualizing a specific chromosomal site by fluorescence microscopy in living cells . We show that a small sequence element can influence the folding of an entire chromosome . Our new methodology allows three individual chromosomal sites to be imaged simultaneously in living yeast . Quantitative data can be obtained because all cells are labeled identically and permanently . The computational strategy used to evaluate the relative distribution of three objects simultaneously in 3D represents a powerful tool for studying chromosome biology and is applicable to the analysis of any three simultaneously labelled sites in any cell type . It allows identifying transient and unstable conformations of chromosomes which are statistically not the most frequently detected ones , yet may be relevant for regulating DNA processes . This view is also supported by recent studies using polymer modeling of chromatin which revealed that fluctuations in transcriptional activity correlated with probabilistic organization of the Tsix gene domain [44] and with enhancer-promoter communication via modulatory chromatin looping[45] . Our method is highly complementary to genome-wide chromosome conformation capture approaches and necessary to validate models from simulations . It is also amenable to investigation of chromosomal rearrangements governing changes in DNA-related processes in higher eukaryotes . | The spatial organization of the genome inside eukaryotic cell nuclei has been shown to play a role in transcription , replication , recombination and DNA repair . Probabilistic models have correlated structural fluctuations with these processes , but methods to detect transient features describing chromosome conformation are lacking . We developed a new fluorescent repressor-operator system ( FROS ) based on the λ repressor and combined it with the existing lac and tet FROS to measure the relative spatial positioning between three labelled DNA loci on chromosome 3 in live yeast cells . To quantitatively analyze and interpret the data we applied an original computational method that relies on the geometrical distribution of the three tags . Our results show that the conformation of the small yeast chromosome 3 is mating type specific in G1 . Differential folding of the left arm of this chromosome can be attributed to a small DNA element which could explain why loci on this arm may be excluded from recombination with the MAT locus on the right arm of chromosome 3 . Chromatin structural properties altered in the absence of the Asf1 histone chaperone contribute to the lineage specific chromosome organization and the relative position of the three mating type loci . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] | [] | 2015 | Differential Chromosome Conformations as Hallmarks of Cellular Identity Revealed by Mathematical Polymer Modeling |
Mutation of the tumor suppressor Pten often leads to tumorigenesis in various organs including the uterus . We previously showed that Pten deletion in the mouse uterus using a Pgr-Cre driver ( Ptenf/fPgrCre/+ ) results in rapid development of endometrial carcinoma ( EMC ) with full penetration . We also reported that Pten deletion in the stroma and myometrium using Amhr2-Cre failed to initiate EMC . Since the Ptenf/fPgrCre/+ uterine epithelium was primarily affected by tumorigenesis despite its loss in both the epithelium and stroma , we wanted to know if Pten deletion in epithelia alone will induce tumorigenesis . We found that mice with uterine epithelial loss of Pten under a Ltf-iCre driver ( Ptenf/f/LtfCre/+ ) develop uterine complex atypical hyperplasia ( CAH ) , but rarely EMC even at 6 months of age . We observed that Ptenf/fPgrCre/+ uteri exhibit a unique population of cytokeratin 5 ( CK5 ) and transformation related protein 63 ( p63 ) -positive epithelial cells; these cells mark stratified epithelia and squamous differentiation . In contrast , Ptenf/fLtfCre/+ hyperplastic epithelia do not undergo stratification , but extensive epithelial cell apoptosis . This increased apoptosis is associated with elevation of TGFβ levels and activation of downstream effectors , SMAD2/3 in the uterine stroma . Our results suggest that stromal PTEN via TGFβ signaling restrains epithelial cell transformation from hyperplasia to carcinoma . In conclusion , this study , using tissue-specific deletion of Pten , highlights the epithelial-mesenchymal cross-talk in the genesis of endometrial carcinoma .
Endometrial carcinoma ( EMC ) is the most common cancer of the female reproductive organs in the United States . In 2017 , about 60 , 000 new cases were diagnosed and about 11 , 000 deaths occurred related to EMC in the US [1 , 2] . EMC has been categorized into two major types: type I endometrioid cancers are focused in the endometrial gland cells , and type II non-endometrioid cancers are often of serous morphology . Type I represents approximately 85% of EMCs in which Pten is commonly mutated . Other than endometrial cancer , Pten mutations are also evident in endometrial hyperplasia [3–5]; hyperplasia is a well-established precursor lesion of EMC [6] . The understanding of divergence between hyperplasia and cancer is of clinical significance . On one hand , faulty diagnosis of complex atypical hyperplasia ( CAH ) may lead to hysterectomy [7] , a non-reversible procedure that negatively impacts women seeking to preserve fertility . Alternatively , diagnosis at early stage of hyperplasia may prevent progression to carcinoma . Although stromal invasion and histological changes are considered diagnostic standards of EMC [8] , identification of the biomarkers for early stage carcinomas and the mechanism underlying cancer progression are greatly needed . Pten homozygous null mice are embryonic lethal . Therefore , Pten heterozygous mice are widely used for cancer studies [6] . Pten heterozygous females show atypical endometrial hyperplasia phenotype , with 20% developing cancer . Using the Cre-loxP system and Pgr-Cre driver , we previously showed that Ptenf/fPgrCre/+ mice with endometrial Pten deletion develop epithelial carcinoma as early as one month of age with Pten loss in major uterine cells [9] . To study roles of Pten in different uterine cell types , we created mice with Pten deletion specifically in the stroma and myometrium using Amhr2-Cre driver [10] . Ptenf/f/Amhr2Cre/+ females showed no EMC; instead myometrial cells transformed into adipocytes [11] . Taken together , these findings suggest epithelial origin of this pathology . We thought that the epithelial origin of EMC could be tested if only epithelial-specific loss of Pten is induced in the uterus , disrupting the cross-talk between the stroma and epithelium to initiate EMC and its progression . To address this issue , an efficient Cre mouse line is necessary to specifically delete epithelial genes . Pten was conditionally deleted in the epithelium using Wnt7a-Cre , but the mutant pups died around 10 days of age [11] . Conditional deletion of Pten using Sprr2f-Cre met with failure because of brain cancer and limited life span [11 , 12] . To generate a mouse line with Cre activity specifically in the adult uterine epithelium , we generated a mouse line expressing codon-improved Cre ( iCre ) under a Lactoferrin ( Ltf ) promoter . By crossing with LacZ reporter mice , we showed that the Ltf-driven iCre expression exhibits robust Cre activity in uterine luminal and glandular epithelia beginning at puberty [13] . In contrast to Cre expression driven by promoters of Pgr , Amhr2 , and Wnt7a that occur before or right after birth , Cre activity driven by Ltf promoter is activated with the beginning of estrous cycle [13] . In this study , using LtfCre/+ mice , we established the mouse model with uterine epithelial-specific Pten deletion by crossing with Ptenf/f mice . Surprisingly , Ptenf/fLtfCre/+ females rarely develop EMC , but show epithelial CAH . We also found that Ptenf/fLtfCre/+ females do not readily form stratified epithelial layers which are prevalent in Ptenf/fPgrCre/+ uteri . Ptenf/fPgrCre/+ epithelial layers show the presence of CK5 , a stratified epithelial cell marker , and CK8 that is primarily expressed in simple epithelial cells . CK5 positive cells are located between CK8 positive epithelia and stroma , and the two populations of CK5 and CK8-positive cells are mutually exclusive in Ptenf/fPgrCre/+ epithelia . p63 is critical for initiation of epithelial stratification [14] , and has been identified as a prognostic marker in multiple cancers [15 , 16] . Interestingly , p63 is expressed in CK5 positive cells in Ptenf/fPgrCre/+ epithelia . We further identified TGFβ , which represses stratification of epithelium [17] , is downregulated in the Ptenf/fPgrCre/+ stroma as compared to that in Ptenf/fLtfCre/+ mice . The differential phenotypes between Ptenf/fPgrCre/+ and Ptenf/fLtfCre/+ mice highlight the crucial role of stromal microenvironment and stromal-epithelial interactions in EMC progression .
In Ptenf/fLtfCre/+ mice , genomic deletion of Pten begins at 1 month of age ( S1A Fig ) . By 2 months of age , rare PTEN positive signals , if any , are observed in the uterine epithelium ( Fig 1A , S1B Fig ) . Notably , levels of PTEN expression in Ptenf/fLtfCre/+ stroma are upregulated as compared to those in Ptenf/f mice at 3 months of age . We examined whether epithelial deletion of Pten produces EMC . Pathological analysis shows that Ptenf/fLtfCre/+ uteri exhibit normal histology by 1 . 5 months old , but most of Ptenf/fLtfCre/+ uteri start developing CAH from 2 months of age . By 4 months , only 2 of 8 ( 25% ) mice show focal myometrial invasion ( Table 1 ) . Ptenf/fLtfCre/+ mice at 6 and 12 months of age also show atypical glandular hyperplastic epithelia showing medium to large cysts with fluid retention . This glandular hyperplasia perhaps predisposed to carcinoma , but no epithelial invasion to the myometrium was evident ( S2 Fig ) . Further analysis shows that PTEN expression patterns are comparable in Ptenf/fLtfCre/+ mice with or without myometrial invasion ( S3 Fig ) . Detailed pathological analyses at different ages is presented in Table 1 . The results show that progression of EMC is dramatically retarded in Ptenf/fLtfCre/+ mice as compared to that in Ptenf/fPgrCre/+ uteri [9] , suggesting that stromal Pten suppresses transformation of CAH to EMC . PTEN , a phosphoinositide 3-phosphatase , metabolizes phosphatidylinositol 3 , 4 , 5-trisphosphate ( PIP3 ) [18 , 19] , and suppresses AKT activation [20 , 21] . As expected , AKT activation markedly increases in Ptenf/fLtfCre/+ uterine epithelia at both 2 and 3 months of age ( S4A Fig ) . Previously , we reported that mTORC1 is a downstream target of PTEN/AKT signaling in Ptenf/fPgrCre/+ uteri [22] . In Ptenf/fLtfCre/+ mice , mTORC1 activation is upregulated in epithelia at both 2 and 3 months of age , as evident from elevated levels of phosphorylated ribosomal protein S6 ( pS6 ) , a downstream effector of mTORC1 ( S4B Fig ) . Heightened COX-2 expression and mTORC1 activity exacerbate EMC in Ptenf/fPgrCre/+ uteri [22] . In Ptenf/fLtfCre/+ uteri , COX-2 expression is induced in Ptenf/fLtfCre/+ epithelia ( S4C Fig ) . Western blotting results confirmed upregulated levels of p-AKT and pS6 in 2-month old Ptenf/fLtfCre/+ uteri ( S5 Fig ) . We compared the expression levels of p-AKT , pS6 , and COX-2 in uteri of Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ females . Levels of p-AKT and pS6 are higher in Ptenf/fLtfCre/+ uterine epithelia as compared to that in Ptenf/f uteri ( Fig 2A and 2B ) . We have previously shown that COX-2 expression is associated with endometrial cancer progression , and inhibition of COX-2 slows down cancer development and progression [22] . Scattered signals of COX-2 are observed in Ptenf/fLtfCre/+ epithelia , whereas COX-2 positive cells are widely distributed in Ptenf/fPgrCre/+ epithelia and underneath stroma ( Fig 2C ) . These results suggest that Ptenf/fLtfCre/+ epithelial cells are less invasive as compared to Ptenf/fPgrCre/+ epithelium . However , Ptenf/fPgrCre/+ epithelial cells do not appear to undergo epithelial mesenchymal transition ( EMT ) as evident from staining of EMT markers E-cadherin ( S6A Fig ) and Desmin ( S6B Fig ) [23 , 24] . Previous studies showed that p63 , a p53 homologue , is a marker of metaplastic differentiation , including basal/squamous differentiation and is found in stratified human tumors including EMC [25 , 26] . Thus , we explored the expression of p63 in Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ uteri . As shown in Fig 3A , p63 is localized primarily in the basal layer of luminal epithelia of Ptenf/fPgrCre/+ mice and surrounding glands at 3 months of age , while no p63 signal was observed in Ptenf/fLtfCre/+ uteri at the same age . Notably , p63 positive cells express E-cadherin ( S6C Fig ) , suggesting that these cells maintain epithelial characteristics . Trp63 encodes multiple isoforms of p63 , including full length TA isoforms with an acidic transactivation domain and ΔN isoforms lacking this domain [27] . Therefore , we used Western blotting analysis to assess the isoforms of p63 in uterine lysates of Ptenf/fPRcre/+ , Ptenf/fLtfcre/+ and respective littermate controls . The result shows that p63 in Ptenf/fPRcre/+ epithelia is of TA isoform ( S6D Fig ) . Cytokeratin can also be used to distinguish simple or stratified epithelium [28] . CK8 is produced by simple epithelia [29] , while CK5 is particularly expressed in the basal layer of stratified squamous epithelium . We found that the expression pattern of CK5 is similar to that of p63 ( Fig 3B ) . Interestingly , co-staining of p63 or CK5 with CK8 identified that the expression pattern of p63/CK5 and CK8 are mutually exclusive in Ptenf/fPgrCre/+ uteri ( Fig 3A and 3B ) . These results suggest a potential relationship between p63 and EMC . Since two Ptenf/fLtfCre/+ mice of 4 month old showed myometrial invasion , we examined the expression of p63 in these mice . Notably , p63 positive cells were observed in Ptenf/fLtfCre/+ mice with myometrial invasion ( Fig 3C ) . These results again suggest that expression of p63 correlates with EMC . To study the correlation between p63 and carcinoma progression , the expression of p63 in Ptenf/fPgrCre/+ uteri was examined in uteri of 1 and 2-month old Ptenf/fPgrCre/+ mice . Ptenf/fPgrCre/+ uteri at 1 month of age are negative for p63 signal , but p63-positive cells appear underneath the CK8 positive luminal epithelium at 2 months of age ( Fig 3D ) . These results provide evidence that stromal PTEN restrains epithelial stratification , and p63 serves as an indicator of EMC . We explored the underlying mechanism preventing epithelial carcinoma by stromal PTEN . Since Ki67 positive cells are present at the leading edge of the tumor in Ptenf/fPgrCre/+ uteri [9] , we examined the distribution of Ki67-positive cells in Ptenf/fLtfCre/+ uteri . As shown in Fig 4A , strong signals for Ki67 are present in both Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ uterine epithelium . Remarkably , Ki67 staining is more intense in the CK8-negative luminal epithelium . These results were corroborated by co-staining of CK8 , p63 , and Ki67 staining on the consecutive sections ( Fig 4C ) . Ki67 signals are localized in p63-positive epithelia . The staining of phosphor-Histone H3 ( pHH3 ) in Ptenf/fPgrCre/+ uteri also showed similar expression pattern to that of Ki67 ( Fig 4B and 4C ) . The results show that CK8 positive epithelial cells are proliferative in Ptenf/fLtfCre/+ uteri , whereas epithelial cells in the p63-positive layer show cell proliferation in Ptenf/fPgrCre/+ uteri . To better understand the turnover of epithelial cells in Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ uteri , we examined cell apoptosis by cleaved-Caspase 3 ( caspase-3 ) immunostaining and observed increased cell population of caspase-3 positive cells in Ptenf/fLtfCre/+ epithelia; the signal is limited in Ptenf/fPgrCre/+ epithelia ( Fig 4D ) . These results indicate that the PTEN-positive stroma in Ptenf/fLtfCre/+ uteri restricts the epithelial hyperplasia by promoting apoptosis in hyperplastic epithelia , while Pten deletion in the stroma in Ptenf/fPgrCre/+ uteri fails to prevent excessive proliferation and transform hyperplastic epithelial cells to EMC . Notably , Ptenf/fAmhr2cre/+ uteri with Pten deletion in the stroma show apparently normal proliferation and apoptosis in epithelia ( S7 Fig ) , suggesting stromal PTEN has limited impact on epithelial growth under normal physiological conditions . Uterine cell proliferation and differentiation is regulated by ovarian hormones through ESR1 [30] and PR [31] . We examined the expression of these two nuclear receptors . The results show that the expression of ESR1 and PR is maintained in all major cell types in both Ptenf/fPRcre/+ and Ptenf/fLtfcre/+ uteri ( S8 Fig ) . Given extensive apoptosis in Ptenf/fLtfCre/+ uteri , we then asked if immune cells play a role in apoptotic cell clearance in Ptenf/fLtfCre/+ uteri . First , we accessed the distribution of CD45-positive cells of hematopoietic origin . Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ uteri show increased population of immune cells in the uterus ( Fig 5A ) ; the weight of spleen , liver and thymus did not show many changes ( S9 Fig ) . The uterine recruitment of immune cells suggests local inflammation . As previously reported that neutrophils are recruited in Ptenf/fPgrCre/+ uteri [32] , the population of Ly6G-positive cells , a marker of neutrophils , is much higher in Ptenf/fPgrCre/+ uteri ( Fig 5B ) . Interestingly , increased CD45-positive cells in Ptenf/fLtfCre/+ are not neutrophils but macrophages , as shown by F4/80 staining ( Fig 5C ) . The infiltration of different immune cells could be due to extensive apoptotic or metaplastic cells in Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ uteri respectively . Two types of macrophages ( M1 and M2 ) exhibit diverse phenotypes and functions . We examined the expression of MHCII and CD206 , markers for M1 and M2 macrophages , respectively , to determine which subtypes contribute to increased macrophage population in Ptenf/fLtfCre/+uteri . The results show that the number of MHCII-positive cells is much higher in Ptenf/fLtfCre/+ uteri than that in Ptenf/fPgrCre/+ ( Fig 5D ) . However , no significant differences in CD206-positive M2 macrophages are observed ( Fig 5E ) . The quantification of M1 versus M2 macrophages is shown in Fig 5F and 5G . Furthermore , F4/80-positive signals do not co-localize with Ki67 or caspase-3 signals ( S10A and S10B Fig ) , suggesting that resident macrophages in the uterus do not proliferate but migrate from the circulation . These results suggest a potential role of macrophages in clearing apoptotic epithelial cells . TGFβ signaling has been reported to have a dual function during the progression of carcinoma: cell-cycle arrest and apoptosis in the early-stage cancer and tumorigenesis at the late stage [33] . TGFβ signaling also inhibits epithelial stratification [17] . Using immunofluorescence , we observed distinct TGFβ signals in the stroma of Ptenf/fLtfCre/+ uteri , whereas the signal is much lower in Ptenf/fPgrCre/+ stroma , especially in the stroma surrounding the luminal epithelium ( Fig 6A ) . The distribution of phosphorylated SMAD2/3 ( p-SMAD2/3 ) , a downstream effector , correlates with TGFβ signaling [34] . Consistent with TGFβ staining , the activation of p-SMAD2/3 is significantly lower in Ptenf/fPgrCre/+ uteri ( Fig 6B ) . These results suggest that stromal Pten potentially exerts its tumor suppressive role by upregulating TGFβ signaling . To study if our findings have any relevance to human uterine corpus endometrial carcinoma ( UCEC ) , we compared the RNA profile from patients with UCEC and controls that are available in RNA-seq dataset from The Cancer Genome Atlas ( TCGA ) . Our analysis shows that the UCEC group has significantly lower levels of PTEN and TGFβ RNA , as well as TGFβ’s target genes , SERPINE1 and ID1 , as compared to those in control tissues ( Fig 6C ) . This is consistent with low TGFβ levels in Ptenf/fPgrCre/+ uteri . ( Fig 6C ) .
PTEN is considered as a tumor suppressor protein . Pten mutations are closely related to various types of tumorigenesis , especially type I EMC [35] . Our present and previous studies using cell specific deletion of Pten in the uterus provide evidence that absence of Pten in the epithelium , stroma and myometrium promptly produces EMC , while its deletion in the stroma and myometrium fail to generate EMC but transforms myometrial cells to adipocytes . Surprisingly , Pten deletion specifically in the epithelium primarily shows CAH . The function of epithelial Pten has been studied using several approaches . Adenovirus was used to delete endometrial epithelial Pten by intraluminal injection [36] , although a small percentage of endometrial stromal cells of adeno-Cre injected mice showed Pten deletion . Conditional Pten deletion using Wnt7a-Cre and Ksp-Cre in combination with Pik3ca mutation was also reported [37] . Combination of Pten deletion and Pik3ca mutation leads to carcinoma , while Pik3ca mutation alone showed no EMC or hyperplasia phenotype . Similar to the CAH phenotype in the uterus , Pten deletion leading to hyperplasia has been corroborated in several other different epithelial tissues besides the endometrial epithelium , such as urothelial cells , keratinocytes , prostatic epithelial cells , and lung epithelium [6] . Furthermore , the glandular epithelium specific Pten deletion also showed endometrial hyperplasia [38] . As Pten is deleted in both luminal and glandular epithelia in our Ltf-iCre model , definitive answers to distinguish the role of Pten in the luminal or glandular epithelium will require a luminal epithelium-specific deletion mouse model . It is also of interest to evaluate the uterine phenotype in stroma and glandular-deletion or stroma and luminal-deletion of Pten using a combination of Cre systems . Our study with Ptenf/fLtfCre/+ uteri suggests that stromal Pten restrains transition of hyperplasia to carcinoma . In contrast , the deficiency of this gene in three major uterine cell types ( Ptenf/fPgrCre/+ ) with rapid generation of EMC suggests that Pten-deleted stroma provides a more susceptible microenvironment for further deterioration of hyperplastic epithelium into EMC . In this regard , the role of endometrial stroma in EMC was reported using a stromal-specific Lkb1-deleted mouse model in which the loss of Lkb1 in the stroma was sufficient to initiate neoplasia [39] . A previous study also showed that EMC develops in uteri with epithelial modification in both Pten and Pik3ca [37] . In the mouse uterus , epithelial deletion of Pten alone is not sufficient to induce EMC . In human cancer specimens , PTEN is predominantly lost in the epithelium and maintained in the stroma [40] . EMC was also observed in a transplant model , in which a mixture of Pten deficient epithelial cells and WT stromal cells were transplanted under kidney capsule [40] . In spite of these findings , many questions still remain about the differences between human and mouse models of cancers . The higher levels of pS6 signal in Ptenf/fLtfCre/+ epithelia as compared with that in Ptenf/fPgrCre/+ mice at 3 months of age suggest that activation of mTORC1 is closely associated with hyperplasia . Our previous study using mice with whole uterine deletion of Tsc1 also supports this conclusion [41] . Interestingly , mice with Tsc1 deletion in the stroma and myometrium also shows hyperplasia , suggesting the existence of unidentified paracrine signals from stromal influencing epithelial proliferation . Furthermore , our results with rapamycin ( an inhibitor of mTORC1 signaling ) suggest that inhibition of mTORC1 signaling could be an effective preventive strategy to combat endometrial hyperplasia and/or EMC . We have also shown that inhibition of upregulated COX-2 in the uterus of Ptenf/fPgrCre/+ mice is reduced by a COX-2 inhibitor ( Celecoxib ) with attenuated EMC development [22] . In the present study , COX-2 is also induced in hyperplasic Ptenf/fLtfCre/+ epithelia . By comparing the expression of COX-2 in Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ uteri , we found that the COX-2 level is much higher in Ptenf/fPgrCre/+ uteri , suggesting hyperplasic cells are less invasive than cancerous cells . The current study demonstrates that the expression of p63 is closely associated with EMC development . However , the role of p63 in uterine luminal epithelial stratification is still not clear . p63 plays multiple roles in development depending on different contexts [42] . p63 is required for establishing stratified epithelia perhaps by maintaining stem cell populations or triggering differentiation of simple epithelia into stratified epithelia [43 , 44] . In humans , p63 is expressed in hyperplastic and metaplastic endometria [25] . It is possible that p63 suppresses epithelial metaplasia and prevents epithelia from further invading into the muscle layer , since the loss of p63 in tumor tissues is associated with more aggressive EMC [26] . p63-positive cells invade the area underneath p63-negative columnar cells and push them upward , which leads to the detachment of p63-negative cells [45] . However , we cannot rule out the possibility that p63 itself promotes EMC development . TGFβ signaling appears to constrain hyperplastic Ptenf/fLtfCre/+ epithelia from stratification toward tumorigenesis . TGFβ acts as a tumor suppressor in the epithelium [46] and restricts epithelial growth and early tumor development [47] . In mouse uteri , TGFβr1 mRNA is detected mainly in the epithelia of Ptenf/fPgrCre/+ uteri . SMAD2 is highly expressed in uterine epithelium at the proestrus phase . These results suggest a role for TGFβ in epithelial proliferation [48] . Pten and TGFβr1 double knockout mice using Pgr-Cre driver show severe endometrial lesions with disrupted myometrial layers and pulmonary metastasis [49] , suggesting a role for TGFβr1 in cancer progression . Mice with uterine stromal TGFβr1-deletion using Amhr2-Cre show enhanced proliferation in both luminal and glandular epithelia [50] , suggesting TGFβ signaling is involved in epithelial-stromal interactions . In this study , we observed PTEN levels are upregulated in the stroma of Ptenf/fLtfCre/+ mice ( Fig 1A ) and is associated with heightened stromal TGFβ and pSMAD2/3 levels . In contrast , TGFβ levels are suppressed in Ptenf/fPgrCre/+ stroma , indicating stromal TGFβ signaling may play a role in preventing epithelial tumorigenesis . Taken together , these data indicate that Pten expression in the stroma maintains stromal TGFβ expression , which perhaps limits epithelial growth . TGFβ signaling plays a role in cell proliferation and apoptosis . In mouse uteri , reduced TGFβ signaling leads to loss of growth-inhibitory response , and constitutively activated TGFβr1 reduces glandular growth [51] , suggesting an inhibitory role for TGFβ in epithelial proliferation . TGFβ signaling also plays a role in cell apoptosis . In polarized endometrial epithelial cells , TGFβ induces apoptosis via SMAD3 [52] . Pten knockdown blocks TGFβ-induced apoptosis and leads to increased cell proliferation . We observed intense cell apoptosis in Ptenf/fLtfCre/+ mice compared to Ptenf/fPgrCre/+ uteri , in which cell apoptosis is rarely seen . Interestingly , epithelial cell proliferation is not affected by changes in TGFβ signaling as evident by comparable numbers of Ki67 positive cells in Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ . Given the role of TGFβ in apoptosis , the decreased levels of TGFβ and apoptosis in Ptenf/fPgrCre/+ uteri suggest stromal PTEN-driven TGFβ prevents epithelial tumorigenesis by promoting epithelial cell apoptosis . Pten deletion often leads to cancer in situ [53] . However , PTEN’s role in providing a microenvironment conducive to cancer progression is not clear . By comparing the phenotype of Ptenf/fLtfCre/+ and Ptenf/fPgrCre/+ mouse uteri , we show here that EMC development requires Pten deletion in both the stroma and epithelium . Our data also imply that stromal regulation of epithelial growth is mediated by TGFβ signaling . Our current study presents new insights into the role of Pten in the microenvironment for tumorigenesis .
All mice were housed in the Cincinnati Children’s Hospital Medical Center Animal Care Facility in conformity with NIH and institutional guidelines . PtenloxP/loxP mice ( stock number 004597 , 129S4/SvJae/BALB/cAnNTac ) were obtained from the Jackson Laboratory ( Sacramento , CA , USA ) . Ptenf/fPgrCre/+ ( 129S4/SvJae/BALB/cAnNTac/C57BL/6 ) mice and Ltf-iCre mice ( 129/C57BL/6/albino B6 ) were generated as previously described [9 , 13] . Ptenf/fLtfCre/+ were generated by crossing PtenloxP/loxP and Ltf-iCre mice . Littermate floxed mice were used as controls in all experiments . Uterine tissues from the diestrous stage were collected for experiments . For paraffin sections , tissues were fixed in Safefix ( Thermo Fisher Scientific , Lafayette , CO , USA ) and embedded in paraffin . After deparaffinization and hydration , sections ( 6 μm ) were subjected to antigen retrieval by autoclaving in 0 . 01M sodium citrate solution ( pH = 6 ) for 10 min . For frozen tissues , sections ( 12 μm ) were fixed in 4% paraformaldehyde solution . Depending on the primary antibody ( S1 Table ) , some sections were subjected to antigen retrieval by autoclaving in 0 . 01M sodium citrate solution ( pH = 6 ) for 10 min . COX-2 and TGFβ antibodies were custom-made as previously described [54 , 55] . For immunohistochemistry , Histostain-Plus kit ( Invitrogen , Carlsbad , CA , USA ) was used to visualize signals . Immunofluorescence was performed using secondary antibodies conjugated with Alexa 488 or Alexa 594 ( Jackson ImmunoResearch , West Grove , PA , USA ) . Hematoxylin and Hoechst were used for counterstain in immunohistochemistry and immunofluorescence , respectively . For all images of pHH3 staining at lower magnification , the maximum filter of ImageJ was applied to the red staining channel for clear visibility . Numbers of M1 and M2 macrophages were calculated by counting MHCII and CD206 positive cells according to immunofluorescence staining . Sections from 3 different mice , and 4 fields per section have been evaluated . cRNA probes for Pten were generated by reverse RT-PCR followed by 35S-labeling using Sp6 or T7 RNA polymerases . Paraformaldehyde-fixed frozen sections ( 12 μm ) were hybridized with 35S-labeled cRNA probes of Pten as previously described [9] . Western blotting was performed as previously described [11] . Briefly , uterine protein samples from uteri at the diestrous stage were run on 10 or 12% SDS-PAGE gels depending on the molecular weights of proteins and transferred onto PVDF membranes . After blocking in 5% BSA for detection of phosphorylated protein , or in 10% non-fat milk for detection other proteins , membranes were blotted with antibodies to PTEN , p-AKT , AKT , pS6 , S6 , p63 and β-ACTIN . Signals were detected using ECL reagents ( GE healthcare , Pittsburgh , PA , USA ) . RNAseq data were downloaded from TCGA data portal ( https://tcga-data . nci . nih . gov/ ) . RNAseq data from 176 UCEC cases and 23 controls were used for data analysis . Transcript-levels of genes were calculated using RNA-Seq by Expectation Maximization ( RSEM ) method . Data were analyzed by the Mann Whitney tests . P<0 . 05 was considered significant . Values are mean ± SEM . | Endometrial cancer is highly prevalent gynecological cancer in the United States . Pten is the most commonly mutated gene in endometrial carcinoma originating in the epithelium . Previous studies focused on PTEN’s role in epithelial growth regulation . Here we show that in addition to Pten mutation in the epithelium , its mutation in the stromal compartment is critical for the initiation and progression of endometrial carcinoma . We present evidence that while loss of Pten function in both uterine epithelia and stroma results in rapid development of endometrial carcinoma , its loss in epithelial cells leads to endometrial hyperplasia , but not carcinoma . Our findings highlight the critical role of stromal PTEN in the transformation of hyperplasia to carcinoma and stromal TGFβ appears to play a role in preventing this transformation . This study reveals a previously unidentified role of PTEN in influencing the microenvironment in the uterus for the initiation and generation of endometrial carcinoma . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"blood",
"cells",
"cell",
"death",
"uterus",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"immune",
"cells",
"cell",
"processes",
"immunology",
"epithelial",
"cells",
"animal",
"models",
"model",
"organisms",
"experimental",
"organism",
"systems",
"research",
"and",
"analysis",
"methods",
"white",
"blood",
"cells",
"animal",
"cells",
"biological",
"tissue",
"mouse",
"models",
"signal",
"transduction",
"myometrium",
"anatomy",
"cell",
"biology",
"apoptosis",
"tgf-beta",
"signaling",
"cascade",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"macrophages",
"cell",
"signaling",
"signaling",
"cascades"
] | 2018 | The uterine epithelial loss of Pten is inefficient to induce endometrial cancer with intact stromal Pten |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.